This dataset shows the global distribution of mangroves, and was produced as joint initiatives of the International Tropical Timber Organization (ITTO), International Society for Mangrove Ecosystems (ISME), Food and Agriculture Organization of the United Nations (FAO), UN Environment World Conservation Monitoring Centre (UNEP-WCMC), United Nations Educational, Scientific and Cultural Organization’s Man and the Biosphere Programme (UNESCO-MAB), United Nations University Institute for Water, Environment and Health (UNU-INWEH) and The Nature Conservancy (TNC). Major funding was provided by ITTO through a Japanese Government project grant; the project was implemented by ISME.

Click on the map below to start exploring. Click on a river to see more information. Or filter to show a single river catchment.

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/

United Kingdom UK: GDP: USD: Gross National Income per Capita: Atlas Method data was reported at 40,530.000 USD in 2017. This records a decrease from the previous number of 42,370.000 USD for 2016. United Kingdom UK: GDP: USD: Gross National Income per Capita: Atlas Method data is updated yearly, averaging 21,055.000 USD from Dec 1970 (Median) to 2017, with 48 observations. The data reached an all-time high of 48,420.000 USD in 2008 and a record low of 2,440.000 USD in 1970. United Kingdom UK: GDP: USD: Gross National Income per Capita: Atlas Method data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s UK – Table UK.World Bank: Gross Domestic Product: Nominal. GNI per capita (formerly GNP per capita) is the gross national income, converted to U.S. dollars using the World Bank Atlas method, divided by the midyear population. GNI is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States.; ; World Bank national accounts data, and OECD National Accounts data files.; Weighted Average;

This dataset shows the global distribution of coral reefs in tropical and subtropical regions. It is the most comprehensive global dataset of warm-water coral reefs to date, acting as a foundation baseline map for future, more detailed, work. This dataset was compiled from a number of sources by UNEP World Conservation Monitoring Centre (UNEP-WCMC) and the WorldFish Centre, in collaboration with WRI (World Resources Institute) and TNC (The Nature Conservancy). Data sources include the Millennium Coral Reef Mapping Project (IMaRS-USF and IRD 2005, IMaRS-USF 2005) and the World Atlas of Coral Reefs (Spalding et al. 2001).

Citation: UNEP-WCMC, WorldFish Centre, WRI, TNC (2018). Global distribution of warm-water coral reefs, compiled from multiple sources including the Millennium Coral Reef Mapping Project. Version 4.0. Includes contributions from IMaRS-USF and IRD (2005), IMaRS-USF (2005) and Spalding et al. (2001). Cambridge (UK): UN Environment World Conservation Monitoring Centre. URL: http://data.unep-wcmc.org/datasets/1

Citations for the separate entities: IMaRS-USF (Institute for Marine Remote Sensing-University of South Florida) (2005). Millennium Coral Reef Mapping Project. Unvalidated maps. These maps are unendorsed by IRD, but were further interpreted by UNEP World Conservation Monitoring Centre. Cambridge (UK): UNEP World Conservation Monitoring Centre

IMaRS-USF, IRD (Institut de Recherche pour le Developpement) (2005). Millennium Coral Reef Mapping Project. Validated maps. Cambridge (UK): UNEP World Conservation Monitoring Centre

Spalding MD, Ravilious C, Green EP (2001). World Atlas of Coral Reefs. Berkeley (California, USA): The University of California Press. 436 pp.

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)

A PDF map that shows International Territorial Levels 2 and 3 in United Kingdom as at January 2021. (File Size - 2.17 MB).

