Bitcoin accepted map

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Non Fungible Tokens NFTs are digital assets that represent objects like art, collectible, and in-game items. They are traded online, often with cryptocurrency, and are generally encoded within smart contracts on a blockchain. Public attention towards NFTs has exploded in , when their market has experienced record sales, but little is known about the overall structure and evolution of its market.

Here, we analyse data concerning 6. First, we characterize statistical properties of the market. Second, we build the network of interactions, show that traders typically specialize on NFTs associated with similar objects and form tight clusters with other traders that exchange the same kind of objects. Third, we cluster objects associated to NFTs according to their visual features and show that collections contain visually homogeneous objects.

Finally, we investigate the predictability of NFT sales using simple machine learning algorithms and find that sale history and, secondarily, visual features are good predictors for price. We anticipate that these findings will stimulate further research on NFT production, adoption, and trading in different contexts. The revolution is not confined to the art market. While NFT adoption in gaming has already reached a certain maturity, for example concerning the trade of in-game objects, different other industries, especially those involved with the production of digital content such as music or video, are experimenting with the technology.

An NFT is a unit of data stored on a blockchain that certifies a digital asset to be unique and therefore not interchangeable, while offering a unique digital certificate of ownership for the NFT 3. Several types of digital objects can be associated to an NFT including photos, videos, and audio. NFTs are now being used to commodify digital objects in different contexts, such as art, gaming, and sports collectibles. The first popular example of NFTs is CryptoKitties, a collection of artistic images representing virtual cats that are used in a game on Ethereum that allows players to purchase, collect, breed, and sell them on Ethereum 5.

In December , CryptoKitties congested the Ethereum network 6. By many considered a chief example of the irrationality driving the cryptocurrency market in 7 , CryptoKitties remained the only popular example of NFTs for almost 2 years. The purchase resulted in the third-highest auction price achieved for a living artist, after Jeff Koons and David Hockney 9. The profitability of NFTs has motivated celebrities to create their own NFTs, with collectibles of NBA and famous football players getting sold for hundreds of thousands dollars Research on NFTs is still limited, and focuses mostly on technical aspects, such as copyright regulations 3 ; components, protocols, standards, and desired properties 13 ; new blockchain-based protocols to trace physical goods 14 ; and the implications that NFTs have on the art world 15 , 16 , in particular as they allow to share secondary sale royalties with the artist.

Empirical studies aiming at characterizing properties of the market have focused on a limited number of NFT collections, such as, CryptoKitties 17 , 18 , Cryptopunks, and Axie 19 , or on a single NFT market, such as, Decentraland 19 , 20 or SuperRare 21 , These analyses revealed that the digital abundance of NFTs in digital games has led to a substantial decrease of their value 17 , and that, even if NFT prices are driven by the prices of cryptocurrencies 19 , the NFT market could be prone to speculation 18 , Further, it was shown that NFTs valued by experts are more successful 21 , and that, based on 16, NFTs sold on the SuperRare market, the structure of the the NFT co-ownership network is highly centralized, and small-world-like 22 , In this paper, we provide a first comprehensive quantitative overview of the NFT market.

To this end, we analyse a large dataset including 6. We start by analysing the overall statistical properties of the NFT market and its evolution over time. NFTs are further clustered based on their visual features. Finally, we present the results of regression and classification models predicting the occurrence of NFT secondary sales and their price. We break down our analysis by NFT categories, which are classified by manual inspection, with references to the classification proposed by NonFungible Corporation 24 , a specialized company that track NFTs sales, and OpenSea 25 , one of the largest NFT marketplace.

However, the exact classification of different categories in which NFTs are used is outside of the scope of the present paper. For example, Art objects can be in some cases classified as Collectibles , while some Game objects may present sophisticated aesthetic and cultural properties that may qualify them as Art.

Items exchanged on the NFT market are organized in collections , sets of NFTs that, in most cases, share some common features. Collections can be widely different in nature, from sets of collectible cards, to selections of art masterpieces, to virtual spaces in online games. We show the top 5 collections in terms of number of unique assets n for each category see Fig. Description of the NFT landscape. The size of each circle is proportional to the number of assets in each collection.

Days with volume below USD are not shown. Results in these panels are averaged over a rolling window of 30 days. Until the end of , the market was fully dominated by the Art category, and in particular by the CryptoKitties collection.

