PLEASE NOTE: This data product is not available in Shapefile format or KML at https://naturalengland-defra.opendata.arcgis.com/datasets/Defra::living-england-habitat-map-phase-4/about, as the data exceeds the limits of these formats. Please select an alternative download format.This data product is also available for download in multiple formats via the Defra Data Services Platform at https://environment.data.gov.uk/explore/4aa716ce-f6af-454c-8ba2-833ebc1bde96?download=true.The Living England project, led by Natural England, is a multi-year programme delivering a satellite-derived national habitat layer in support of the Environmental Land Management (ELM) System and the Natural Capital and Ecosystem Assessment (NCEA) Pilot. The project uses a machine learning approach to image classification, developed under the Defra Living Maps project (SD1705 – Kilcoyne et al., 2017). The method first clusters homogeneous areas of habitat into segments, then assigns each segment to a defined list of habitat classes using Random Forest (a machine learning algorithm). The habitat probability map displays modelled likely broad habitat classifications, trained on field surveys and earth observation data from 2021 as well as historic data layers. This map is an output from Phase IV of the Living England project, with future work in Phase V (2022-23) intending to standardise the methodology and Phase VI (2023-24) to implement the agreed standardised methods.The Living England habitat probability map will provide high-accuracy, spatially consistent data for a range of Defra policy delivery needs (e.g. 25YEP indicators and Environment Bill target reporting Natural capital accounting, Nature Strategy, ELM) as well as external users. As a probability map, it allows the extrapolation of data to areas that we do not have data. These data will also support better local and national decision making, policy development and evaluation, especially in areas where other forms of evidence are unavailable. Process Description: A number of data layers are used to inform the model to provide a habitat probability map of England. The main sources layers are Sentinel-2 and Sentinel-1 satellite data from the ESA Copericus programme. Additional datasets were incorporated into the model (as detailed below) to aid the segmentation and classification of specific habitat classes. Datasets used:Agri-Environment Higher Level Stewardship (HLS) Monitoring, British Geological Survey Bedrock Mapping 1:50k, Coastal Dune Geomatics Mapping Ground Truthing, Crop Map of England (RPA), Dark Peak Bog State Survey, Desktop Validation and Manual Points, EA Integrated Height Model 10m, EA Saltmarsh Zonation and Extent, Field Unit NEFU, Living England Collector App NEFU/EES, Long Term Monitoring Network (LTMN), Lowland Heathland Survey, National Forest Inventory (NFI), National Grassland Survey, National Plant Monitoring Scheme, NEFU Surveys, Northumberland Border Mires, OS Vector Map District , Priority Habitats Inventory (PHI) B Button, European Space Agency (ESA) Sentinel-1 and Sentinel-2 , Space2 Eye Lens: Ainsdale NNR, Space2 Eye Lens: State of the Bog Bowland Survey, Space2 Eye Lens: State of the Bog Dark Peak Condition Survey, Space2 Eye Lens: State of the Bog (MMU) Mountain Hare Habitat Survey Dark Peak, Uplands Inventory, West Pennines Designation NVC Survey, Wetland Inventories, WorldClim - Global Climate DataFull metadata can be viewed on data.gov.uk.

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

This dataset is part of the Geographical repository maintained by Opendatasoft. This dataset contains data for countries in the United Kingdom.Processors and tools are using this data.EnhancementsAdd ISO 3166-3 codes.Simplify geometries to provide better performance across the services.

As part of the ESA Land Cover Climate Change Initiative (CCI) project a set of Global Land Cover Maps have been produced. These are available at 300m spatial resolution for three epochs centred on the year 2010 (2008-2012), 2005 (2003-2007) and 2000 (1998-2002), where each epoch covers a 5-year period.

Each pixel value corresponds to the label of a land cover class defined using UN-LCCS classifiers. For each epoch, the land cover map is delivered along with 4 quality flags which document the reliability of the classification. These are described further in the Product User Guides.

