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 has been corrected and refined to include linkage to other geospatial references such as GB1900 and OpenStreetMap, and this version is available as GeoJSON in the Linked Places Format.

The dataset can be viewed both on an interactive map and in reconstituted tabular form through the GitHub repository here.

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

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.

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/

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

The Monmouth Rebellion of 1685 prompted the government in London to undertake a survey the following year to establish the number of guest beds and quantity of stabling available across England and Wales for billeting soldiers. This dataset represents an attempt to identify and geolocate all of the place-names noted in that survey.

Transcription was undertaken for CAMPOP by Jacob Field, with funding provided by Leigh Shaw-Taylor and Dan Bogart. Stephen Gadd is responsible for place-name identification and geolocation, matching place-names as far as possible to the Index Villaris, 1680 dataset, GB1900 labels, and OpenStreetMap nodes.

PLEASE NOTE: THIS PRE-RELEASE DOES NOT CONTAIN ANY DATA

Digital data from VG07-5 Springston, G. and De Simone, D., 2007,�Surficial geologic map of the town of Williston, Vermont: Vermont Geological Survey Open-File Report VG07-5, 1 color plate, scale 1:24,000.� Data may include surficial geologic contacts, isopach contours lines, bedrock outcrop polygons, bedrock geologic contacts, hydrogeologic units and more. The surficial geologic materials data at a scale of 1:24,000 depict types of unconsolidated surficial and glacial materials overlying bedrock in Vermont. Data is created by mapping on the ground using standard geologic pace and compass techniques and/or GPS on a USGS 1:24000 topographic base map. The materials data is selected from the Vermont Geological Survey Open File Report (OFR) publication (https://dec.vermont.gov/geological-survey/publication-gis/ofr). The OFR contains more complete descriptions of map units, cross-sections, isopach maps and other information that may not be included in this digital data set.

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

No abstract provided

Georeferenced map of 'Old and New Town of Edinburgh and Leith with the proposed docks' By John Ainslie (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.

This dataset is not the "Planning Register" as described in The Town and Country Planning (Development Management Procedure) (England) Order 2010; which is currently provided via Public Access https://planning.bradford.gov.uk/online-applications/

This dataset contains a current set of Planning Application boundaries held since 1974. The Planning Service is constantly adding and amending boundaries as it discovers missing boundaries and updates incorrect boundaries.

This dataset will be updated every 24hrs.

This dataset represents the complete descriptive metadata for John Wood's town plan maps, a digitised collection of town plan maps of Scottish towns by mapmaker John Wood (1780-1747).

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.

This archive contains aerial photography of UK boroughs (towns/cities) and counties. The scale of the photographs depends to some extent on the borough/county, but is predominantly 1:5000 for towns/cities and 1:10000 for complete counties.

These vertical aerial photographs are taken with a large camera mounted in the floor of an aeroplane flying in a series of pre-planned flight lines. The images overlap by 60% along the flight line to allow for stereoscopic (3D) viewing. There is a 25% overlap between flight lines.

In addition to their prime application in photogrammetric mapping (from updating and contouring existing maps to preparing large scale engineering plans), air photos are used for environmental studies, general planning, land use and land capability, soils, pollution, forestry, mining and quarrying, housing and leisure development, agriculture, geology, water, transport and civil engineering, boundary disputes, public enquiries, etc.

The data is stored mainly as colour photographic negatives and can be supplied as both digital and photographic products (positive or negative). To find out what imagery is available for a specific area, a cover search can be performed free of charge. Price lists and further information about cover searches are available, on request, from the National Remote Sensing Centre (NRSC).

Note: All photography is flown to RICS Specification for Aerial Photography Issue III, see references.

As of 2023, the population density in London was by far the highest number of people per square km in the UK, at 5,690. Of the other regions and countries which constitute the United Kingdom, North West England was the next most densely populated area at 533 people per square kilometer. Scotland, by contrast, is the most sparsely populated country or region in the United Kingdom, with only 70 people per square kilometer. UK population over 67 million According to the official mid-year population estimate, the population of the United Kingdom was just almost 67.6 million in 2022. Most of the population lived in England, where an estimated 57.1 million people resided, followed by Scotland at 5.44 million, Wales at 3.13 million and finally Northern Ireland at just over 1.9 million. Within England, the South East was the region with the highest population at almost 9.38 million, followed by the London region at around 8.8 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 494 billion British pounds, almost a quarter of UK GDP overall. In terms of GDP per capita, Londoners had a GDP per head of 56,431 pounds, compared with an average of 33,224 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 33.2 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.

OSNI Street Maps showing detailed information, including road names and one-way systems, railway lines, car parking, public buildings, churches and schools, for Northern Ireland’s cities and towns. Dataset derived from OSNI large and smallscale data.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

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)

Hastings Local Plan 2004 planning constraints including Protected Green and Open Spaces, Industrial Land Allocation, Employment Land Allocation, Housing Land Allocation and Shopping Areas. This data is a digitised version of the Hastings Local Plan 2004 Policies Map (Polygon Data) so far as it has not been supersceded by the Hastings Local Plan Planning Strategy 2011-2028. The data is a snapshot of the constraints when they were created in 2004 and things have changed since then. It includes Green Constraints that have national and internation designations including Ancient Woodland, Sites of Special Scientific Interest, SAC and AONB and these are no longer correct and should not be relied upon, this data should be obtained direct from Natural England. Upon accessing this Licenced Data you will be deemed to have accepted the terms of the Public Sector End User Licence - INSPIRE (http://www.ordnancesurvey.co.uk/business-and-government/public-sector/mapping-agreements/inspire-licence.html)

London had the highest unemployment rate among regions of the United Kingdom in the first quarter of 2025 at ****percent, while for the UK as a whole, the unemployment rate was ****percent. Three other regions also had an unemployment rate higher than the national average, while Northern Ireland had the lowest unemployment rate in this time period, at ****percent. Labor market recovery after COVID-19 After reaching historically low levels of unemployment in 2019, there was a noticeable spike in the UK unemployment rate in the aftermath of the COVID-19 pandemic. After peaking at ****percent in late 2020, the unemployment rate declined throughout 2021 and 2022. High levels of job vacancies, resignations, and staff shortages in 2022, were all indicative of a very tight labor market that year, but all these measures have started to point in the direction of a slightly looser labor market. UK's regional economic divide While the North of England has some of the country’s largest cities, the sheer size and economic power of London is much larger than the UK's other urban agglomerations. Partly, due to the size of London, the United Kingdom is one of Europe’s most centralized counties, and there is a clear divide between the economic prospects of north and south England. In 2022, for example, the gross domestic product per head in London was ****** British pounds, far higher than the UK average of *******pounds, and significantly larger than North East England, the region with the lowest GDP per head at *******pounds.