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The Great Britain Historical Database has been assembled as part of the ongoing Great Britain Historical GIS Project. The project aims to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain at sub-county scales. Further information about the project is available on A Vision of Britain webpages, where users can browse the database's documentation system online.

These data were originally collected by the Censuses of Population for England and Wales, and for Scotland. They were computerised by the Great Britain Historical GIS Project and its collaborators. They form part of the Great Britain Historical Database, which contains a wide range of geographically-located statistics, selected to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain, generally at sub-county scales.

The first census report to tabulate social class was 1951, but this collection also includes a table from the Registrar-General's 1931 Decennial Supplement which drew on census occupational data to tabulate social class by region. In 1961 and 1971 the census used a more detailed classification of Socio-Economic Groups, from which the five Social Classes are a simplification.

This is a new edition. Data from the Census of Scotland have been added for 1951, 1961 and 1971. Wherever possible, ID numbers have been added for counties and districts which match those used in the digital boundary data created by the GBH GIS, greatly simplifying mapping.

A lookup file between unitary authorities and Department for Children Education Lifelong Learning and Skills areas in Wales as at 31 December 2019. (File Size - 16 KB)Field Names - UA19CD, UA19NM, DCELL19CD, DCELL19NM, FIDField Types - Text, Text, Text, TextField Lengths - 9, 17, 9, 20FID = 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/UA19_DCELL19_WA_LU_29846bf2cfc84775b448cb8626239081/FeatureServer

Results of a survey to guage the amount of litter located in an area of the Sychnant Pass in North Wales, close to paths popular with walkers and other casual visitors. It was intended to show the types of litter as an indicator of behaviour and littering-motivation. Coordinates are WGS-84. All litter was cleared away and disposed of, including recycling where relevant. Survey carried out by Andrew Thomas Sunday 4th May 2014. These data were used for creation of the fusion-table heat-map at http://www.snowdonia-society.org.uk/news.php?n_id=530

This dataset is wholly owned by the Royal Yachting Association (RYA). The Royal Yachting Association (RYA) UK Coastal Atlas of Recreational Boating is a GIS dataset of recreational boating activity around the UK, comprising spatial data including indicators of intensity of use, general boating areas, offshore routes, as well as the locations of clubs, training centres and marinas

A lookup file between unitary authorities and Department for Children Education Lifelong Learning and Skills areas in Wales as at 31 December 2017. (File Size - 16 KB)Field Names - UA17CD, UA17NM, DCELL17CD, DCELL17NMField Types - Text, Text, Text, TextField Lengths - 9, 17, 9, 20REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/UA17_DCELL17_WA_LU_61b27629c43a49f8827d5757bee7d403/FeatureServer

Classifying trees from point cloud data is useful in applications such as high-quality 3D basemap creation, urban planning, and forestry workflows. Trees have a complex geometrical structure that is hard to capture using traditional means. Deep learning models are highly capable of learning these complex structures and giving superior results.Using the modelFollow the guide to use the model. The model can be used with the 3D Basemaps solution and ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning frameworks libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.InputThe model accepts unclassified point clouds with the attributes: X, Y, Z, and Number of Returns.Note: This model is trained to work on unclassified point clouds that are in a projected coordinate system, where the units of X, Y, and Z are based on the metric system of measurement. If the dataset is in degrees or feet, it needs to be re-projected accordingly. The provided deep learning model was trained using a training dataset with the full set of points. Therefore, it is important to make the full set of points available to the neural network while predicting - allowing it to better discriminate points of 'class of interest' versus background points. It is recommended to use 'selective/target classification' and 'class preservation' functionalities during prediction to have better control over the classification.This model was trained on airborne lidar datasets and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For such cases, this pre-trained model may be fine-tuned to save on cost, time and compute resources while improving accuracy. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block, and extra attributes should match those of the data originally used for training this model (see Training data section below).OutputThe model will classify the point cloud into the following 2 classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: 0 Background 5 Trees / High-vegetationApplicable geographiesThis model is expected to work well in all regions globally, with an exception of mountainous regions. However, results can vary for datasets that are statistically dissimilar to training data.Model architectureThis model uses the PointCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. Class Precision Recall F1-score Trees / High-vegetation (5) 0.975374 0.965929 0.970628Training dataThis model is trained on a subset of UK Environment Agency's open dataset. The training data used has the following characteristics: X, Y and Z linear unit meter Z range -19.29 m to 314.23 m Number of Returns 1 to 5 Intensity 1 to 4092 Point spacing 0.6 ± 0.3 Scan angle -23 to +23 Maximum points per block 8192 Extra attributes Number of Returns Class structure [0, 5]Sample resultsHere are a few results from the model.

The map allows you to pick any location of interest and quickly and simply create an elevation profile.Accurate elevation data from inside ArcGIS Online is used to produce an info-graphic for any area.Use as a front of class tool to explore with students, or as a resource for their own independent investigations.

This web map service (WMS) is the 25m raster version of the Land Cover Map 2015 (LCM2015) for Great Britain and Northern Ireland. It shows the target habitat class with the highest percentage cover in each 25m x 25m pixel. The 21 target classes are based on the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompass the entire range of UK habitats.The 25m raster web map service is the most detailed of the LCM2015 raster products, both thematically and spatially, and it is derived from the LCM2015 vector product. For LCM2015 per-pixel classifications were conducted, using a random forest classification algorithm. The resultant classifications were then mosaicked together, with the best classifications taking priority. This produced a per-pixel classification of the UK, which was then 'imported' into the spatial framework, recording a number of attributes, including the majority class per polygon which is the Land Cover class for each polygon.Find out more about Land Cover Map 2015 at ceh.ac.uk.LCM2015 is available for download to Catchment Based Approach (CaBA) Partnerships in the desktop GIS data package. Please contact your CaBA catchment host for further information.

