Besucherstromanalyse via re:publica W-LAN:
Nice: Open Data City haben die Besucherströme der re:publica per WLan visualisiert: „Wie haben sich die Besucherströme der re:publica 2013 von einem Vortrag zum nächsten bewegt? Der Konferenzplan zeigt die mit dem dortigen W-LAN verbundenen Geräte – mit gedrückter Maustaste/ per Touch lassen sich Punkte markieren.“ (via Spreeblick)
DataVisualization of repetitive Arrested Development-Jokes:
Ich bin selbst sehr spät zur Arrested Development-Party gekommen und habe die Serie erst sehr spät irgendwann letzten Sommer nachgeholt. Den Hype habe ich nicht verstanden, aber toll, ja, genau wie das hier: Recurring Developments – An interactive visualization of Jokes in Arrested Development.
Visualization of the Reddit Network
Schöne Visualisierung des internen Netzwerks auf Reddit von sharkbait784, welche Sub-Reddits mit welchen am engsten verbunden sind und am häufigsten aufeinander verlinken. Schade, dass er die bekannten Subreddits per se rausgeschmissen hat und nicht optional zuschaltbar sind. Immer noch superinteressant und schön zu sehen, welche Sub-Netzwerke es dort gibt. Der Gnubbel rechts ist zum Beispiel das Perv-Network ausgehend von /r/nsfw.
I’ve produced a graph showing links between subreddits, where a link is defined as a redditor posting a link in one subreddit to another subreddit. This graph was based on data collected from public Reddit posts since 2008, kindly provided by Deimorz.
In order to identify the most interesting cliques, the following process was applied to the data (if you aren’t familiar with the nomenclature of graph theory, a “node” is a point on a graph (so one of the subreddits in this case), an “edge” is a link between two nodes and the “degree” of a node is the number of edges connected to it):
- Removed edges between subreddits that have less than eight occurrances.
- Removed nodes with a degree greater than 75 (this was enough to get rid of every sub in the top 20 subreddits (by subscriber). Since these subs are likely to link to a wide variety of topics, an association with one of these subs is not particularly interesting to us.
The Reddit network (via MeFi)
PBS Off Book about The Art of Data Visualization
Wo wir grade bei Datenvisualisierung sind: Tolle neue Folge von PBSs Minidoku-Reihe Off Book.
Humans have a powerful capacity to process visual information, skills that date far back in our evolutionary lineage. And since the advent of science, we have employed intricate visual strategies to communicate data, often utilizing design principles that draw on these basic cognitive skills. In a modern world where we have far more data than we can process, the practice of data visualization has gained even more importance. From scientific visualization to pop infographics, designers are increasingly tasked with incorporating data into the media experience. Data has emerged as such a critical part of modern life that it has entered into the realm of art, where data-driven visual experiences challenge viewers to find personal meaning from a sea of information, a task that is increasingly present in every aspect of our information-infused lives.
Vorher auf Nerdcore:
PBS Offbook: Can Hackers Be Heroes?
PBS Offbook: The Art of Illustration
PBS Minidoc about Photoshopping
PBS on Videogames
PBS Off Book about animated GIFs
PBS Minidoc: The Rise of Webcomics
The Art of Glitch
Minidoku: Will 3D Printing Change the World?
The Evolution of 8-Bit Art
Mini-Doku: The Rise of Competitive Gaming & E-Sports
Mini-Doc: The Art of Creative Coding
Mini-Doku: The Creativity of Indie Video Games
We ❤ Retro Media: Vinyl, VHS, Tapes & Film
PBS Off Book about Hacker-Art-Group F.A.T. Lab
PBS on Videogames
PBS on Generative Art
PBS-Minidoku: The Evolution of Music Online
DataVisualized SloMo High Frequency-Trading
Youtube Direkttrade, via Motherboard
Die Marktanalysten von Nanex, die vor einem Jahr bereits High Frequency Trading in einem sensationellen GIF visualisierten, haben nun eine halbe Sekunde HFT auf sechs Minuten gedehnt und visualisiert.
Watch High Frequency Traders (HFT) at the millisecond level jam thousands of quotes in the stock of Johnson and Johnson (JNJ) through our financial networks on May 2, 2013. Video shows 1/2 second of time. If any of the connections are not running perfectly, High Frequency Traders can profit from the price discrepancies that result. There is no economic justification for this abusive behavior.
