The Mercator projection can be useful for giving directions, but when it comes to world maps, the projection doesn’t hold up well as you move far north and south. By how much? Give this puzzle game a try and match the red boundaries to their respective countries.

Carlos Scheidegger and Kenny Shirley, along with Chris Volinsky, visualized Major League Baseball Hall of Fame voting, from the first class in 1936 (which included Babe Ruth) up to present.

All a fan can do is accept that Baseball Hall of Fame voting, conducted by the Baseball Writers Association of America (BBWAA), is a phenomenon unto itself. If we can’t understand baseball Hall of Fame voting, though, maybe the next best thing is visualizing the data behind it. The set of interactive plots on this webpage is our attempt to do that. We were especially interested in two things: (1) viewing the trajectories of BBWAA vote percentage by year for different players throughout history, and (2) simultaneously viewing the career statistics of these players, to help find patterns and explain their trajectories (or to reassure ourselves that the writers really are crazy).

The interactive is on the analysis side of the spectrum, so you might be a bit lost if you don’t know a lick about baseball. However, if your’re a baseball fan, there’s a lot to play around with and dimensions to poke around at, as you can filter on pretty much all player stats such as home run count, batting average, and innings played. At the very least, you’re getting a peek at how statisticians pick and prod at their data.

Start at the examples section for quick direction. I eventually found myself looking for downward trajectories. Poor Mark McGwire. [Thanks, Chris]

As the Super Bowl draws near, Facebook took a look at football fandom across the country.

The National Football League is one of the most popular sports in America with some incredibly devoted fans. At Facebook we have about 35 million account holders in the United States who have Liked a page for one of the 32 teams in the league, representing one of the most comprehensive samples of sports fanship ever collected. Put another way, more than 1 in 10 Americans have declared their support for an NFL team on Facebook.

It’s a fairly straightforward geographic breakdown based on the most liked team in each county, as shown above. So you can kind of see where rivalries come from.
Read More

We’ve all seen rain maps for a sliver of time. Screw that. I want to see the total amount of rainfall over a ten-year period. Bill Wheaton did just that in the video above, showing cumulative rainfall between 1960 and 1970. The cool part is that you see mountains appear, but they’re not actually mapped.

The hillshaded terrain (the growing hills and mountains) is based on the rainfall data, not on actual physical topography. In other words, hills and mountains are formed by the rainfall distribution itself and grow as the accumulated precipitation grows. High mountains and sharp edges occur where the distribution of precipitation varies substantially across short distances. Wide, broad plains and low hills are formed when the distribution of rainfall is relatively even across the landscape.

See also Wheaton’s video that shows four years of rain straight up.

Is there more recent data? It could be an interesting complement to the drought maps we saw a few months ago. [Thanks, Bill]

I’m almost certain this relationship is significant. Side note: Is there a meaningless-correlations tumblr yet? [via]

Arthur Buxton plotted the most common colors of Penguin Publishing science fiction colors and arranged them over time. Also available in print.

I wonder if there’s a good way to show connections between the titles or the different covers for each title.

How wealthy are the richest people in the world? How do they compare to each other, and how does their net worth change over time? Bloomberg just put up an interactive tool to answer such questions, and it’s updated daily with new data.
Read More

For the past year, I’ve been working on Data Points: Visualization that Means Something, and you can pre-order it now.

Visualization has grown a lot in the 5-something years I’ve written for FlowingData. It’s not just a tool for analysis anymore. Visualization is a way to express data, and it comes in the form of information graphics, entertainment, everyday interfaces, data art, and yeah, tools, too. Your approach to data and visualization changes based on application.

But even with all these (awesome) new applications, there’s a constant across all of them: the data.

Data Points starts here, and takes you through the process of understanding data, representing it, exploring it, and designing for different applications. Whereas Visualize This was about getting your feet wet with lots of code examples, Data Points is code-independent and is a perfect complement that helps you understand and allow others to understand data better, which is sorta the whole point.

The manuscript is written, the 240 graphics (by me and many of your favorites) are set, and I’m really happy with how it turned out.

It’ll officially be out late March or early April. Crazy, nerve-racking, and exciting all at the same time.

More details to come. Until then: pre-order the book today.

Thanks to Sha Hwang, you can now siltscan videos on YouTube and Vimeo with an easy-to-use bookmarklet. Just go to the video and click. In case you’re unfamiliar with the technique, here’s a description from Golan Levin:

Slitscan imaging techniques are used to create static images of time-based phenomena. In traditional film photography, slit scan images are created by exposing film as it slides past a slit-shaped aperture. In the digital realm, thin slices are extracted from a sequence of video frames, and concatenated into a new image.

Be sure to switch over to HTML5 on YouTube or Vimeo first. The bookmarklet won’t work with Flash.

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.
Read More

The Chronicle of Higher Education has a look at the percentage of academic papers published by women, over the past five centuries.

The articles and authors described in this data were drawn from the corpus of JSTOR, a digital archive of scholarly papers, by researchers at the Eigenfactor Project at the University of Washington. About two million articles, representing 1765 fields and sub-fields, were examined, spanning a period from 1665 to 2011. The data are presented here for three time periods, the latest one ending in 2010, and a view that combines all periods.

Percentage of female authors is on the horizontal, and each bubble is a subfield sized by total number of authors. The graphic starts with publishing for all years, but be sure to click on the tabs for each time span to see changes.

The data is based on the archive of about two million articles from JSTOR, and a hierarchical map equation method is used to determine subfields.

The gender classification they used for names seems like it could be nifty for some applications. Gender is inferred by comparing names against the ones kept by the U.S. Social Security Administration, which includes gender. If a name was used for female at least 95 percent of the time, it was classified as a female name, and the same was done with male. Anything ambiguous was not included in the study.

From the 1932 Atlas of the Historical Geography of the United States, these maps paint the picture of transportation in the 1800s. Each line represents how far one could travel in some amount of time, starting from New York. For example, it took about a month to get to Louisiana.
Read More