From January , other categories started gaining popularity, both in terms of total volume exchanged see Fig. Importantly, however, the market composition is quite different when considering the number of transactions. Overall, we observe that the share of volume spent in Art has been growing since , while its share of transactions has been decreasing Fig.

The discrepancy between volume and transactions reveals that prices of items categorized as Art are higher, on average, compared to other categories.

We dig further into these differences by looking at the distribution of NFT prices across categories see Fig. The highest prices so far were reached by assets categorized as Art , with 4 NFTs that were sold for more than 1 million dollars. Statistical properties of the NFT market. To assess the market activity, we measured how often individual assets are traded.

When looking at different categories, the distribution of number of sales is affected by cut-off values. For example, the maximum number of sales for assets in the Utility category is 12, while an asset in the Games category is sold more than a thousand times, and an asset in the Art category more than five thousands times. Temporal patterns of secondary sales are unique for each collection, as evidenced by considering the top collection in each category see Fig.

For example, when Cryptokitties emerged in , secondary sale prices were typically lower than the price of their first sale. More recently in , instead, their secondary sale prices have gone up because of an increase in the number of potential customers. Other collections, like Alien, alternated periods when secondary sale prices went down and period when they went up. In the Unstoppable collection secondary sales are rare because NFTs correspond to web domains secured by blockchain technology.

Secondary sale prices. Each horizontal line represents an NFT and each dot a sale. Sales are coloured based on the change in price compared to previous sale see colourbar.

How do traders interact with each other? Are there central actors? We approach these questions adopting a network science approach 23 , We consider the network of trades , where nodes are traders, a directed link from a trader to another exists if the former the buyer purchases at least one NFT from the latter the seller. Each link has a weight corresponding to the total number of items that the buyer bought from the seller. First, we study the behaviour of individual NFT traders by focusing on properties of the nodes.

This result reveals that the average number of daily trades is larger for traders active over long periods of time. The relation between strength and specialization is not monotonic: the most specialized traders have either few less than ten or many more than ten thousands transactions see Fig.

Key network properties. Secondly, we turn to properties of the network links, describing interactions between pairs of traders.

An interesting question is whether traders connect preferentially to traders that have similar strength. We tackle this question by studying the assortativity coefficient r 28 , that measures the correlation between the sum of the weights of all outgoing links the outgoing strength of a given node with the average sum of the weights of incoming links the incoming strength of its neighbours.

Finally, we focus on the network structure. Building upon the result that traders are specialized, we assign each trader to their top collection, and we study the modularity 29 of the network under this partition of nodes.

It reveals that the collections well represent the underlining network structure, where traders specialized in a collection tends to buy and sell NFTs with other traders specialized in the same collection. We now turn to the exploration of how NFTs are connected to one another. Rather than linking all NFTs ever traded by the same trader, this choice allows to understand the relations between NFT that are semantically similar, because they are bought by the same trader in approximately the same period of time.

Further, it ensures that the network structure is not dominated by large cliques. In fact, when two NFTs are purchased simultaneously, this creates two links for each of the two nodes one ingoing and one outgoing.

The next question we ask is: which NFTs are connected to one another? What are the implications of this behaviour on the NFT network structure? It reveals that 1 the network is clustered and 2 the collections well represent the underlining community structure.

That is, some traders buy or sell assets belonging to multiple collections. While the high network modularity reveals that traders tend to purchase assets from the same collection in sequence, the presence of very large SCCs reveals that there are less frequent sequences of purchases in different collections. A visual representation of the trader network including the Art category on February shows the clusters formed by NFT traders specialized in the same collection see Fig.

Similarly, the same visualization for the NFT network shows a similar trend, where NFTs, albeit surrounded by other NFTs in the same collection, tend to form a sparser structure see Fig. Networks visualization.

For visualization purposes, we selected the ten top collections in the Art category on February Visualization is done using Netwulf We find that key results presented above, including the shape of the strength distributions, hold across categories. On the contrary, the Ethereum component has the NFTs are linked to digital assets of different types, including videos, text, animated GIFs, and audio.

Currently, the most popular NFTs are images 10 , With this representation, vectors extracted from images that are visually similar are close in the vector space. To quantify the visual difference between pairs of pictures, we calculated the cosine distance CD between them, a value that goes from zero for identical images to one for highly different images.

