During a two-week visualization course, Momo Miyazaki, Manas Karambelkar, and Kenneth Aleksander Robertsen imagined what a body of text would be without the the silent letters in silenc.

silenc is based on the concept of the find-and-replace command. This function is applied to a body of text using a database of rules. The silenc database is constructed from hundreds of rules and exceptions composed from known guidelines for “un”pronunciation. Processing code marks up the silent letters and GREP commands format the text.

So nothing too fancy on the analysis side, but the experimental views are kinda interesting to see. [via @alexislloyd]

New Scientist mapped global temperature change based on a NASA GISTEMP analysis.

The graphs and maps all show changes relative to average temperatures for the three decades from 1951 to 1980, the earliest period for which there was sufficiently good coverage for comparison. This gives a consistent view of climate change across the globe. To put these numbers in context, the NASA team estimates that the global average temperature for the 1951-1980 baseline period was about 14 °C.

The more red an area the greater the increase was estimated to be, relative to estimates for 1951 to 1980 (especially noticeable in the Northern Hemisphere).

The most interesting part is when you compare all the way back to to the 19th century when it was much cooler. You can also click on locations for a time series of five-year averages. [Thanks, Peter]

When it was time to settle down with the right man, Amy Webb joined two dating sites, created a profile, and went on some horrible dates. Her solution was to create fake male profiles and then scrape and analyze data to find out how she could improve her chances.

Posing as these men, I spent a month using JDate. I interacted with 96 women, cataloging how they behaved and presented themselves online and scraping data from their profiles (such as the language they used or the number of hours they waited before emailing back one of my profiles). Wanting to learn everything I could about my competition, I kept a detailed database, and I recorded which female profiles were popular. While JDate doesn’t publicly release its algorithms, at the time of my experiment I observed that the more popular profiles come up higher in search results, allowing one to get a quick-and-dirty ranking of who’s hot (or not). I quickly realized that the popular women seemed to know something I didn’t; they were clearly attracting the sort of smart, attractive professionals who had been ignoring my profile. Being hypercompetitive, I wasn’t about to let some bubblegum-popping blonde steal the neurotic Jewish doctor of my mother’s dreams.

Basically, she pulled an OKCupid for herself. It worked.

Jeff Clark took a detailed look at Victor Hugo’s Les Miserables via character mentions, word connections, and word usage. The above is character mentions with color showing sentiment. Red means negative, and blue positive.

Characters are listed from top to bottom in their order of appearance. The horizontal space is segmented into the 5 volumes of the novel. Each volume is subdivided further with a faint line indicating the various books and, finally, small rectangles indicate the chapters within the books. In the 5 volumes there are a total of 48 books and 365 chapters. The height of the small rectangles indicate how frequently that character is mentioned in that particular chapter.

There’s a good amount of blue towards the end, when everyone decides everyone else isn’t so bad.

See the full version and other views here.

The Lotr Project breaks down the thought process in the magical mind.

Run for your lives. The red concentric circles on the green squiggly are headed your way. From The Onion:

[via @civilstat]

By Francisco Javier Aragón Artacho, “This is a walk made out of the first 100 billion digits of pi in base 4 with the following rules for the steps: 0 right, 1 up, 2 left, 3 down.” [via]

I made a graphic a while back that showed traffic fatalities over a year. John Nelson extended on that, pulling five years of data and subsetting by some factors: alcohol, weather, and if a pedestrian was involved. And he aggregated by time of day and day of week instead of calendar dates.
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