The Historic Environment Opportunity Map for New Woodland dataset identifies areas in England that may be suitable for new woodland, based solely on available Historic Environment data. The dataset categorises land by different opportunity ratings to reflect the potential suitability of land for woodland creation while acknowledging areas of uncertainty due to data availability.The purpose of this dataset is to guide landowners, planners, and decision-makers in considering woodland creation from a historic environment perspective. It should be noted that this dataset only considers the Historic Environment and therefore the opportunity ratings do not guarantee or preclude approval for woodland creation proposals.As any forestry proposal could have the potential to affect the Historic Environment you should contact your local historic environment service. The local historic environment service can provide further data to support woodland creation proposals.NHLE is the official, up to date register of all nationally protected historic buildings and sites in England.SHINE is a single, nationally consistent dataset of non-designated historic and archaeological features from across England that could benefit from land management schemes.The opportunity ratings are as defined:· Favourable - Areas deemed suitable for new woodland on consideration of available Historic Environment data.· Neutral - Areas deemed neither favourable nor unfavourable for new woodland on consideration of available Historic Environment data. Proposals in these areas will require additional consideration of the Historic Environment on a case-by-case basis.· Unclassified - Areas, where SHINE data has been supplied, with no assigned opportunity rating. This illustrates a current absence of recorded data from a Historic Environment perspective. However, as SHINE data is included in the dataset for this area, a degree of confidence may be inferred when considering the absence of historic environment features.· Unclassified (No SHINE supplied) - Areas, where SHINE data has not been supplied, with no assigned opportunity rating. This illustrates a current absence of recorded data from a Historic Environment perspective.· Unsuitable - Areas deemed unsuitable for new woodland on consideration of available Historic Environment data.Unclassified areas may be suitable or unsuitable for new woodland. To better understand these areas, contact the local historic environment service in accordance with the UKFS and Historic Environment Guidance for Forestry in England - GOV.UKThe datasets included in each opportunity rating are as follows:Favourable· Lost Historic Woodlands (ArchAI/Forestry Commission) – An A.I. dataset that identifies areas of woodland depicted on early 20th Century Ordnance Survey mapping which have since been lost.Neutral· Historic Parklands (Zulu Ecosystems) – an A.I. dataset that identifies areas of parkland depicted on early 20th Century Ordnance Survey mapping.· World Heritage Site Core data (Historic England) – Core areas of World Heritage Sites, as designated by UNESCO.· World Heritage Site Buffer (Historic England) – Buffer zones surrounding World Heritage Sites, as designated by UNESCO.· Ridge and Furrow (Low) (ArchAI) – an A.I. dataset that identifies areas of less well-preserved historic ridge and furrow derived from LiDAR data.Unclassified· HER Boundaries (SHINE supplied) – Geographic areas covered by local historic environment services, where SHINE data has been supplied to the Forestry Commission.· HER Boundaries (No SHINE supplied) - Geographic areas covered by local historic environment services where SHINE data has not been supplied to the Forestry Commission.Unsuitable· Historic Landscape Characterisation (HLC) (local historic environment services) – regional datasets that provide information on the historic character of the landscape.· Scheduled Monuments (Historic England) – Protected archaeological sites of national importance.· Scheduled Monuments Buffer – A 20 metre buffer surrounding Scheduled Monuments in-line with UKFS.· Selected Heritage Inventory for Natural England (SHINE)(local historic environment services) – National dataset of non-designated heritage assets.· Registered Parks and Gardens (Historic England) – Parks and Gardens designated as being of national significance.· Registered Battlefields (Historic England) – Battlefields designated as being of national significance.· Ridge and Furrow (High) (ArchAI) – an A.I. dataset that identifies areas of well-preserved historic ridge and furrow derived from LiDAR data.

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.

World Elevation layers are compiled from many authoritative data providers, and are updated quarterly. This map shows the extent of the various datasets comprising the World Elevation dynamic (Terrain, TopoBathy) and tiled (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) services.The tiled services (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) also include an additional data source from Maxar's Precision3D covering parts of the globe.Topography sources listed in the table below are part of Terrain, TopoBathy, Terrain 3D, TopoBathy 3D, World Hillshade and World Hillshade (Dark), while bathymetry sources are part of TopoBathy and TopoBathy 3D only. Data Source Native Pixel Size Approximate Pixel Size (meters) Coverage Primary Source Country/Region

Topography

Australia 1m 1 meter 1 Partial areas of Australia Geoscience Australia Australia

Moreton Bay, Australia 1m 1 meter 1 Moreton Bay region, Australia Moreton Bay Regional Council Australia

New South Wales, Australia 5m 5 meters 5 New South Wales State, Australia DFSI Australia

SRTM 1 arc second DEM-S 0.0002777777777779 degrees 31 Australia Geoscience Australia Australia

Burgenland 50cm 0.5 meters 0.5 Burgenland State, Austria Land Burgenland Austria

Upper Austria 50cm 0.5 meters 0.5 Upper Austria State, Austria Land Oberosterreich Austria

Austria 1m 1 meter 1 Austria BEV Austria

Austria 10m 10 meters 10 Austria BEV Austria

Wallonie 50cm 0.5 meters 0.5 Wallonie state, Belgium Service public de Wallonie (SPW) Belgium