We measured such distance between pictures within the same collection and across collections. Figure 6 a shows the matrix of average CD values between all pairs of collections. Values on the diagonal represent the intra-collection CD values, and reveal that most collections have a high degree of homogeneity e.

Legality of cryptocurrency by country or territory

Find a location. A growing number of services and merchants are accepting Bitcoin and Ether Cryptocurrencies all over the world. We offer a convenient, concierge experience with our tellers ready to help you. Read our company update for more details and follow our blog series on cryptocurrencies. More than 15, businesses worldwide accept Bitcoin. To find flight tickets, hotels, rentals, cars, tours, and activities to plan your next trip, you can book your travel with bitcoins using Bitcoin.

Companies from AT&T to WeWork are accepting Bitcoin and other crypto. But do customers want to spend it?

BitcoinCash Map - Bitcoinmap - Crypto Coin Tourist Coinmap

When preparing to buy Bitcoin, one of your first steps should be to find out whether it is legal in your country or not. If you already know the state of your own country but are curious for the rest of them, read on! Bitcoin is not just legal or If you are looking to buy, sell, or spend Bitcoin, you should check if it is legal in your country. In fact, there are many countries with different cryptocurrency regulations. Some of them even single out Bitcoin, allowing it to be used as money, pay taxes, purchase goods, or trade it like a commodity. In other countries, even the mere possession of Bitcoin can get you to prison. Others have not even bothered to regulate it yet, leaving Bitcoin and other cryptos in legal limbo. Although it has had some chance to mature, in the grand scheme of the history of money, Bitcoin is still new. Disclaimer: Although this list is extensive, crypto regulations are often subject to change.

This map shows where cryptocurrency is taking off around the world

bitcoin accepted map

A few days later, the Bitcoin Law was passed, to take effect Sept. Businesses would be required to accept bitcoin for all payments. Bitcoin was originally created to be a form of money outside government control. Using bitcoin as a government-endorsed currency had a number of obvious issues: Cryptocurrency has a stupendous money-laundering problem, the price of bitcoin is incredibly volatile, and cryptocurrencies remain difficult and unwieldy to use. Building a payment system that users trust takes time.

A few South African centsworth on bitcoin 1. Part-time lecturer, Department of Auditing, University of Pretoria.

A beginner's guide to Bitcoin: Everything you need to know about it and how it works

Global examples of regulation and best practices were also looked at. In September, China declared all cryptocurrency transactions illegal, effectively imposing a complete ban. The same month, El Salvador permitted Bitcoin as legal tender. In most parts of the world, this market remains unregulated. The US Securities and Exchange Commission Friday is reported to have rejected rule changes that would have allowed the listing and trading of the VanEck Bitcoin exchange-traded fund.

NWA Council offering $10K in Bitcoin to draw talent to Northwest Arkansas

Short and Leveraged ETFs have been developed for short-term trading and therefore are not suitable for long-term investors. Before you decide on investing in a product like this, make sure that you have understood how the index is calculated. Be aware that for holding periods longer than one day, the expected and the actual return can very significantly. Bitcoin 7. Chart comparison of all ETFs on this index 7. Chart comparison of all ETFs on this category

Abstract background with world map silhouette and many golden bitcoin coins with dash stroke web. Download a Free Preview or High Quality Adobe Illustrator.

Special Report: Cryptos on the rise

There's not a week that goes by when Bitcoin isn't making headlines. But unless you're a trader or interested in cryptocurrencies, what it is has probably flown right over your head. So, why is Bitcoin in the news so much and why should you care? Seen by many as the future of finance, it could have a detrimental impact on the money you have in your pocket.

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Over the decades, businesses and the general public have been awaiting a single currency that could work as a viable medium of exchange.

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In a new report and map, we look at the state of crypto-assets, their risk and regulation, and how their impact and acceptance is evolving around the world. Crypto-assets have become the proverbial double-edged sword among financial experts. The rise and deployment of crypto-assets has pushed digital transformation and has the potential to make payments and transfers more efficient. However, the speed and reach of such transactions — together with the potential for anonymous activity and for transactions without financial intermediaries — also make crypto-assets vulnerable to misuse and raise the risk of money laundering.

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  1. List

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