Global Administrative Unit Layers (GAUL), national level (level 0) for reference year 2008 - version 2009, Dec. 2008

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Provided by The European environmental Agency (EEA)

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Dataset information

Country of origin
Updated
Created
Available languages
English
Keywords
Quality scoring
80

Dataset description

The Global Administrative Unit Layers (GAUL) is an initiative implemented by FAO within the EC-FAO Food Security Programme funded by the European Commission (http://www.foodsecinfoaction.org/News/news_06_06.htm). The GAUL aims at compiling and disseminating the most reliable spatial information on administrative units for all the countries in the world, providing a contribution to the standardization of the spatial dataset representing administrative units. The GAUL always maintains global layers with a unified coding system at country, first (e.g. regions) and second administrative levels (e.g. districts). In addition, when data is available, it provides layers on a country by country basis down to third, fourth and lowers levels. The GAUL is released once a year and the target beneficiary of the GAUL data is the UN community, the Universities and other authorized international and national institutions/agencies. Data might not be officially validated by authoritative national sources and can not be distributed to the general public. A disclaimer should always accompany any use of the GAUL data. About 21 countries have been updated respect to the previous release (see countries_updated.dbf). The GAUL keeps track of administrative units that has been changed, added or dismissed in the past for political causes. Changes implemented in different years are recorded in the GAUL on different layers. For this reason the GAUL product is not a single layer but a group of layers, named "GAUL Set" (see Gaul_Set.pdf). The original dataset containing Level 0 administrative units has been combined by EEA with some attributes data provided within GAUL in a separate file. No data from outside GAUL have been used and it is possible to reconstruct the original dataset by removing the extra attributes added.
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