Each box represents one exchange. The SIP (CQS in this case) is the box at 6 o’clock. It shows the National Best Bid/Offer. Watch how much it changes in a fraction of a second. The shapes represent quote changes which are the result of a change to the top of the book at each exchange. The time at the bottom of the screen is Eastern Time HH:MM:SS:mmm (mmm = millisecond). We slow time down so you can see what goes on at the millisecond level. A millisecond (ms) is 1/1000th of a second.
Note how every exchange must process every quote from the others — for proper trade through price protection. This complex web of technology must run flawlessly every millisecond of the trading day, or arbitrage (HFT profit) opportunities will appear. It is easy for HFTs to cause delays in one or more of the connections between each exchange.
Vorher auf Nerdcore:
Son of Godzillas Bad Hair Day: High Frequency Trading Ethernet-Engine
High Frequency Trading in Super Slow Audio
High Frequency Trading GIF’d
Tweeted Daily Happiness Averages
Schöne Visualisierung der – äh – Durchschnittsglücklichkeit anhand von Tweets (mit dem etwas verunglückten Namen Hedonometer). Dienstag is a bitch, Montag auch, Mittwoch und Donnerstag sind sind wohl immer noch bemerkenswert schlecht gelaunt, Sonntage sind eher bipolar, Samstage sind twitternachweislich am glücklichsten – but it’s Friday I’m in love.
Our hedonometer is based on people’s online expressions, capitalizing on data-rich social media, and we’re measuring how people present themselves to the outside world. For our first version of hedonometer.org, we’re using Twitter as a source but in principle we can expand to any data source in any language (more below). We’ll also be adding an API soon.
Meteorite Data-Visualization:
„BOLIDES is my take on data collected and provided by the The Meteoritical Society reporting all the reported meteorites that hit the Earth. The visualization focuses mainly on meteorites that were eye-witnessed when falling and hitting the ground.“
Data Visualizing the Identity of Satoshi Nakamoto
Spannendes Posting auf Bitslog, wo man die ersten Bitcoins visualisiert hat. Ich steig’ selbst nicht ganz durch, aber anscheinend lässt sich anhand dieses Verfahrens, wenn die Identität auch nur einer Bitcoin-Transaktion rauskommt, die Identität von Bitcoin-Erschaffer Satoshi Nakamoto quasi per Reverse Engineering herausfinden. Ich tendiere bei Bitcoins mittlerweile zu „Größtes Netz-Wirtschafts-Kunstprojekt aller Zeiten“, wer weiß, was da am Ende bei rauskommt…
In the following graphs each dot is the creation of 50 BTC. I have only analyzed and printed graphs from block 0 upto block 36288. […] I can’t assure with 100% certainty that the all the black dots are owned by Satoshi, but almost all are owned by a single entity, and that entity began mining right from block 1, and with the same performance as the genesis block. It can be identified by constant slope segments that occasionally restart. Also this entity is the only entity that has shown complete trust in Bitcoin, since it hasn’t spend any coins (as last as the eye can see). I estimate at eyesight that Satoshi fortune is around 1M Bitcoins, or 100M USD at current exchange rate.[…]
One of the consequences of these graph is that if the real name of the sender of a single transaction belonging to the entity is identified, then Satoshi mystery identity will be revealed. I bet that this will happen in the days following this post.
The Well Deserved Fortune of Satoshi Nakamoto, Bitcoin creator, Visionary and Genius (via Adafruit)
Listen To Bitcoin:
Vertonte Echtzeitdaten der Bitcoin-Exchange MTGox: „Realtime Bitcoin transaction and trade visualizer.“
Instrument-Visualization of the Beatles’ Discography
Großartige Posterserie der Popchart Labs, die den kompletten Katalog der Beatles nach Instrumentierung aufgeschlüsselt und das ganze in drei großformatige Infografik-Prints visualisiert haben. Man sieht in diesen Infografiken sehr genau die vor allem in ihrer mittleren Schaffensphase unheimlich komplex werdenden Instrumentierungen. Toll! Volume 1 gibt’s hier, Vol.2 dort, das dritte Poster da und ein Box-Set mit allen drei Prints kann man hier kaufen.
The complete collection of our Beatles Song Charts, ready to cover a whole wall with the complete discography of the Fab Four.
