A collection of 1:250 000 scale geophysical maps in the Universal Transverse Mercator (UTM) projection, covering the United Kingdom and continental shelf areas between 1975 – 1990. Mapping is divided into squares which cover 1 degree by 1 degree of latitude / longitude. A geophysical map is a graphical representation of data collected through various geophysical methods to investigate the subsurface characteristics of the Earth. Geophysics is the study of the physical properties and processes of the Earth using measurements of physical quantities such as gravity, magnetic fields, seismic waves, electrical resistivity, and others. The collection includes aeromagnetic anomaly maps (1975 – 1990), Bouguer gravity anomaly maps (1975 – 1989) and a small number of free air anomaly maps (1981 – 1989). These maps are hard-copy paper records stored in the National Geoscience Data Centre (NGDC) and are delivered as digital scans through the BGS website.

The UKSeaMap Predictive Habitats Map 2025 (version 1) is a broad-scale prediction that uses physical models of depth, light, sediment and energy to predict the physical seabed habitats for the whole UK seabed. This map covers the UK extended Continental Shelf as defined by the Continental Shelf (Designation of Areas) Order 2013, but excluding the intertidal zone, Dee Estuary and Morecambe Bay.

Two habitat classification systems are present in the final output:

EUNIS habitat classification system version 2007-11 The Marine Habitat Classification for Britain and Ireland (https://mhc.jncc.gov.uk/) version 22.04 The attribute table includes a column for each of level 2, level 3 and level 4 of each of these two classification schemes. In some cases, there were 2-3 options of habitat type, which were both included and separated by the word “OR“. There is also a column containing the most detailed unique habitat type for each of the two classification systems.

The habitats were determined by combining 4 categorical input layers called 'habitat descriptors', which are the basis for describing physical habitats in the Marine Habitat Classification for Britain and Ireland. These are also present in the geodatabase.

Habitat descriptor data layers:

Seabed substrate type - created using the British Geological Survey's national broad-scale predictive sediment map - Marchant et al. (2025) and the JNCC-BGS-Cefas national broad-scale predictive rock map (JNCC, 2019) Biological zone (also known as biozone) - created using the depth to seabed, wave disturbance at the seabed and amount of light reaching the seabed. Kinetic energy at the seabed - created using energy from tidal currents and energy from waves Salinity regime - created using the Annex I Habitats Regulations datasets for coastal lagoons and estuaries features A methods report will be published in due course.

The UKSeaMap Predictive Map forms part of the UK Atlas of Seabed Habitats (UKASH), a suite of mapping products, offering the most complete characterisation of seabed habitats in the UK in the Marine Habitat Classification for Britain and Ireland and the European standard classification system, EUNIS. UKASH is composed of:

UKASH Library of Localised Maps: A standardised collection of individual, ground-truthed habitat maps from various sources. UKASH Mosaic of Localised Maps: A unified, non-overlapping map product that prioritises the most reliable maps from the UKASH Library of Localised Maps. UKSeaMap Predictive Map: A seamless, full-coverage predictive map of physical seabed habitats in the UK. UKASH Combined Map: The UKASH Mosaic of Localised Maps, with gaps filled by the UKSeaMap Predictive Map.

Further info: https://jncc.gov.uk/our-work/uk-atlas-of-seabed-habitats-ukash/#ukseamap

It is still unknown which factors of physical activity behaviour (PAB) may be effective and how they may influence PAB in UK children. The objective of the current study was to generate a conceptual analysis of the factors of PAB in UK children (5-12 years) using the input of researchers in the field of physical activity (PA experts; PAE) and researchers in other fields (non-PA experts; non-PAE). The concept mapping approach was used to identify potential (new) factors of PAB in children, assess their importance based on rating of potential modifiability and effect, and generate a concept map depicting the associations between them. In the first (brainstorming) stage (n=32 experts) yielded 93 factors, including 14 (new) not identified in previous reviews. In the second (rating and sorting) stage (n=26 experts), 32 factors were rated as important and four-cluster concept map was generated including themes related to Society/community, Home/social setting, Personal/social setting, and Psychological/emotional factors. Two additional concept maps were generated for PAE and non-PAE. From expert opinion, we identified new factors of PAB that warrant further research and we highlight the need to consider the interaction between intrapersonal and external factors when designing interventions to promote PA in UK children.The data has been downloaded from Ariadne (minds21.org) and includes the raw data and the analysed data (clustering and rating data). Participant information has been removed from the data files and replaced with participant numbers.

