## 5 Conclusions  
In this study, an automated system based on Web data extraction and evaluation algorithms was presented. This system collected a large sample of.eu websites and inferred their original country of origin, making use of a series of metrics regarding the website's various technical and content related aspects. Through its use, the landscape of the.eu TLD usage by each country was outlined. By defining the _eu/gdp_ ratio of that usage divided by the country's GDP as a percentage of the total GDP of the European Union, the involvement of each country in the.eu TLD was measured independently from its economic size. The interrelation of this ratio and the ratio of each country's national ccTLD usage divided by its GDP percentage (_nat/gdp_) was investigated and the conclusion was that there is a strong significant correlation between them, which acts as a confirmation to an extent of the ability of the algorithm to infer each.eu website's country of origin. By investigating the interrelation between both these ratios and the _ex/gdp_ ratio that presents the importance of a country's external trade in relevance with its economic size, it was concluded that involvement in the.eu TLD and external trade are significantly correlated even when the country's economic size is removed from the picture. Especially when considering countries with lower total external trade revenue, the relationship between the _eu/gdp_ and _ex/gdp_ variables is even stronger. The main limitations of this research appeared in the process of detecting the country of origin. Some notable weak points have already been pointed out, such as the difficulty of inferring Ireland's presence because of the common use of the English language, or the inability to attribute a country of origin to all such domains. For some domains the concept of country of origin might not necessarily apply due to their international nature. Despite these, the algorithm put a lot of effort into handling edge cases and the result was satisfactory. Another limitation was the use of the Common Crawl Index to identify both the.eu domains and to determine the landscape of national ccTLDs. Although ccIndex data is almost certainly representative, there may be value in repeating the process with websites selected not randomly, but based on their popularity as derived through data traffic from a digital intelligence provider. Finally, it should be emphasized that the Spearman correlation between _eu/gdp_ and _ex/gdp_, as any Spearman correlation indicates a monotonic relationship only between the input variables and not a linear relationship, and it does not establish causality. There was no proportionality discovered in the increases of each variable. Using a similar methodology in the future, other TLDs can be investigated for their websites' countries of origin beyond the scope of the EU. Analyzing global TLD usage worldwide, especially outside the US, penetration of newer gTLDs in different regional markets and other similar issues can lead to interesting conclusions about the connection of TLDs with economic, social, or even political indexes. Alternatively, a similar methodology can be used to measure the influence of larger countries within their sphere of economic or geopolitical influence. For example, identifying USA oriented websites in Central and South America, or China oriented websites in East and South East Asia. Finally, it might be a good idea to study the relationships established in this article across a broader time period, either by regularly repeating the study and collecting data in the future, or through usage of archival website data as they can be retrieved from internet archival initiatives. The World Wide Web is the perfect means to reach audience beyond a country's own borders both culturally and of course commercially. But in order to achieve results, recognition and confidence are required.