Volume I covers all original compositions from their very first releases through “Help!” in 1965, showing the group’s progression from simple, catchy singles to complex arrangements like “You’ve Got to Hide Your Love Away.”
Volume II covers their explosively creative middle period, from the release of “Rubber Soul” through “Magical Mystery Tour,” as the band incorporated exotic instruments such as the dilruba and tambura, as well as improvised sounds such as clinking glasses, rattling chains, and more.
Volume III covers their final period, from the “White Album” through “Let It Be,” as the band continued to push the possibilities of recorded music on songs like “Revolution 9″ and “Across the Universe.”
Visualizing Weezers Energy-Distribution
Schöne Spielerei mit Statistiken und Mucke: ArtistX visualisiert die Echo Nest-Datenbank und die Attribute, die Künstlern dort zugewiesen werden. So kann man dann zum Beispiel Weezers Brett-Verteilung ansehen. Sweet!
Distribution: Type the name of the artist, select an attribute and see the distribution of songs with that attribute. Click on a bar to start playing songs with that range of values for the attribute. Scatter: Type the name of the artist, select attributes for the X, Y axis along with the color and size of the displayed points. Click on a song to hear it.
Attributes
Danceability – how danceable a song is. 0 is least danceable, 100 is most danceable.
Duration – the length of the song in seconds.
Energy – the overall energy of the song, 0 is least, 100 is most.
Hotttnesss – the popularity of the song, 0 is least, 100 is most.
Key – the key the song. 0 is C, 1 is C# and so on.
Liveness – the likelihood that a song was performed in front of an audience. Above 80 is usually live.
Loudness – the overall loudness of the song in decibels.
Mode – the mode of the song where major is 0 and minor is 1.
Speechiness – how much spoken word is in the song. 0 is least, 100 is most.
Tempo – the most frequently occuring tempo in the song, in beats-per-minute.
Time signature – the number of beats per measure in the song.
Internet-Visualization: There’s an App for that…
Youtube Direktinternet, via Flowing Data
Peer1-Hosting haben eine interaktive 3D-Visualisierung des Internets als App für iOS und Android am Start. Sweet!
Users can view Internet service providers (ISPs), Internet exchange points, universities and other organizations through two view options – Globe and Network. The app also allows users to generate a trace route between where they are located to a destination node, search for where popular companies and domains are, as well as identify their current location on the map.
First Pic from Mars on TV
Wusste ich auch noch nicht: Das erste Bild vom Mars im Fernsehen entstand, indem sie die Daten der Helligkeitswerte der Pixel ausdruckten, die Zahlenkolonnen aneinanderklebten und wie bei Malen-Nach-Zahlen colorierten. Vintage analoge Datenvisualisierung from Space, quasi.
This image represents the first view of another planet from a vantage point in space. It was taken on July 15th, 1965, when the space probe Mariner 4 flew by only 6,118 miles from the surface of Mars. Before this image the most sophisticated, high res image of Mars was this image by Percival Lowell from the late 1800′s. […]
After a few variations, it seemed most efficient to print out the digits and color over them based upon how bright each pixel was. So Mr. Grumm went to a local art store and asked for a set of chalk with different shades of gray. The art store replied that they “did not sell chalk” […], but they did have colored pastels. Richard did not want to spend a lot of time arguing with them, so he bought the pastels (actual pastels seen [besides]), had the 1’s and 0’s printed out on ticker tape about 3in wide, and his team colored them by their brightness level.
Die ganze Story gibt’s hier: First TV Image of Mars, das Bild in HighRes hier. (via Reddit)
Microsoft Viral Search
Youtube Direktviral, via Information Aesthetics
Schönes Spielzeug, das sich derzeit bei Microsoft in der Entwicklung befindet: Mit Viral Search kann man die vorale Verbreitung von Items im Netz genau untersuchen. Beschränkt sich wohl noch auf Twitter und ist leider nicht öffentlich zugänglich, damit hätte ich gerne mal die #aufschrei-Debatte visualisiert.
What does it mean for online content to “go viral”? An analysis of almost a billion information cascades on Twitter news, videos, and photos has produced the first quantitative notion of whether something has indeed gone viral, thereby enabling further research into topic experts, trending topics, and viral-incident metrics.
Data-Visualization of Twitter-Communications:
Schöne Datenvisualisierung von Santiago Ortiz: „One week of conversations at Twitter Co. from 2/15/13 to 2/22/13.“
