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www.connectedaction.net/2009/03/02/facebook-social-networ...
Here is a good example of an application of Bernie Hogan’s Facebook edgelist extractor. Alan Shussman used it on his own Facebook account and generated the following image: Alan Shussman's personal Facebook egonetwork visualization Alan Shussman's personal Facebook egonetwork visualization Alan used the NetworkX tool and python to build this image of his sub-groups in Facebook. It does work nicely to highlight the life-stage clusters of relationships that mostly stay inward focused, each school or work experience is a set of relationships that mostly link to themselves.
This image created for BU's Deep Vision Display Wall shows a patient's heart (red), an implanted defibrillator (green) and multicolored bands simulating the electric field distribution during a defibrillating shock. Image: Visualization by Raymond Gasser and Daniel Mocanu. Courtesy of the Scientific Computing and Visualization Group, Boston University.
Inspired by the awesome Charting The Beatles project (http://mikemake.com/#72772/Charting-the-Beatles) I made a visualization based on the frequency of initial letters in song titles from the 14 stereo albums.
I is the most frequent, with 32. J is the only letter with exactly one song.
Following the lead of talented wfu designer Caryn Kesler’s missed connections infographic of San Francisco, I made one for New Orleans. Full pdf here.
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This shot from below makes the statue take up a majority of the screen space and leaves the viewer with a sense of awe in regards of its power and massiveness.
flowingdata.com/2008/03/12/17-ways-to-visualize-the-twitt...
and
www.visualcomplexity.com/vc/project_details.cfm?id=594&am...
A way to visualize the Twitter universe. More visualizations on this website.
Akshay Java, from ebiquity group, used the Large Graph Layout (LGL) tool to visualize a large social network on Twitter. The top graph shown here was built using contacts from about 25,000 users. Notice that there is a link connecting two users if either one has the other as a friend and hence it is an undirected graph (of about 250,000 edges). Compare this to the bottom graph that is constructed using only users who are mutually acquainted. i.e. A knows B and also B knows A. As Akshay reveals in his post: "I find that visualizing such large graphs is quite a challenge and to glean meaningful information from it is even more difficult". However, he goes further in explaining that some insights can still be gained from this project. Akshay points out that a number of users seem to be trying to win a popularity contest of some sort, while a number of bloggers and (perhaps fake) celebrity profiles have a huge fan following in Twitter. He also mentions how the two graphs look very different on account of the fact that users with public profiles get a lot of followers whom they might not really know and would hence never add them as an acquaintance. But to really understand what the differences are one would need to look at the community structure and properties of the two graphs. ebiquity group has also explored the Twitter API in other projects [1] [2] in order to get a better understanding of the microblogging trend.
Julia Kaganskiy (@juliaxgulia) organizes Arts, Culture and Technology meetups in NYC. This event on 27th April 2010 was on Data Mining & Visualization: www.meetup.com/Arts-Culture-and-Technology/calendar/13144...
Comparison of differences in two versions of the same manga page. Only areas of the page which are different are shown -- most of the original manga comic book art stayed the same between the two, and was screened out.
* On the left is the official publisher translation.
* On the right is the original unofficial scanlation produced by fans.
* In the center is an interference pattern between the two.
Some seemingly similar image elements appear because they were unchanged in the scanlation, but were later retouched by the publisher -- often adding color screening or line detail. Others are differences created by the difference in texture between the scanlation (typically low-quality scans of cheap newsprint print runs) and the higher print and scan resolution of foreign trade paperbacks. The kinds of texture that trigger a difference are often consistent in a given work -- for example, only the fine lines use in rushing action backgrounds, the texture on clothing, and lines around close-up eyes.
from upper-left to bottom right:
start in Safeway parking lot. Slower uphill, then downhill. Slow to make a turn. Slow to pass through intersection, and stop at stop light. Quickly downhill, then slow slightly to move up the bike trail, then stop at Frontseat offices.
The renowned science & technology portal Seed (US) is collaborating with visualizing.org on a collection of outstanding examples of how creatively data can be visually processed to generate really impressive BIG PICTURES.
credit: visualizing.org
Julia Kaganskiy (@juliaxgulia) organizes Arts, Culture and Technology meetups in NYC. This event on 27th April 2010 was on Data Mining & Visualization: www.meetup.com/Arts-Culture-and-Technology/calendar/13144...
created by this applet: www.aharef.info/static/htmlgraph/
what the colors mean:
blue: for links (the A tag)
red: for tables (TABLE, TR and TD tags)
green: for the DIV tag
violet: for images (the IMG tag)
yellow: for forms (FORM, INPUT, TEXTAREA, SELECT and OPTION tags)
orange: for linebreaks and blockquotes (BR, P, and BLOCKQUOTE tags)
black: the HTML tag, the root node
gray: all other tags
energy monitoring lamp; if you are using energy efficiently in your home, it fans out, gives more light and is far more attractive. It shrinks if you overuse your appliances and electronic devices.
Revolve's photo exhibition launch at the Halles Saint Gery in downtown Brussels on July 1, 2014. In the presence of the City of Brussels, the Brussels Environment Agency (IBGE) and REScoop.
First Hacks/Hackers Meetup held at Atherton Studio at HPR. Great presentations by Ben Trevino, Jared Kuroiwa and Misa Maruyama.
Julia Kaganskiy (@juliaxgulia) organizes Arts, Culture and Technology meetups in NYC. This event on 27th April 2010 was on Data Mining & Visualization: www.meetup.com/Arts-Culture-and-Technology/calendar/13144...
iSGTW story | Photo courtesy of Argonne National Laboratory.
Scientists Folker Meyer and Elizabeth Glass analyze species and metabolic diversity from soil samples using MG-RAST (Metagenome Rapid Annotation using Subsystem Technology) on an active mural display.
Photographs by Katie Holland Lewis.
Process of Accumulation. pins, pencil
The artist Katie Lewis methodically records, documents and visualizes minute details of her body in everyday life. Every trace is accumulated into large data installation often using simple material such as pins and paper.
katiehollandlewis.com