Vlaanderen 1m 1 meter 1 Vlaanderen state, Belgium agentschap Digitaal Vlaanderen Belgium

Canada HRDEM 1m 1 meter 1 Partial areas of Canada Natural Resources Canada Canada

Canada HRDEM 2m 2 meter 2 Partial areas of the southern part of Canada Natural Resources Canada Canada

Denmark 40cm 0.4 meters 0.4 Denmark KDS Denmark

Denmark 10m 10 meters 10 Denmark KDS Denmark

England 1m 1 meter 1 England Environment Agency England

Estonia 1m 1 meter 1 Estonia Estonian Land Board Estonia

Estonia 5m 5 meters 5 Estonia Estonian Land Board Estonia

Estonia 10m 10 meters 10 Estonia Estonian Land Board Estonia

Finland 2m 2 meters 2 Finland NLS Finland

Finland 10m 10 meters 10 Finland NLS Finland

France 1m 1 meter 1 France IGN-F France

Bavaria 1m 1 meter 1 Bavaria State, Germany Bayerische Vermessungsverwaltung Germany

Berlin 1m 1 meter 1 Berlin State, Germany Geoportal Berlin Germany

Brandenburg 1m 1 meter 1 Brandenburg State, Germany GeoBasis-DE/LGB Germany

Hamburg 1m 1 meter 1 Hamburg State, Germany LGV Hamburg Germany

Hesse 1m 1 meter 1 Hesse State, Germany HVBG Germany

Nordrhein-Westfalen 1m 1 meter 1 Nordrhein-Westfalen State, Germany Land NRW Germany

Saxony 1m 1 meter 1 Saxony State, Germany Landesamt für Geobasisinformation Sachsen (GeoSN) Germany

Sachsen-Anhalt 2m 2 meters 2 Sachsen-Anhalt State, Germany LVermGeo LSA Germany

Hong Kong 50cm 0.5 meters 0.5 Hong Kong CEDD Hong Kong SAR

Italy TINITALY 10m 10 meters 10 Italy INGV Italy

Japan DEM5A *, DEM5B * 0.000055555555 degrees 5 Partial areas of Japan GSI Japan

Japan DEM10B * 0.00011111111 degrees 10 Japan GSI Japan

Latvia 1m 1 meters 1 Latvia Latvian Geospatial Information Agency Latvia

Latvia 10m 10 meters 10 Latvia Latvian Geospatial Information Agency Latvia

Latvia 20m 20 meters 20 Latvia Latvian Geospatial Information Agency Latvia

Lithuania 1m 1 meters 1 Lithuania NZT Lithuania

Lithuania 10m 10 meters 10 Lithuania NZT Lithuania

Netherlands (AHN3/AHN4) 50cm 0.5 meters 0.5 Netherlands AHN Netherlands

Netherlands (AHN3/AHN4) 10m 10 meters 10 Netherlands AHN Netherlands

New Zealand 1m 1 meters 1 Partial areas of New Zealand Land Information New Zealand (Sourced from LINZ. CC BY 4.0) New Zealand

Northern Ireland 10m 10 meters 10 Northern Ireland OSNI Northern Ireland

Norway 10m 10 meters 10 Norway NMA Norway

Poland 1m 1 meter 1 Partial areas of Poland GUGIK Poland

Poland 5m 5 meters 5 Partial areas of Poland GUGIK Poland

Scotland 1m 1 meter 1 Partial areas of Scotland Scottish Government et.al Scotland

Slovakia 1m 1 meter 1 Slovakia ÚGKK SR Slovakia

Slovakia 10m 10 meters 10 Slovakia GKÚ Slovakia

Slovenia 1m 1 meter 1 Slovenia ARSO Slovenia

Madrid City 1m 1 meter 1 Madrid city, Spain Ayuntamiento de Madrid Spain

Spain 2m (MDT02 2019 CC-BY 4.0 scne.es) 2 meters 2 Partial areas of Spain IGN Spain

Spain 5m 5 meters 5 Spain IGN Spain

Spain 10m 10 meters 10 Spain IGN Spain

Varnamo 50cm 0.5 meters 0.5 Varnamo municipality, Sweden Värnamo Kommun Sweden

Canton of Basel-Landschaft 25cm 0.25 meters 0.25 Canton of Basel-Landschaft, Switzerland Geoinformation Kanton Basel-Landschaft Switzerland