This is a web map service (WMS) for the 10-metre Land Cover Map 2023. The map presents the and surface classified into 21 UKCEH land cover classes, based upon Biodiversity Action Plan broad habitats.UKCEH’s automated land cover algorithms classify 10 m pixels across the whole of UK. Training data were automatically selected from stable land covers over the interval of 2020 to 2022. 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 10 m pixel classification into a land parcel framework (the LCM2023 classified land parcels product). The classified land parcels were compared to known land cover producing a confusion matrix to determine overall and per class accuracy.

Audience: PublicExtent: UKUpdate Frequency: Every SundaySustrans is the custodian of the National Cycle Network (NCN). We work with our many partners and stakeholders to develop the Network across the UK. This view layer depicts more than 12,000 miles of signed paths and routes for walking, cycling, wheeling and exploring outdoors.While we receive updates from regional staff and volunteers on a continuous basis, we can't guarantee the data to be free of error. If you discover an error please inform us by emailing our Sustrans GIS team so that it can be corrected. About SustransSustrans is the charity making it easier for people to walk and cycle.Join us on our journey, Sustrans Website Layer VisibilityThis layer contains over 35,000 polyline features. To optimise drawing performance the view layer has been limited to Cities (1:160000) level. Known IssuesThe NCN view layer's native projection is British National Grid (EPSG: 27700). The Network in Northern Ireland was captured in WGS 84 and reprojected to British National Grid. This is essential for maintaining a complete dataset and for producing overall statistics about the network. For this reason, the public version is projected in WGS 84 / Pseudo-Mercator (EPSG: 3857). Attribute Information1. DescriptionTrafficFree: Cycle route closed to public motor vehicles such as a footway, cycle path or bridleway.Onroad : Cycle route open to and used by public motor vehicles2. Route type

NCN (National Cycle Network): Cycle route is signed by a number in a red box. RCN (Regional Cycle Network – network not maintained/updated by Sustrans): Cycle route is signed by a number in a blue boxLink (connects to NCN, but not part of a route): Cycle route is signed by a number enclosed in brackets (blue or red box).PROM: promoted routes, not part of the NCN, but links to NCN and forms part of national or regional routes e.g. John O’Groats to Lands’ End3. Route category (RouteCat)

Main routeAlternative routeTemporary diversion (where a route has been closed for works etc and an temporary alternative route has been designed)5. Quality

Smooth: Top quality asphalt, newly laid path, motorway standard. Standard: Average quality asphalt.Acceptable: Rough British country road or good quality unsealed surface.Rough: Would not normally be ridden on a road bike.MTB Only: A road bike definitely would not be a sensible vehicle for using this section (whether or not it is theoretically possible to cycle on this with enough skill).6. LightingFullLit: Route link is fully lit, no dark patches.PartLit: Route is part lit, a few dark patches.NotLit: Route is not lit.Additional information on surface type is available on request. Please email GISSupport@sustrans.org.uk if you require this.Access the data on our open data portal here.

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

Ordnance Survey ® OpenMap - Local Buildings are polygon features that represent a built entity that includes a roof. This is a generalized building and could be made up of an amalgamation of other buildings and structures.Ordnance Survey ® OpenMap - Local Important Buildings are polygon features that represent buildings that fall within the extent of a functional site across England, Wales and Scotland. Important Buildings are classified into a number of building themes such as:Attraction and Leisure - A feature that provides non-sporting leisure activities for the public. Includes Tourist Attractions.Air Transport - This theme includes all sites associated with movement of passengers and goods by air, or where aircraft take off and land. Includes Airport, Helicopter Station, Heliport.Cultural Facility - A feature that is deemed to be of particular interest to society. Includes Museum, Library, Art Gallery.Education facility - This theme includes a very broad group of sites with a common high level primary function of providing education (either state funded or by fees). Includes: Primary Education, Secondary Education, Higher or University Education, Further Education, Non State Secondary Education, Non State Primary Education, Special Needs Education.Emergency Services - Emergency services are organizations which ensure public safety and health by addressing different emergencies. Includes: Fire Station, Police Station.Medical Facility - This theme includes sites which focus on the provision of secondary medical care services. Includes: Medical Care Accommodation, Hospital, Hospice.Religious Building - A place where members of a religious group congregate for worship. Includes: Places of Worship (churches etc.)Retail - A feature that sells to the general public finished goods. Includes: Post OfficeRoad Transport - This theme includes: Bus Stations, Coach Stations, Road user services.Sports and Leisure Facility - A feature where many different sports can be played. Includes: Sports and Leisure CentreWater Transport - This theme includes sites involved in the transfer of passengers and or goods onto vessels for transport across water. Includes: Port consisting of Docks and Nautical Berthing, Vehicular Ferry Terminal, Passenger Ferry Terminal.With OS OpenMap - Local Buildings and Important Buildings you can:Understand your area in detail, including the location of key sites such as schools and hospitals.Share high-quality maps of development proposals to help interested parties to understand their extent and impact.Analyse data in relation to important public buildings, roads, railways, lines and more.Use in conjunction with other layers such as Functional Sites – an area or extent which represents a certain type of function or activity.Present accurate information consistently with other available open data products.The currency of the data is 04/2025