The Geographical Distribution of Bitcoin
Where are the bitcoin clients? This article will cover the Geographical distribution of Identified Bitcoin clients. Most of them are located in the more developed regions. We’ll also look at the different ways to use the service. After you learn about the different ways to use Bitcoin, you’ll be ready to start interacting with Bitcoin! Let’s begin! Identifying Bitcoin Clients
Locations of Identified Bitcoin clients
One of the ways to map the distribution of Bitcoin is to analyze the IP addresses of Bitcoin clients. According to this study, there was a significant -0.91 correlation between the total amount of Bitcoin owned by identified clients and the average exchange rate during the study period. However, if a client’s IP address is hidden or moved, the results may be inaccurate. Hence, it is not recommended to use this method for large-scale Bitcoin distribution analysis.
Geographical distribution of Identified Bitcoin clients
A recent study reveals a striking correlation between the total amount of Bitcoin owned by identified Bitcoin clients and the global exchange rate. This result suggests that Bitcoin is more widely distributed in western countries than in developing countries. However, the geographical distribution of Bitcoin clients is not completely clear. Since most users of Bitcoin use Tor and may not be able to provide accurate location information, the study’s findings are still highly relevant. However, the findings of this study provide the first step towards an accurate and complete understanding of the Bitcoin distribution.
The amount of Bitcoins in circulation has been increasing. At the time of measurement, there were 13500000 Bitcoins in circulation. However, the total number of identified Bitcoin clients had a high point of 432666 on 10/25/2013, which corresponded to just over 3.2% of all Bitcoins. Data collection may have been affected by systematic differences, or by the different intentions of users in different areas.
For example, transactions belonging to a single user have a low proportion of non-originator clients. If more than one originator client belongs to the same user, the probabilities associated with the transactions are higher. Using the naive Bayes classifier method, these probabilities can be combined and used to identify originator clients more efficiently. We can see this in table 1.