Grand Geneva 50cm 0.5 meters 0.5 Grand Geneva metropolitan, France/Switzerland SITG Switzerland and France

Switzerland swissALTI3D 50cm 0.5 meters 0.5 Switzerland and Liechtenstein swisstopo Switzerland and Liechtenstein

Switzerland swissALTI3D 10m 10 meters 10 Switzerland and Liechtenstein swisstopo Switzerland and Liechtenstein

OS Terrain 50 50 meters 50 United Kingdom Ordnance Survey United Kingdom

Douglas County 1ft 1 foot 0.3048 Douglas County, Nebraska, USA Douglas County NE United States

Lancaster County 1ft 1 foot 0.3048 Lancaster County, Nebraska, USA Lancaster County NE United States

Sarpy County 1ft 1 foot 0.3048 Sarpy County, Nebraska, USA Sarpy County NE United States

Cook County 1.5 ft 1.5 foot 0.46 Cook County, Illinois, USA ISGS United States

3DEP 1m 1 meter 1 Partial areas of the conterminous United States, Puerto Rico USGS United States

NRCS 1m 1 meter 1 Partial areas of the conterminous United States NRCS USDA United States

San Mateo County 1m 1 meter 1 San Mateo County, California, USA San Mateo County CA United States

FEMA LiDAR DTM 3 meters 3 Partial areas of the conterminous United States FEMA United States

NED 1/9 arc second 0.000030864197530866 degrees 3 Partial areas of the conterminous United States USGS United States

3DEP 5m 5 meter 5 Alaska, United States USGS United States

NED 1/3 arc second 0.000092592592593 degrees 10 conterminous United States, Hawaii, Alaska, Puerto Rico, and Territorial Islands of the United States USGS United States

NED 1 arc second 0.0002777777777779 degrees 31 conterminous United States, Hawaii, Alaska, Puerto Rico, Territorial Islands of the United States; Canada and Mexico USGS United States

NED 2 arc second 0.000555555555556 degrees 62 Alaska, United States USGS United States

Wales 1m 1 meter 1 Wales Welsh Government Wales

WorldDEM4Ortho 0.00022222222 degrees 24 Global (excluding the countries of Azerbaijan, DR Congo and Ukraine) Airbus Defense and Space GmbH World

SRTM 1 arc second 0.0002777777777779 degrees 31 all land areas between 60 degrees north and 56 degrees south except Australia NASA World

EarthEnv-DEM90 0.00083333333333333 degrees 93 Global N Robinson,NCEAS World

SRTM v4.1 0.00083333333333333 degrees 93 all land areas between 60 degrees north and 56 degrees south except Australia CGIAR-CSI World

GMTED2010 7.5 arc second 0.00208333333333333 degrees 232 Global USGS World

GMTED2010 15 arc second 0.00416666666666666 degrees 464 Global USGS World

GMTED2010 30 arc second 0.0083333333333333 degrees 928 Global USGS World

Bathymetry

Bass Strait 30m 2022 0.0003 degrees 30 area of seabed between the coastlines of Victoria and northern Tasmania, extending approximately 460 km from west of King Island to east of Flinders Island. Geoscience Australia Australia

AusBathyTopo 2024 0.0025 degrees 250 Australian continent and Tasmania, and surrounding Macquarie Island and the Australian Territories of Norfolk Island, Christmas Island, and Cocos (Keeling) Islands. Geoscience Australia Australia

Canada west coast 10 meters 10 Canada west coast Natural Resources Canada Canada

Gulf of Mexico 40 feet 12 Northern Gulf of Mexico BOEM Gulf of Mexico

MH370 150 meters 150 MH370 flight search area (Phase 1) of Indian Ocean Geoscience Australia Indian Ocean

Switzerland swissBATHY3D 1 - 3 meters 1, 2, 3 Lakes of Switzerland swisstopo Switzerland

NCEI 1/9 arc second 0.000030864197530866 degrees 3 Puerto Rico, U.S Virgin Islands and partial areas of eastern and western United States coast NOAA NCEI United States

NCEI 1/3 arc second 0.000092592592593 degrees 10 Partial areas of eastern and western United States

