Data Driven Digest for February 6

Each Friday we share some favorite reporting on, and examples of, data driven visualizations and embedded analytics that came onto our radar in the past week.

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Hot shot: It’s been fascinating to watch Anthony Davis evolve since he entered the NBA in 2012. In a great story on Grantland, Kirk Goldsberry dissects and admires that evolution, saying Davis “has turned the increasingly out-of-style territory within the 3-point arc into his personal basketball laboratory.” He also says Davis’ favorite shot is not a flashy dunk nor a dramatic three-pointer, but a simple jump shot from above the free throw line. Davis is also improving his old-school bank shot. Goldsberry backs up these observations with the terrific graphic above. Actually, we’ve posted only one of two graphics; the other one shows Davis’ shooting in his rookie season. Click through to see and compare them both; the difference between the two tells the story.


Dry ideas: As other parts of the U.S. dig out from severe winter storms, Californians hope for rain this weekend. But we in the Golden State are constantly reminded that an entire winter of heavy rains won’t crack our persistent drought. The drought’s effects are felt far beyond the state’s borders: the USDA says “major impacts from the drought in California have the potential to result in food price inflation above the 25 year historical average of 2.8 percent.” On the map above, NASA’s Earth Observatory plots how the drought has affected California farmland; comparing images of active and idle farmland from 2011 and 2014, you see how much acreage has been taken out of production due to lack of irrigation water.



On the move: Randy Olson is back with another application of data science to issues that, frankly, aren’t very important. We highlighted his chart of gender-neutral names a couple of months ago. This time Olson has focused his talents on a profound question: Where’s Waldo? Spinning off from a 2013 article on Slate, Olson was determined to “pull out every machine learning trick in my tool box to compute the optimal search strategy for finding Waldo.” (Yes, he is aware that this is silly, but it’s his time to waste.)  The kernel density estimate chart above is the result of Olson’s calculations (the dotted line represents the spine of a Where’s Waldo spread), and he’s published it along with an optimal search path. We admire how Olson explains his questions, methods and conclusions, and we’re relieved to know he also spends time on bigger issues.

Do you have a favorite or trending resource on embedded analytics and data visualization? Share it with the readers of the Actuate blog. Submit ideas to or add a comment below. Subscribe (at left) and we’ll email you when new entries are posted.

Recent Data Driven Digests:

January 30: World population, Super Bowl geography, big game commercials

January 23: SOTU tweets, Moore’s Law, Big Data roles

January 16: Tallest buildings, Ohio State’s Elo rating, airport efficiency


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