This dataset comprises 2 collections of maps. The facsmile collection contains all the marginalia information from the original map as well as the map itself, while the georectified collection contains just the map with an associated index for locating them. Each collection comprises approximately 101 000 monochrome images at 6-inch (1:10560) scale. Each image is supplied in .tiff format with appropriate ArcView and MapInfo world files, and shows the topography for all areas of England, Wales and Scotland as either quarter or, in some cases, full sheets. The images will cover the approximate epochs 1880's, 1900's, 1910's, 1920's and 1930's, but note that coverage is not countrywide for each epoch. The data was purchased by BGS from Sitescope, who obtained it from three sources - Royal Geographical Society, Trinity College Dublin and the Ordnance Survey. The data is for internal use by BGS staff on projects, and is available via a customised application created for the network GDI enabling users to search for and load the maps of their choice. The dataset will have many uses across all the geoscientific disciplines across which BGS operates, and should be viewed as a valuable addition to the BGS archive. There has been a considerable amount of work done during 2005, 2006 and 2007 to improve the accuracy of the OS Historic Map Collection. All maps should now be located to +- 50m or better. This is the best that can be achieved cost effectively. There are a number of reasons why the maps are inaccurate. Firstly, the original maps are paper and many are over 100 years old. They have not been stored in perfect condition. The paper has become distorted to varying degrees over time. The maps were therefore not accurate before scanning. Secondly, different generations of maps will have used different surveying methods and different spatial referencing systems. The same geographical object will not necessarily be in the same spatial location on subsequent editions. Thirdly, we are discussing maps, not plans. There will be cartographic generalisations which will affect the spatial representation and location of geographic objects. Finally, the georectification was not done in BGS but by the company from whom we purchased the maps. The company no longer exists. We do not know the methodology used for georectification.

Supplementary information files for article: 'The future scope of large-scale solar in the UK: site suitability and target analysis'.Abstract:This paper uses site suitability analysis to identify locations for solar farms in the UK to help meet climate change targets. A set of maps, each representing a given suitability criterion, is created with geographical information systems (GIS) software. These are combined to give a Boolean map of areas which are appropriate for large-scale solar farm installation. Several scenarios are investigated by varying the criteria, which include geographical (land use) factors, solar energy resource and electrical distribution network constraints. Some are dictated by the physical and technical requirements of large-scale solar construction, and some by government or distribution network operator (DNO) policy. It is found that any suitability map which does not heed planning permission and grid constraints will overstate potential solar farm area by up to 97%. This research finds sufficient suitable land to meet Future Energy Scenarios (UK National Grid outlines for the coming energy landscape).

Land Cover Map 2021 (LCM2021) is a suite of geospatial land cover datasets (raster and polygon) describing the UK land surface in 2021. These were produced at the UK Centre for Ecology & Hydrology by classifying satellite images from 2021. Land cover maps describe the physical material on the surface of the country. For example grassland, woodland, rivers & lakes or man-made structures such as roads and buildingsThis is a 10 m Classified Pixel dataset, classified to create a single mosaic of national cover. Provenance and quality:UKCEH’s automated land cover classification algorithms generated the 10m classified pixels. Training data were automatically selected from stable land covers over the interval of 2017 to 2019. A Random Forest classifier used these to classify four composite images representing per season median surface reflectance. Seasonal images were integrated with context layers (e.g., height, aspect, slope, coastal proximity, urban proximity and so forth) to reduce confusion among classes with similar spectra.Land cover was validated by organising the pixel classification into a land parcel framework (the LCM2021 Classified Land Parcels product). The classified land parcels were compared to known land cover producing confusion matrix to determine overall and per class accuracy.View full metadata information and download the data at catalogue.ceh.ac.uk

This dataset contains summary data regarding historical (1930s-40s) land use and land-use change between 1930s and 2007 according to broad land-use categories. Data provided are summary values at the 10-km grid square 'hectad' level of the British National Grid, specifying the proportion and proportion of change in broad land-use categories.