Further Land Cover CCI products, user tools and a product viewer are available at: http://maps.elie.ucl.ac.be/CCI/viewer/index.php

This data set contains Global maps of five ecosystem services using 6 different among-model ensemble approaches: the provisioning services of water supply, biomass for fuelwood and forage production, the regulating service Carbon Storage for CO2 retention and the cultural non-material service Recreation. For water, the data comes as one shapefile with polygons per watershed, each polygon containing seven ensemble estimates. The other services – recreation, carbon storage, biomass for fuelwood and forage production – come as seven tiff- maps at a 1-km2 resolution with associated world files for each tiff-map contains 43,200 x 18,600 pixels for one ensemble approach, with LZW compressed file sizes between 400MB and 950MB. For all maps, 600dpi jpg depictions are added to the supporting information with uniform colour scaling set for the median ensemble per service. Ensemble output maps were calculated with different approaches following the supporting documentation and associated publication. Uncertainty estimates for these services are included as variation among contributing model outputs and among the employed ensemble approaches. The work was completed under the ‘EnsemblES - Using ensemble techniques to capture the accuracy and sensitivity of ecosystem service models’ project (NE/T00391X/1) funded by the UKRI Landscape Decisions programme, with additional funding from ES/R009279/1 (MobilES) & ES/T007877/1 (RUST). Full details about this dataset can be found at https://doi.org/10.5285/bd940dad-9bf4-40d9-891b-161f3dfe8e86

DescriptionThe Highway key is a label from OpenStreetMap which aims to map and document any kind of road, street or path. More information on the tag here. LimitationsBear in mind that OpenStreetMap (OSM) is a digital map database of the world built through crowdsourced volunteered geographic information (VGI). Therefore, there is no systematic quality check performed on the data, and the detail, precision and accuracy varies across space. AttributesOBJECTID: Assigned by WWF. Unique identifierhighway: Type of road facility (motorway, trunk, primary, secondary, tertiary)name: Name of the road facilitysource: Source of the Feature (Landsat, Bing, GPS, Yahoo)surface: Type of surface (paved, unpaved, asphalt, ground) oneway: Direction of flow in only one direction (N: No, Y: Yes).maxspeed: Maximum speed allowed (km/h)lanes: Number of traffic lanes for general purpose traffic, also for buses and other specific classes of vehicleservice: Other type of facilities in the road (alley, driveway, parking_aisle)source: Source of the feature (Landsat, Bing)

This data results from the NRSC's ongoing 1:25000 UK Aerial Photography Programme; a project designed to maintain an up to date aerial coverage of the United Kingdom, covering the complete area at least every 5 years.

The Orthoview product has been generated from vertical aerial photographs. The photographs have been orthorectified (to correct for distortion towards their edges) then mosaiced to provide a seamless dataset for the UK at a 0.5 metre resolution. This allows imagery for any area of interest to be generated without issues associated with scenes falling across multiple photographs.

In addition to its prime application in photogrammetric mapping (from updating and contouring existing maps to preparing large scale engineering plans), the data is 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 in digital form and can be supplied on either Exabyte, CD-ROM or CCT. Various hard copy forms can also be generated, including posters and photographic positives/negatives. Price lists and further information are available from the National Remote Sensing Centre (NRSC).

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

[From The Landmap Project: Introduction, "http://www.landmap.ac.uk/background/intro.html"]

 A joint project to provide orthorectified satellite image mosaics of Landsat,
 SPOT and ERS radar data and a high resolution Digital Elevation Model for the
 whole of the UK. These data will be in a form which can easily be merged with
 other data, such as road networks, so that any user can quickly produce a
 precise map of their area of interest.

 Predominately aimed at the UK academic and educational sectors these data and
 software are held online at the Manchester University super computer facility
 where users can either process the data remotely or download it to their local
 network.

 Please follow the links to the left for more information about the project or
 how to obtain data or access to the radar processing system at MIMAS. Please
 also refer to the MIMAS spatial-side website,
 "http://www.mimas.ac.uk/spatial/", for related remote sensing materials.

QUEST projects both used and produced an immense variety of global data sets that needed to be shared efficiently between the project teams. These global synthesis data sets are also a key part of QUEST's legacy, providing a powerful way of communicating the results of QUEST among and beyond the UK Earth System research community. This dataset contains a map of a ecosystem. This map depicts the 825 terrestrial ecoregions of the globe. Ecoregions are relatively large units of land contain ing distinct assemblages of natural communities and species, with boundaries that approximate the original extent of natural communities prior to major land-use change. This comprehensive, global map provides a useful framework for conducting biogeographical or macroecological research, for identifying areas of outstanding biodiversity and conse rvation priority, for assessing the representation and gaps in conservation efforts worldwide, and for communicating the global distribution of natural communities on earth.

