From businesses to demographics, there’s data for just about anywhere you are. Sitegeist, a mobile application by the Sunlight Foundation, puts the sources into perspective.

Sitegeist is a mobile application that helps you to learn more about your surroundings in seconds. Drawing on publicly available information, the app presents solid data in a simple at-a-glance format to help you tap into the pulse of your location. From demographics about people and housing to the latest popular spots or weather, Sitegeist presents localized information visually so you can get back to enjoying the neighborhood. The application draws on free APIs such as the U.S. Census, Yelp! and others to showcase what’s possible with access to data.

Available for free on both Android and iPhone. Data just a flick and a scroll away. [Thanks, Nicko]

Thessaly La Force, with illustrator Jane Mount, recently published My Ideal Bookshelf, which is a look into the books that some people of interest, including Judd Apatow, Chuck Klosterman, and Tony Hawk, would like to have on their ideal bookshelf. La Force’s boyfriend took a more data-centric look at the collections.

In the network above, each node is a person who listed their ideal books, and connections represent people who named the same books. Those in the center of the network had more book similarities than those on the edges. For example, James Franco named a ton of books and as you might expect has a bunch of connections. [via @shiffman]

By now, everyone’s heard of Moneyball. Applying statistics to baseball to build the best team for the buck. Naturally, there’s a lot of interest these days in applying the same data-based philosophy to other sports. Jennifer Fewell and Dieter Armbruster used network analysis to model gameplay in basketball.

To analyze basketball plays, Fewell and Armbruster used a technique called network analysis, which turns teammates into nodes and exchanges — passes — into paths. From there, they created a flowchart of sorts that showed ball movement, mapping game progression pass by pass: Every time one player sent the ball to another, the flowchart lines accumulated, creating larger and larger and arrows.

Using data from the 2010 playoffs, Fewell and Armbruster’s team mapped the ball movement of every play. Using the most frequent transactions — the inbound pass to shot-on-basket — they analyzed the typical paths the ball took around the court.

The challenge with basketball is that play is continuous, whereas baseball events are discrete, so you can’t apply the same methods. But if you can model the game properly, you know where to optimize and areas that need work.

As 2013 nears, let the recaps, reviews, and best ofs begin. Twitter put up their 2012 year in review of top tweets, trends, and such, which is mostly pictures and lists, but in collaboration with Vizify, they also have a section to visualize your own tweets. Click on the “View year on Twitter” button in the top right. Here’s mine, for example. (Surprise, I mention maps, data, and charts often.)

It’s a word frequency chart that shows usage over the year. Scroll left to right or mouse over bubbles to see specific tweets. Mostly, it’s just fun to look back. [Thanks, Todd]

This one’s for you Game of Thrones fans and aficionados. Jerome Cukier visualized groups of people, from Lannisters to Starks, and kills throughout the books. Each circle represents a character and is sized by number of appearances. Color represents status, and connecting lines are killer-killee relationships (aw, so sweet). The best part is that this all plays out over time.

From machine learning to data mining. From statistics to probability. A lot of it seems similar, so what are the differences? Statistician William Briggs explains in an FAQ.

What’s the difference between machine learning, deep learning, big data, statistics, decision & risk analysis, probability, fuzzy logic, and all the rest?

None, except for terminology, specific goals, and culture. They are all branches of probability, which is to say the understanding and sometime quantification of uncertainty. Probability itself is an extension of logic.

I was surprised he didn’t throw data science into the mix, but you could and the document would pretty much be the same.

Andrew Barr and Richard Johnson for the National Post took a detailed look at the who, what, and when of Walking Dead kills.

While AMC lets The Walking Dead gang take a short mid-season break — the Post’s Andrew Barr
and Richard Johnson look at a few of the key statistics of two-and-a-half season’s worth of undead mayhem. They find noteworthy — the gradual increase in the body count, the increasingly creative means of Zombie dispatch, and the fact that every character seems to have developed a clear enjoyment for putting the ambulatory cadavers down for good.

They also included weapons used, ranging from handgun to tree branch. See the full version here. Somewhere there’s a piece of paper with a ton of tally marks on it.

[Thanks, Thomas]

Something changed. [via]

James Grady from Fathom Information Design had a look at the family tree of All in the Family, a popular television from the 1970s:

All in the Family was the origin of seven spin-off shows that aired between the early ’70s and the mid-’90s: Maude, Good Times, The Jeffersons, Checking In, Archie Bunker’s Place, Gloria, and 704 Hauser.

In tribute to nostalgia, the end of fall and its beautiful colors, and my fascination with retro TV shows, I’ve created All in the Family Tree, an interactive visualization of all the characters from each of the eight shows listed above. Each character is represented by a leaf and each show is indicated by a separate color. A branch line connects a character’s crossover from original show to spin-off and vice versa.

It’s a charming piece that’s sure to bring back good memories for anyone who watched the shows. I was too young to appreciate them at the time, and all I can remember is the opening sequence of The Jeffersons. I think they were moving on up. To the east side.

Max Fisher for the Washington Post mapped country emotion ratings, based on the results of a recent Gallup study. Singapore was ranked least emotional, whereas the Philippines was ranked most emotional. The United States was also relatively high. From Gallup:

While higher incomes may improve people’s emotional wellbeing, they can only do so to a certain extent. In the United States, for example, Nobel Prize-winning economist Daniel Kahneman and Princeton economist Angus Deaton found that after individuals make $75,000 annually, additional income will have little meaningful effect on how they experience their lives. Consider this finding in the context of Singapore, a country with one of the lowest unemployment rates and highest GDP per capita rates in the world, but a place where residents barely experience any positive emotions. This research shows that it will take more than higher incomes to increase positive emotions or decrease negative emotions. Singapore leadership needs to consider strategies that lie outside of the traditional confines of classic economics and would be well-advised to include wellbeing in its overall strategies if it is going to further improve the lives of its citizenry.

I’m curious about what we’re seeing here though. The research infers wellbeing, but the survey was done by phone and face-to-face. Did Americans call overseas, or did residents call other citizens? The former might be kind of weird for some.

More importantly though, they asked questions like “Did you smile or laugh a lot yesterday?” and “Did you experience enjoyment?” Some cultures just don’t express emotions, but it doesn’t mean they don’t feel them. (Read as: I’m not a robot! I have feelings, too!)

[Thanks, John]