Historical data are based on the first Land Utilisation Survey of Great Britain (Stamp 1931). For England and Wales, digitisation of the historical maps contains information supplied by Natural England, based on methods developed by Baily et al. (2011). For Scotland, map images were digitised using the R package HistMapR (Auffret et al. 2017). Both methods involve processing and classifying images based on the colour of the historical land-use map categories. Classified maps were then resampled to the 25m resolution of the modern UK Land Cover Map 2007 (Morton et al. 2011), and both historical and modern land-use categories were adjusted to produce broad categories of equivalent land use: Arable, Grassland, Urban, Woodland, Agriculturally-Improved Grassland and Surface Water. In Scotland, surface water from a modern map is used for the historical time period due to issues in classifying this category. Pixels within a 75m buffer of the modern road network were removed due to the disproportionate size of roads shown in the historical maps, and pixels falling into some coastal land-use categories in the modern maps were removed due to a lack of equivalent in the historical maps. The proportions of remaining pixels within each hectad, and the change in the proportion over time was then calculated. Full details of data creation and processing can be found in Suggitt et al. (2023), and more information on the data files can be found in the readme.

The extent of the data files: GB_LandUseChange_Data.csv - table containing summary data, 2802 rows and 15 columns GB_LandUseChange_LowlandGrasslandChange.csv - table containing data on lowland grassland change, 2802 rows and 10 columns

The file GB_LandUseChange_Raster.tif is a GeoTIFF file primarily intended to be used with the R script. It can also be opened using other GIS software.

If R is installed with required packages (see sessionInfo.txt), the file Rplots.pdf can be generated running: Rscript GB_LandUseChange_Code.R

References:

Auffret, A.G., Kimberley, A., Plue, J., Skånes, H., Jakobsson, S., Waldén, E., Wennbom, M., Wood, H., Bullock, J.M., Cousins, S.A.O., Gartz, M., Hooftman, D.A.P., Tränk, L., 2017, HistMapR: Rapid digitization of historical land-use maps in R, Methods in Ecology and Evolution 8: 1453-1457. https://doi.org/10.1111/2041-210X.12788

Baily, B., Riley, M., Aucott, P. & Southall, H., 2011, Extracting digital data from the First Land Utilisation Survey of Great Britain – Methods, issues and potential, Applied Geography 31: 959-968. https://doi.org/10.1016/j.apgeog.2010.12.007

Morton, D., Rowland, C., Wood, C., Meek, L., Marston, C., Smith, G., Wadsworth, R., Simpson, I.C., 2011, Final Report for LCM2007 – the new UK Land Cover Map, Centre for Ecology & Hydrology, Wallingford, UK. http://nora.nerc.ac.uk/id/eprint/14854

Stamp, D.L., 1931, The Land Utilisation Survey of Britain. Geographical Journal 78: 40-47. https://doi.org/10.2307/1784994

Suggitt, A.J., Wheatley, C.J., Aucott, P., Beale, C.M., Fox, R., Hill, J.K., Isaac, N.J.B., Martay, B., Southall, H., Thomas, C.D., Walker, K.J., Auffret, A.G., 2023, Linking climate warming and land conversion to species’ range changes across Great Britain, Nature Communications, https://doi.org/10.1038/s41467-023-42475-0