The map is designed to be used as a basemap by marine GIS professionals and as a reference map by anyone interested in ocean data. The basemap focuses on bathymetry. It also includes inland waters and roads, overlaid on land cover and shaded relief imagery.The Ocean Base map currently provides coverage for the world down to a scale of ~1:577k; coverage down to ~1:72k in United States coastal areas and various other areas; and coverage down to ~1:9k in limited regional areas.The World Ocean Reference is designed to be drawn on top of this map and provides selected city labels throughout the world. This web map lets you view the World Ocean Base with the Reference service drawn on top. Article in the Fall 2011 ArcUser about this basemap: "A Foundation for Ocean GIS".The map was compiled from a variety of best available sources from several data providers, including General Bathymetric Chart of the Oceans GEBCO_08 Grid version 20100927 and IHO-IOC GEBCO Gazetteer of Undersea Feature Names August 2010 version (https://www.gebco.net), National Oceanic and Atmospheric Administration (NOAA) and National Geographic for the oceans; and Garmin, HERE, and Esri for topographic content. You can contribute your bathymetric data to this service and have it served by Esri for the benefit of the Ocean GIS community. For details on the users who contributed bathymetric data for this map via the Community Maps Program, view the list of Contributors for the Ocean Basemap. The basemap was designed and developed by Esri. The GEBCO_08 Grid is largely based on a database of ship-track soundings with interpolation between soundings guided by satellite-derived gravity data. In some areas, data from existing grids are included. The GEBCO_08 Grid does not contain detailed information in shallower water areas, information concerning the generation of the grid can be found on GEBCO's website: https://www.gebco.net/data_and_products/gridded_bathymetry_data/. The GEBCO_08 Grid is accompanied by a Source Identifier (SID) Grid which indicates which cells in the GEBCO_08 Grid are based on soundings or existing grids and which have been interpolated. The latest version of both grids and accompanying documentation is available to download, on behalf of GEBCO, from the British Oceanographic Data Centre (BODC) https://www.bodc.ac.uk/data/online_delivery/gebco/.The names of the IHO (International Hydrographic Organization), IOC (intergovernmental Oceanographic Commission), GEBCO (General Bathymetric Chart of the Oceans), NERC (Natural Environment Research Council) or BODC (British Oceanographic Data Centre) may not be used in any way to imply, directly or otherwise, endorsement or support of either the Licensee or their mapping system.Tip: Here are some famous oceanic locations as they appear this map. Each URL launches this map at a particular location via parameters specified in the URL: Challenger Deep, Galapagos Islands, Hawaiian Islands, Maldive Islands, Mariana Trench, Tahiti, Queen Charlotte Sound, Notre Dame Bay, Labrador Trough, New York Bight, Massachusetts Bay, Mississippi Sound

This layer shows the purchasing power per capita in United Kingdom in 2023, in a multiscale map (United Kingdom, Country, Region, County, District, Lower Super Output Area, and Census Output Area). Nationally, the purchasing power per capita is 22,986 British pound. Purchasing Power describes the disposable income (income without taxes and social security contributions, including received transfer payments) of a certain area's population. The figures are in British pound (GBP) per capita.The pop-up is configured to show the following information at each geography level:Purchasing power per capitaPurchasing power per capita by various categoriesThe source of this data is Michael Bauer Research. The vintage of the data is 2023. This item was last updated in February, 2024 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