This style provides a detailed vector basemap for Great Britain using Open Data featuring the classic Esri topographic map style designed for use with a the GB Hillshade serviceThe vector tile layer is a similar style to the Esri World Topographic Map which is provided in Web Mercator projection.This service contains data supplied by the Ordnance Survey in their Zoomstack product (data last updated December 2024)The map projection is British National Grid.Customise this MapBecause this is a vector tile layer, you can customise the map to change its content and symbology. You are able to turn on and off layers and change their symbols. You can open this style in the vector tile style editor, make your changes and save a copy of your modified style to use yourself.Please send any feedback to VectorTiles@esriuk.com

Data for Figure Atlas.24 from Atlas of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure Atlas.24 shows changes in annual mean surface air temperature and precipitation from reference regions in Europe for different lines of evidence (CMIP5, CORDEX and CMIP6).

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citations: For the report component from which the figure originates: Gutiérrez, J.M., R.G. Jones, G.T. Narisma, L.M. Alves, M. Amjad, I.V. Gorodetskaya, M. Grose, N.A.B. Klutse, S. Krakovska, J. Li, D. Martínez-Castro, L.O. Mearns, S.H. Mernild, T. Ngo-Duc, B. van den Hurk, and J.-H. Yoon, 2021: Atlas. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1927–2058, doi:10.1017/9781009157896.021

Iturbide, M. et al., 2021: Repository supporting the implementation of FAIR principles in the IPCC-WG1 Interactive Atlas. Zenodo. Retrieved from: http://doi.org/10.5281/zenodo.5171760

Figure subpanels

The figure has thirteen panels, with data provided for all panels in the master GitHub repository linked in the documentation.

List of data provided

This dataset contains global monthly precipitation and near surface temperature aggregated by reference region for model output datasets: - CMIP5, CMIP6 (1850-2100) - CORDEX (1970-2100) These are presented separately for land, sea, and land-sea gridboxes (a single run per model). Regional averages are weighted by the cosine of latitude in all cases. An observation-based product (1979-2016) is also provided in the same format for reference: W5E5 (Lange, 2019).

Data provided in relation to figure

All datasets of monthly precipitation and near surface temperature aggregated by region for CMIP5, CMIP6 and CORDEX models are provided in the labelled directories and regions over Europe are used for the production of this figure.

CMIP5 is the fifth phase of the Coupled Model Intercomparison Project. CMIP6 is the sixth phase of the Coupled Model Intercomparison Project. CORDEX is The Coordinated Regional Downscaling Experiment from the WCRP. SSP1-2.6 is based on SSP1 with low climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 2.6 W/m2 in the year 2100. SSP2-4.5 is based on SSP2 with medium challenges to climate change mitigation and adaptation and RCP4.5, a future pathway with a radiative forcing of 4.5 W/m2 in the year 2100. SSP5-8.5 is based on SSP5 where climate change mitigation challenges dominate and RCP8.5, a future pathway with a radiative forcing of 8.5 W/m2 in the year 2100. RCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100. RCP4.5 is the Representative Concentration Pathway for 4.5 Wm-2 global warming by 2100. RCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100. GWL stands for global warming levels. JJA and DJF stand for June, July, August and December, January, February respectively.

Notes on reproducing the figure from the provided data

Data and figures are produced by the Jupyter Notebooks that live inside the notebooks directory. To reproduce each panel in this figure using the 'regional-scatter-plots_R.ipynb' notebook, in regions: select each of the regions over Europe in the top panel of the figure, area: 'land', cordex.domain: 'EUR' and scatter.seasons: list of months by number e.g. JJA: list(c(12, 1, 2), 6:8).

The notebooks describe step by step the basic process followed to generate some key figures of the AR6 WGI Atlas and some products underpinning the Interactive Atlas, such as reference regions, global warming levels, aggregated datasets. They include comments and hints to extend the analysis, thus promoting reusability of the results. These notebooks are provi... For full abstract see: https://catalogue.ceda.ac.uk/uuid/4f314945d3944aeaa12f819fe801dea0.

A PDF map that shows International Territorial Levels 1 and 2 in United Kingdom as at January 2021. (File Size - 1.65 MB).

Data from the British Geological Survey's GeoIndex Map products theme are made available for viewing here. GeoIndex is a website that allows users to search for information about BGS data collections covering the UK and other areas world wide. Access is free, the interface is easy to use, and it has been developed to enable users to check coverage of different types of data and find out some background information about the data. More detailed information can be obtained by further enquiry via the web site: www.bgs.ac.uk/geoindex.