This is the land parcels (polygon) dataset for the UKCEH Land Cover Map of 2018(LCM2018) representing Northern Ireland. It describes Northern Ireland's land cover in 2018 using UKCEH Land Cover Classes, which are based on UK Biodiversity Action Plan broad habitats. This dataset was derived from the corresponding LCM2018 20m classified pixels dataset. All further LCM2018 datasets for Northern Ireland are derived from this land parcel product. A range of land parcel attributes are provided. These include the dominant UKCEH Land Cover Class given as an integer value, and a range of per-parcel pixel statistics to help to assess classification confidence and accuracy; for a full explanation please refer to the dataset documentation. LCM2018 represents a suite of geospatial land cover datasets (raster and polygon) describing the UK land surface in 2018. These were produced at the UK Centre for Ecology & Hydrology by classifying satellite images from 2018. LCM2018 was simultaneously released with LCM2017 and LCM2019. These are the latest in a series of UKCEH land cover maps, which began with the 1990 Land Cover Map of Great Britain (now usually referred to as LCM1990) followed by UK-wide land cover maps LCM2000, LCM2007 and LCM2015. This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability. Full details about this dataset can be found at https://doi.org/10.5285/35f15502-d340-4ab5-a586-abd42f238b6e

This dataset contains gridded human population with a spatial resolution of 1 km x 1 km for the UK based on Census 2021 (Census 2022 for Scotland) and Land Cover Map 2021 input data. Data on population distribution for the United Kingdom is available from statistical offices in England, Wales, Northern Ireland and Scotland and provided to the public e.g. via the Office for National Statistics (ONS). Population data is typically provided in tabular form or, based on a range of different geographical units, in file types for geographical information systems (GIS), for instance as ESRI Shapefiles. The geographical units reflect administrative boundaries at different levels of detail, from Devolved Administration to Output Areas (OA), wards or intermediate geographies. While the presentation of data on the level of these geographical units is useful for statistical purposes, accounting for spatial variability for instance of environmental determinants of public health requires a more spatially homogeneous population distribution. For this purpose, the dataset presented here combines 2021/2022 UK Census population data on Output Area level with Land Cover Map 2021 land-use classes 'urban' and 'suburban' to create a consistent and comprehensive gridded population data product at 1 km x 1 km spatial resolution. The mapping product is based on British National Grid (OSGB36 datum).

This layer of the GeoIndex shows the location of available 1:50000 scale digital geological maps within Great Britain. The Digital Geological Map of Great Britain project (DiGMapGB) has prepared 1:625 000, 1:250 000 and 1:50 000 scale datasets for England, Wales and Scotland. The datasets themselves are available as vector data in a variety of formats in which they are structured into themes primarily for use in geographical information systems (GIS) where they can be integrated with other types of spatial data for analysis and problem solving in many earth-science-related issues. Most of the 1:50 000 scale geological maps for England & Wales and for Scotland are now available digitally as part of the DiGMapGB-50 dataset. It integrates geological information from a variety of sources. These include recent digital maps, older 'paper only' maps, and desk compilations for sheets with no published maps.

UKSeaMap 2018V2 is a broad-scale physical habitat map for UK waters. It is a by-product of the 2013-2016 activities of the EMODnet Seabed Habitats 2013–2016 consortium. This dataset contains two products: a roughly 100 m* resolution broad-scale predictive seabed habitat map in geodatabase format, and a set of confidence maps in GeoTIFF format. The data has been clipped to cover the current extent of the UK continental shelf.

*3 arc second = 93 m latitudinally by between 44 m (north) and 53 m (south) longitudinally

Classification system:

• EUNIS habitat classification system, with additional deep sea zones
• MSFD Benthic Broad Habitat Types
• The Marine Habitat Classification for Britain and Ireland (https://mhc.jncc.gov.uk/)

Input data layers:

• Seabed substrate type
• Depth to the seabed
• Amount of light reaching the seabed
• Wave disturbance at the seabed
• Kinetic energy at the seabed caused by tidal currents and waves