In the century between Napoleon's defeat and the outbreak of the First World War (known as the "Pax Britannica"), the British Empire grew to become the largest and most powerful empire in the world. At its peak in the 1910s and 1920s, it encompassed almost one quarter of both the world's population and its land surface, and was known as "the empire on which the sun never sets". The empire's influence could be felt across the globe, as Britain could use its position to affect trade and economies in all areas of the world, including many regions that were not part of the formal empire (for example, Britain was able to affect trading policy in China for over a century, due to its control of Hong Kong and the neighboring colonies of India and Burma). Some historians argue that because of its economic, military, political and cultural influence, nineteenth century Britain was the closest thing to a hegemonic superpower that the world ever had, and possibly ever will have. "Rule Britannia" Due to the technological and logistical restrictions of the past, we will never know the exact borders of the British Empire each year, nor the full extent of its power. However, by using historical sources in conjunction with modern political borders, we can gain new perspectives and insights on just how large and influential the British Empire actually was. If we transpose a map of all former British colonies, dominions, mandates, protectorates and territories, as well as secure territories of the East India Trading Company (EIC) (who acted as the precursor to the British Empire) onto a current map of the world, we can see that Britain had a significant presence in at least 94 present-day countries (approximately 48 percent). This included large territories such as Australia, the Indian subcontinent, most of North America and roughly one third of the African continent, as well as a strategic network of small enclaves (such as Gibraltar and Hong Kong) and islands around the globe that helped Britain to maintain and protect its trade routes. The sun sets... Although the data in this graph does not show the annual population or size of the British Empire, it does give some context to how Britain has impacted and controlled the development of the world over the past four centuries. From 1600 until 1920, Britain's Empire expanded from a small colony in Newfoundland, a failing conquest in Ireland, and early ventures by the EIC in India, to Britain having some level of formal control in almost half of all present-day countries. The English language is an official language in all inhabited continents, its political and bureaucratic systems are used all over the globe, and empirical expansion helped Christianity to become the most practiced major religion worldwide. In the second half of the twentieth century, imperial and colonial empires were eventually replaced by global enterprises. The United States and Soviet Union emerged from the Second World War as the new global superpowers, and the independence movements in longstanding colonies, particularly Britain, France and Portugal, gradually succeeded. The British Empire finally ended in 1997 when it seceded control of Hong Kong to China, after more than 150 years in charge. Today, the United Kingdom consists of four constituent countries, and it is responsible for three crown dependencies and fourteen overseas territories, although the legacy of the British Empire can still be seen, and it's impact will be felt for centuries to come.

This version of The Digital Chart of the World (DCW) is an Environmental Systems Research Institute, Inc. (ESRI) product originally developed for the US Defense Mapping Agency (DMA) using DMA data. This data was downloaded from the Penn State web site and then converted to Shapefile format using ArcMap.

This dataset is derived from the Vector Map (VMap) Level 0 database; the third edition of the Digital Chart of the World. The second edition was a limited release item published 1995 09. The product is dual named to show its lineage to the original DCW, published in 1992, while positioning the revised product within a broader emerging-family of VMap products.

VMap Level 0 is a comprehensive 1:1,000,000 scale vector basemap of the world. It consists of cartographic, attribute, and textual data stored on compact disc read only memory (CD-ROM). The primary source for the database is the National Imagery and Mapping Agency's (NIMA) Operational Navigation Chart (ONC) series. This is the largest scale unclassified map series in existence that provides consistent, continuous global coverage of essential basemap features. The database contains more than 1,900 megabytes of vector data and is organized into 10 thematic layers. The data includes major road and rail networks, major hydrological drainage systems, major utility networks (cross-country pipelines and communication lines), all major airports, elevation contours (1000 foot (ft), with 500ft and 250ft supplemental contours), coastlines, international boundaries and populated places.

British Virgin Islands World-Wide Human Geography Data (WWHGD) Hurricane Irma data

DescriptionThe railway key is a label from OpenStreetMap which aims to map and document all types of railways including light rail, mainline railways, metros, monorails and trams. More information on the tag here. LimitationsBear in mind that OpenStreetMap (OSM) is a digital map database of the world built through crowdsourced volunteered geographic information (VGI). Therefore, there is no systematic quality check performed on the data, and the detail, precision and accuracy varies across space.AttributesOBJECTID: Assigned by WWF. Unique identifierrailway: Type or status of railway facility (platform, subway, rail)electrified: Source of electricity (contact_line: a power line over the train head, rail: a third rail near the track supplying the train with power, yes: electrified track, but no details available, no: track with no power supply)Gauge: Voltage used for the railway facility operation (W)

As part of the ESA Land Cover Climate Change Initiative (CCI) project a new set of Global Land Cover Maps have been produced. These maps are available at 300m spatial resolution for each year between 1992 and 2015.Each pixel value corresponds to the classification of a land cover class defined based on the UN Land Cover Classification System (LCCS). The reliability of the classifications made are documented by the four quality flags (decribed further in the Product User Guide) that accompany these maps. Data are provided in both NetCDF and GeoTiff format.Further Land Cover CCI products, user tools and a product viewer are available at: http://maps.elie.ucl.ac.be/CCI/viewer/index.php . Maps for the 2016-2020 time period have been produced in the context of the Copernicus Climate Change service, and can be downloaded from the Copernicus Climate Data Store (CDS).

scPDSI consists of global maps of monthly self-calibrating Palmer Drought Severity Index (scPDSI) which have been calculated for 1901-2009 based on the CRU TS 3.10.01 data sets.