The 1885 UK parliamentary constituencies for Ireland were re-created in 2017 as part of a conference paper delivered at the Southern Irish Loyalism in Context conference at Maynooth University. The intial map only included the territory of the Irish Free State and was created by Martin Charlton and Jack Kavanagh. The remaining six counties of Ulster were completed by Eoin McLaughlin in 2018-19, the combined result is a GIS map of all the parliamentary constituecies across the island of Ireland for the period 1885-1918. The map is available in both ESRI Shapefile format and as a GeoPackage (GPKG). The methodology for creating the constituencies is outlined in detail below.

Methodology

A map showing the outlines of the 1855 – 1918 Constituency boundaries can be found on page 401 of Parliamentary Elections in Ireland, 1801-1922 (Dublin, 1978) by Brian Walker. This forms the basis for the creation of a set of digital boundaries which can then be used in a GIS. The general workflow involves allocating an 1885 Constituency identifier to each of the 309 Electoral Divisions present in the boundaries made available for the 2011 Census of Population data release by CSO. The ED boundaries are available in ‘shapefile’ format (a de facto standard for spatial data transfer). Once a Constituency identifier has been given to each ED, the GIS operation known as ‘dissolve’ is used to remove the boundaries between EDs in the same Constituency. To begin with Walker’s map was scanned at 1200 dots per inch in JPEG form. A scanned map cannot be linked to other spatial data without undergoing a process known as georeferencing. The CSO boundaries are available with spatial coordinates in the Irish National Grid system. The goal of georeferencing is to produce a rectified version of the map together with a world file. Rectification refers to the process of recomputing the pixel positions in the scanned map so that they are oriented with the ING coordinate system; the world file contains the extent in both the east-west and north-south directions of each pixel (in metres) and the coordinates of the most north-westerly pixel in the rectified image.

Georeferencing involves the identification of Ground Control Points – these are locations on the scanned map for which the spatial coordinates in ING are known. The Georeferencing option in ArcGIS 10.4 makes this a reasonably pain free task. For this map 36 GCPs were required for a local spline transformation. The Redistribution of Seats Act 1885 provides the legal basis for the constituencies to be used for future elections in England, Wales, Scotland and Ireland. Part III of the Seventh Schedule of the Act defines the Constituencies in terms of Baronies, Parishes (and part Parishes) and Townlands for Ireland. Part III of the Sixth Schedule provides definitions for the Boroughs of Belfast and Dublin.

The CSO boundary collection also includes a shapefile of Barony boundaries. This makes it possible code a barony in two ways: (i) allocated completely to a Division or (ii) split between two Divisions. For the first type, the code is just the division name, and for the second the code includes both (or more) division names. Allocation of these names to the data in the ED shapefile is accomplished by a spatial join operation. Recoding the areas in the split Baronies is done interactively using the GIS software’s editing option. EDs or groups of EDs can be selected on the screen, and the correct Division code updated in the attribute table. There are a handful of cases where an ED is split between divisions, so a simple ‘majority’ rule was used for the allocation. As the maps are to be used at mainly for displaying data at the national level, a misallocation is unlikely to be noticed. The final set of boundaries was created using the dissolve operation mentioned earlier. There were a dozen ED that had initially escaped being allocated a code, but these were quickly updated. Similarly, a few of the EDs in the split divisions had been overlooked; again updating was painless. This meant that the dissolve had to be run a few more times before all the errors have been corrected.

For the Northern Ireland districts, a slightly different methodology was deployed which involved linking parishes and townlands along side baronies, using open data sources from the OSM Townlands.ie project and OpenData NI.

[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.

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 digitally compiled map includes geology, oil and gas fields, and geologic provinces of Europe. The oil and gas map is part of a worldwide series released on CD-ROM by the World Energy Project of the U.S. Geological Survey. For data management purposes the world is divided into eight energy regions corresponding approximately to the economic regions of the world as defined by the U.S. Department of State. Europe (Region 4) including Turkey (Region 2) includes Albania, Andorra, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Liechtenstein, Luxembourg, The Former Yugoslav Republic of Macedonia, Malta, Monaco, Netherlands, Norway, Poland, Portugal, Romania, San Marino, Serbia and Montenegro, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, Vatican City, Faroe Islands, Gibraltar, Guernsey, Jersey, Isle of Man, Svalbard

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.

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