Linear features (shown as polylines) represent six classes of geological structural features e.g. faults, folds or landforms e.g. buried channels, glacial drainage channels at the ground or bedrock surface (beneath superficial deposits). Linear features are associated most closely with the bedrock theme either as an intrinsic part of it for example marine bands or affecting it in the case of faults. However landform elements are associated with both bedrock and superficial deposits. The linear features are organised into seven main categories: Alteration area, indicating a zone of change to the pre-existing rocks due to the application of heat and pressure that can occur round structural features such as faults and dykes. Fault, where a body of bedrock has been fractured and displaced by a large scale process affecting the earth's crust. Fold, where strata are bent or deformed resulting from changes or movement of the earth's surface creating heat and pressure to reshape and transform the original horizontal strata. Folds appear on all scales, in all rock types and from a variety of causes. Fossil horizons, where prolific fossil assemblages occur and can be used to help establish the order in which deposits were laid down (stratigraphy). These horizons allow correlation where sediments of the same age look completely different due to variations in depositional environment. Landforms, define the landscape by its surface form; these include glacial features such as drumlins, eskers and ice margins. Mineral vein, where concentrations of crystallised mineral occur within a rock, they are closely associated with faulting but may occur independently. Rock, identifies key (marker) beds, recognised as showing distinct physical characteristics or fossil content. Examples include coal seams, gypsum beds and marine bands. The data are available in vector format (containing the geometry of each feature linked to a database record describing their attributes) as ESRI shapefiles and are available under BGS data licence.

This layer of the GeoIndex shows the location of available 1:10000 scale digital geological maps within Great Britain. The Digital Geological Map of Great Britain project (DiGMapGB) has prepared 1:625 000, 1:250 000 and 1:50 000 scale datasets for England, Wales and Scotland. The datasets themselves are available as vector data in a variety of formats in which they are structured into themes primarily for use in geographical information systems (GIS) where they can be integrated with other types of spatial data for analysis and problem solving in many earth-science-related issues. The DiGMapGB-10 dataset is as yet incomplete, current work is concentrated on extending the geographical cover, especially to cover high priority urban areas.

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 record is for Approval for Access product AfA439. A habitat map derived from airborne data, specifically CASI (Compact Airborne Spectrographic Imager) and LIDAR (Light Detection and Ranging) data.

The habitat map is a polygon shapefile showing site relevant habitat classes. Geographical coverage is incomplete because of limits in data available. It includes those areas where the Environment Agency, Natural England and the Regional Coastal Monitoring Programme have carried out sufficient aerial and ground surveys in England.

The habitat map is derived from CASI multispectral data, LIDAR elevation data and other GIS products. The classification uses ground data from sites collected near to the time of CASI capture. We use ground data to identify the characteristics of the different habitats in the CASI and LIDAR data. These characteristics are then used to classify the remaining areas into one of the different habitats.

Habitat maps generated by Geomatics are often derived using multiple data sources (e.g. CASI, LIDAR and OS-base mapping data), which may or may not have been captured coincidentally. In instances where datasets are not coincidentally captured there may be some errors brought about by seasonal, developmental or anthropological change in the habitat.

The collection of ground data used in the classification has some limitations. It has not been collected at the same time as CASI or LIDAR capture; it is normally within a couple of months of CASI capture. Some variations between the CASI data and situation on site at the time of ground data collection are possible. A good spatial coverage of ground data around the site is recommended, although not always practically achievable. For a class to be mapped on site there must have been samples collected for it on site. If the class is not seen on site or samples are not collected for a class, it cannot be mapped.

No quantitative accuracy assessment has been carried out on the habitat map, although the classification was trained using ground data and the final habitat map has been critically evaluated using Aerial Photography captured simultaneously with the CASI data by the processors and independently by habitat specialists. Please note that this content contains Ordnance Survey data © Crown copyright and database right [2014] and you must ensure that a similar attribution statement is contained in any sub-licences of the Information that you grant, together with a requirement that any further sub-licences do the same.