The Palmer Drought Severity Index (PDSI) is a measure of soil moisture availability that has been used extensively to study droughts and wet spells in the contiguous USA and elsewhere, particularly as the primary indicator of the severity and extent of recent droughts. Recently, the PDSI has become more popular as a drought metric in studies quantifying possible trends in future soil moisture availability and it has been used as the basis for reconstructing past variations in drought.

As part of the ESA Land Cover Climate Change Initiative (CCI) project a static map of open water bodies at 150 m spatial resolution at the equator has been produced.

The CCI WB v4.0 is composed of two layers:

1. A static map of open water bodies at 150 m spatial resolution resulting from a compilation and editions of land/water classifications: the Envisat ASAR water bodies indicator, a sub-dataset from the Global Forest Change 2000 - 2012 and the Global Inland Water product.

This product is delivered at 150 m as a stand-alone product but it is consistent with class "Water Bodies" of the annual MRLC (Medium Resolution Land Cover) Maps. The product was resampled to 300 m using an average algorithm. Legend : 1-Land, 2-Water

1. A static map with the distinction between ocean and inland water is now available at 150 m spatial resolution. It is fully consistent with the CCI WB-Map v4.0. Legend: 0-Ocean, 1-Land.

To cite the CCI WB-Map v4.0, please refer to : Lamarche, C.; Santoro, M.; Bontemps, S.; D’Andrimont, R.; Radoux, J.; Giustarini, L.; Brockmann, C.; Wevers, J.; Defourny, P.; Arino, O. Compilation and Validation of SAR and Optical Data Products for a Complete and Global Map of Inland/Ocean Water Tailored to the Climate Modeling Community. Remote Sens. 2017, 9, 36. https://doi.org/10.3390/rs9010036

The GEBCO_2020 Grid is a global continuous terrain model for ocean and land with a spatial resolution of 15 arc seconds. In regions outside of the Arctic Ocean area, the grid uses as a base Version 2 of the SRTM15_plus data set (Tozer, B. et al, 2019). This data set is a fusion of land topography with measured and estimated seafloor topography. Included on top of this base grid are gridded bathymetric data sets developed by the four Regional Centers of The Nippon Foundation-GEBCO Seabed 2030 Project. The GEBCO_2020 Grid represents all data within the 2020 compilation. The compilation of the GEBCO_2020 Grid was carried out at the Seabed 2030 Global Center, hosted at the National Oceanography Centre, UK, with the aim of producing a seamless global terrain model. Outside of Polar regions, the gridded bathymetric data sets supplied by the Regional Centers, as sparse grids, i.e. only grid cells that contain data were populated, were included on to the base grid without any blending. The data sets supplied in the form of complete grids (primarily areas north of 60N and south of 50S) were included using feather blending techniques from GlobalMapper software. The GEBCO_2020 Grid has been developed through the Nippon Foundation-GEBCO Seabed 2030 Project. This is a collaborative project between the Nippon Foundation of Japan and the General Bathymetric Chart of the Oceans (GEBCO). It aims to bring together all available bathymetric data to produce the definitive map of the world ocean floor by 2030 and make it available to all. Funded by the Nippon Foundation, the four Seabed 2030 Regional Centers include the Southern Ocean - hosted at the Alfred Wegener Institute, Germany; South and West Pacific Ocean - hosted at the National Institute of Water and Atmospheric Research, New Zealand; Atlantic and Indian Oceans - hosted at the Lamont Doherty Earth Observatory, Columbia University, USA; Arctic and North Pacific Oceans - hosted at Stockholm University, Sweden and the Center for Coastal and Ocean Mapping at the University of New Hampshire, USA.

A lookup between 2021 Output Areas (OA) and countries as at 31 December 2021 in England and Wales. (File Size 6.4 MB).Field Names – OA21CD, CTRY22CD, CTRY22NM, CTRY22NMWField Types – Text, Text, Text, TextField Lengths – 9, 9, 7, 7