Yesterday was the launch of visual.ly, a newnetworking platform for infographics and datavisualizations.
Once their web servers can handle the load, the new and the already much hyped website allows access to over 2,000 different infographic illustrations, uploaded by designers like JESS3 and David McCandless, and including a large collection of own infographics. Visual.ly has already attracted key partners in publishing, design and distribution, including The Atlantic, CNNMoney.com, eBay, GOOD Magazine, OMD, National Geographic, The Next Web, and Smirnoff. Each of these publications is allowed to upload its own graphics, which can then be embedded and shared via a Visual.ly-created embed code.
If you add the cloud (Big Data in the sky?) and a generation of folks who crave niche news, sprinkle on some Edward Tufte dust, Hans Rosling’s Gapminder, a pinch of David McCandless’s Ted talk… and voilà: maps and bubble charts, decision trees and streamgraphs, sankey diagrams and sparklines flogged across so many blogs.
BUT!:
read it, it has a point (and continue reading when you think she loses the plot...)
read it, there is even a video of a speaking dog!
read the full article here
Stephen Few rückt wieder mal alles ins rechte Licht und vergisst dabei nicht allen mitzuteilen, was für lausige Datenvisualisierer David McCandless und Ben Fry sind. Recht hat er trotzdem.
Hier ein kleiner Auszug:
As Jayne (Steven Few's Ehefrau) began to read my review, she soon reached the place where I wrote that I’ve never seen an effective data visualization by McCandless and sighed. Her thoughts were, “Here he goes again,” fearing that my words might set off another firestorm of angry responses. Then she continued reading, and her attitude changed. Here’s what she said about the natural gas visualizations after finishing:
They make no sense whatsoever. I actually found myself getting angry as I reviewed them—primarily because it seems like a deliberate attempt on GE’s part to deceive—to make us think they care about an issue by posting pretty visualizations that, in reality, say nothing. GE and David McCandless should be ashamed! After reviewing these visualizations, it seems clear that GE doesn’t really care about the issue, they just want us to think that they do.
Daniel Amitay recorded the used passcode for his app big brother camera security. like this he recorded 204,508 passcodes. the experiment shows, that the Top ten iPhone passcodes are 1234, 0000, 2580, 1111, 5555, 5683, 0852, 2222, 1212, 1998 and that they represent 15% of all used passcodes.
Interesting as well that it seems to be an exponential curve. would love to dig a bit deeper in this dataset. to find out more of Amitay's conclusions (and why 5683 ist so popular) visit his blog.
Explore the gender-differences on twitter with tweetolife.
Geschlechtsunterschiede bei Twitter? Forschen sie selbst:
Man sieht auch, welche Wörter normalerweise mit dem gesuchten zusammen gebraucht werden. Für "romantic" ist das meistassozierte Wort bei Männern "rival" (Rivale, Gegner), bei Frauen "date". Nichts Neues, es macht aber alleweil Spass, sich von einer Webseite ein paar Klischees bestätigen zu lassen.
(P.S. user92407 scheint besonders romantisch zu sein – oder Opfer einer romantischen Stalkerin)
Das Ganze kann auch nach dem Zeitpunkt des Tweets visualisiert werden und siehe da: Morgens um 8 wird "work" am häufigsten in Verbindung mit "verschlafen" gebraucht...
The visualization depicts the number of homes and vacant homes for all the counties for each state. The size of the outer bubble represents the total number of homes, the size of the inner bubble represents the number of vacant homes. The y-axis shows the population size (on a logarithmic scale) and the x-axis of the bubbels shows the number of vacant homes per population. Each bubble is also connected with a line to another axis: the population / home ratio. On the top right you can see some exact numbers for this data.
Nützliches Tool zur Datenbereinigung und -transformation.
Sehr praktisch: Die Transformationsschritte können als Code (Python und JavaScript) exportiert werden.
der Tweet-o-meter zeigt die Tweets pro Minute in den Metropolen der Welt. Wär interessanter als Linien-Diagramme mit Zeitverlauf.
Tweet-o-Meter is designed to mine data for later analysis relating to furthering our understanding of social and temporal dynamics for e-Social Science within the Twitter demographic. It is under development at CASA, University College London as part of a wider survey tool as part of the NeISS project in association with UrbanTick
Lokalisierungsdatendes iphones visualisieren und als CSV exportieren. Arbeiten an einer Lösung, die Daten auch nach dem letzten Update des iPhones noch über TimeMachine-BackUp zu finden
Also los: wenn die Daten schon gesammelt werden, dann macht was damit!
https://openpaths.cc/
speziell gut: man kann seine Daten auch der Forschung zur Verfügung stellen
Wenn die Daten schon gesammelt werden, sollte man was anständiges damit machen...:
With openpaths, you can preserve your iPhone or iPad's location information as well as visualize where you've been. You can even download your data in CSV and JSON format so you can remix it and use it in your own projects.
What's more, openpaths allows you to securely and anonymously donate your data to researchers who could use it to study mobility, transportation, land use, epidemiology, and overall make the world a better place.