This is a digital map containing polygons representing areas of vegetation within Roudsea Wood National Nature Reserve (NNR), Cumbria. Vegetation was mapped in the field on a basemap as parcels according to tree cover type, tree stocking rates and ground flora communities. The map covers the western side of the reserve (the woodland). The field map was originally created by staff at the Nature Conservancy’s Merlewood Research Station, Grange-over-Sands, Cumbria in 1962 and digitized by the Centre for Ecology & Hydrology from the original field map in 2019. Full details about this dataset can be found at https://doi.org/10.5285/a8d710fb-177d-467c-b2c1-2b215f582d2c

Linear features (shown as polylines) represent six classes of geological structural features e.g. faults, folds or landforms e.g. buried channels, glacial drainage channels at the ground or bedrock surface (beneath superficial deposits). Linear features are associated most closely with the bedrock theme either as an intrinsic part of it for example marine bands or affecting it in the case of faults. However landform elements are associated with both bedrock and superficial deposits. The linear features are organised into seven main categories: Alteration area indicating zones of change to the pre-existing rocks due to the application of heat and pressure that can occur round structural features such as faults and dykes. Fault where a body of bedrock has been fractured and displaced by a large scale process affecting the earth's crust. Fold where strata are bent or deformed resulting from changes or movement of the earth's surface creating heat and pressure to reshape and transform the original horizontal strata. Folds appear on all scales, in all rock types and from a variety of causes. Fossil horizons where prolific fossil assemblages occur and can be used to help establish the order in which deposits were laid down (stratigraphy). These horizons allow correlation where sediments of the same age look completely different due to variations in depositional environment. Mineral vein where concentrations of crystallised mineral occur within a rock, they are closely associated with faulting, but may occur independently. Landforms define the landscape by its surface form; these include glacial features such as drumlins, eskers and ice margins. Rock identifies key (marker) beds, recognised as showing distinct physical characteristics or fossil content. Examples include coal seams, gypsum beds and marine bands. The data are available in vector format (containing the geometry of each feature linked to a database record describing their attributes) as ESRI shapefiles and are available under BGS data licence.

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📈 Business Intelligence & Strategic Planning • Build dashboards powered by Google Maps data, including business counts, category distributions, and review activity • Overlay business presence with population, workforce, or customer base for location planning • Benchmark performance across cities, regions, or market verticals • Track mobility and change by comparing past and current Google Maps metadata

💼 DEI, ESG & Ownership Profiling • Identify minority-owned, women-owned, or other diversity-flagged companies using Google Data ownership attributes • Build datasets aligned with supplier diversity mandates or ESG investment strategies • Segment location insi...

This service is a representation of the Land Classification of Great Britain. The Land Classification is a classification of sets of environmental strata (land classes) to be used as a basis for ecological survey. The classification was originally developed by the Institute of Terrestrial Ecology (ITE) in the late 1970s. The strata were created from the multivariate analysis of 75 environmental variables, including climatic data, topographic data, human geographical features and geology data. The Land Classification has provided a stratification for successive ecological surveys (the Countryside Survey of Great Britain), the results of which have characterised the classes in terms of botanical, zoological and landscape features. Additionally, the Land Classification can be used to stratify a wide range of ecological and biogeographical surveys to improve the efficiency of collection, analysis and presentation of information derived from a sample. There are three layers in this WMS (1) the 1990 version of the Land Classification which contains 32 classes - classifying all 240,000km squares in Great Britain (2) the 1998 version in which the Land Classification was adjusted to 40 classes as a consequence of the need to provide National Estimates for habitats in Scotland in addition to GB (3) the 2007 version in which the Land Classification was adjusted once again, to 45 classes, as a consequence of the need to provide Wales-only estimates in addition to those for Scotland and GB.

2014 Coastal Land Use Data. Digital survey of aerial imagery and desktop mapping software. Carried out by the University of Leicester. Project details: https://www.nationaltrust.org.uk/documents/mapping-our-shores-fifty-years-of-land-use-change-at-the-coast.pdf