is a visualization expert with more than a decade's experience working in both industry and academia.
Visualization as I see it.
The web of lawsuits filed for infringement of mobile technology patents grows more tangled with each passing month. Several infographics have been published to try to help make sense of precisely who is suing whom.
1. Nick Bilton published the graphic below in the New York Times summarising the situation as it stood in March 2010. The chart shows the various protagonists involved but some of the links correspond to multiple lawsuits, which isn’t represented by this graphic.
2. This was followed in October 2010 by a chart published in The Guardian. It’s similar to Nick Bilton’s but distinguishes between lawsuits that are in progress vs. concluded. The choice of colours is garish and kind of unnecessary as they don’t represent anything. The layout could also be improved to reduce the number of crossed links.
3. Dave McCandless reworked The Guardian’s infographic to produce the following infographic for his Information is Beautiful blog. Dave uses company logos which improves recognition of the warring parties, scales them according to their revenues and colours them to represent growing or shrinking incomes. He also annotates the links with information about the lawsuits and their dollar values.
4. Design Language News tried a circular layout to improve clarity.
5. Florian Mueller focussed on the growing number of patent suits ranged against device-makers using the Android operating system.
6. Most recently Harry McCracken published the following “cheat sheet” in Technologizer.
I think Dave McCandless’ infographic is the best of the bunch as it manages to present the most information about the mobile patent wars. Harry McCracken’s cheat sheet is a good compact representation of the situation – being a symmetric sparse matrix it could probably be compressed even further.
What’s your opinion? Please leave a comment below.
If you spot any more such charts then please bring them to my attention.
I’m an avid follower of Formula 1 motor sport, so when I saw the “stack flow” visualization shown above on the Impure blog I was intrigued. The stack flow shows the 590 most populated cities sorted column-by-column according to their populations every five years between 1950 and 2010, and projected to 2025. This kind of data is similar to the lap-by-lap placings of drivers in a (F1) motor race. Shown below is the lap chart for the 2011 Chinese F1 Grand Prix.
As static displays of data both the stack flow and lap chart can be difficult to comprehend. Did you notice Mark Webber’s amazing drive from 18th on the grid to 3rd at the chequered flag? How about Dhaka’s rise from near the bottom of the rankings in 1950 to the world’s fourth largest city by 2025?
I didn’t think so. I knew about the former so could look for it but was able to find the latter fairly easily because the stack flow is interactive. Brushing a city highlights it in the visualization making it easier to see how its population rank changes over time (see examples below). This interactive element is just what is needed to make lap charts more comprehensible.
[ via Visualizing.org ]
As a computer science undergraduate I spent many hours learning various sorting algorithms. Pseudo-code and static diagrams were used to illustrate the implementation and processing of these algorithms. Now (almost a quarter of a century later!) it seems algorithms are still an important part of computer science education. What’s changed is that Web 2.0 technologies are being used to aid understanding.
The video clip shown above represents the Bubble Sort algorithm realized in the style of a Hungarian folk dance. Whether this represents the best visualization of sorting algorithms to date, I’m not so sure, but it’s certainly the most bizarre I’ve seen and is strangely compelling. The clips were created at Sapientia University, Romania and choregraphed by Füzesi Albert.
Below are several other folk dances choreographed to implement various sorting algorithms.
Jason Rowe has produced this stunning visual summary of every candidate exoplanet host star discovered using the Kepler space telescope. Each star’s transiting planet is shown in silhouette – note that some stars have multiple planets. The stars have been scaled according to their size, and coloured according to how they would appear when viewed by eye from outside Earth’s atmosphere (the colours have been saturated for easy viewing). The largest star is 6.1 times the size of our Sun, which is shown for reference (with Jupiter) on its own in the second row.
1. A Radiation Levels chart translated from Japanese to English by Michael Gakuran
2. An interactive Anual Radiation Dose “chart” (more of a web-form) from the American Nuclear Society that allows you to estimate your annual radiation dosage. Mine is just over 4 milliSieverts per annum.
The ESA has just announced that their GOCE satellite has collected enough data “to map the Earth’s gravity with unrivalled precision”. Accompanying the press release was the animation (shown above) of the “geoid” model derived from the new data set.
Unfortunately, the animation lacks the most fundamental feature required of all visualizations: a key to help us make sense of what it actually shows!
The text accompanying the animation sheds a little light on the visualization:
The geoid is the surface of an ideal global ocean in the absence of tides and currents, shaped only by gravity.
If I were to hazard a guess, I’d suggest that the visualization shows the earth’s crust, with a height field and colour map applied to it. The latter two possibly encode the strength of the gravitational field and/or the height of the idealized global ocean (the latter being derived from the former).
The point is I shouldn’t have to guess! This important data deserves better treatment.
If you know what the visualization encodes then please leave a comment below.
[ Update: 8 April, 2011 ]
I had this quick response from the ESA regarding interpreting the Geoid visualization:
The colours in the image represent deviations in height (–100 m to +100 m) from an ideal geoid. The blue colours represent low values and the reds/yellows represent high values.
It is claimed that software freedom activist Richard Stallman famously doesn’t own a mobile phone, claiming they present serious privacy concerns. Just how much information do mobile telephone companies obtain from their subscribers? Quite a lot as it turns out.
German Green party politician Malte Spitze sued his mobile phone service provider Deutsche Telekom, who released to him six months worth of his mobile phone data. Spitze then made this available to ZEIT ONLINE, who combined the geolocation data along with Spitze’s Twitter feed, blog postings and other publicly available online information to create an impressive tracker application. The application visualizes Spitze’s movements, activities, phone and SMS messages and web-surfing for the six months from August 31, 2009 – February 28. 2010.
ZEIT ONLINE’s visualization reveals the wealth and fidelity of personal data that our mobile phone carriers collect and retain. Spitze’s data set is also publicly available if you’d like to scrutinise it further or produce your own visualization.