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.

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

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.

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.

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.

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, 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

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 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 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/

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.

The maps below show the population of the UK in 2022, at country, region, county and postcode sector level. The maps also provide information about the relative wealth, education and employment of people living in different areas.This map shows different countries of the UK.

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.

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)

Overview

Empower your location data visualizations with our edge-matched polygons, even in difficult geographies.

Our self-hosted geospatial data cover postal divisions for the whole world. The geospatial data shapes are offered in high-precision and visualization resolution and are easily customized on-premise.

Use cases for the Global Boundaries Database (Geospatial data, Map data, Polygon daa)

• In-depth spatial analysis

• Clustering

• Geofencing

• Reverse Geocoding

• Reporting and Business Intelligence (BI)

Product Features

• Coherence and precision at every level

• Edge-matched polygons

• High-precision shapes for spatial analysis

• Fast-loading polygons for reporting and BI

• Multi-language support

For additional insights, you can combine the map data with:

• Population data: Historical and future trends

• UNLOCODE and IATA codes

• Time zones and Daylight Saving Time (DST)

Data export methodology

Our location data packages are offered in variable formats, including - .shp - .gpkg - .kml - .shp - .gpkg - .kml - .geojson

All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why companies choose our map data

• Precision at every level

• Coverage of difficult geographies

• No gaps, nor overlaps

Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

Explore a full description of the map.This map layer shows the 24 time zones commonly used in the Greenwich Mean Time model. The hours added or subtracted from the time in Greenwich are marked on the map. For example, if it is 1:00 p.m. in London, England, United Kingdom, it is 6:30 pm in New Delhi, Delhi, India (+5.50), and 5:00 a.m. in Los Angeles, California, United States (-8.00). CreditsEsri, from National Geographic MapMakerTerms of Use This work is licensed under the Esri Master License Agreement.View Summary | View Terms of Use

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.

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)

A PDF map that shows the local authority districts, counties and unitary authorities in the United Kingdom as at April 2023. The map has been created to show the United Kingdom from country level down to local authority district level. (File Size - 1,909 KB)

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