Animation Domination High-Def has a Captain America video of things that America is not so good at, relative to other countries. And they even cited their data source, the CIA World Factbook. How about that.

Media artist Nick Hardeman’s audio visualization app Bass Shapes was rejected by the Mac App Store because “it’s not useful.” So Hardeman released the software as a free OS X download instead. It’s a beauty.

The app takes in sound input from your microphone or an external audio source through Soundflower (also free), and the visuals come to life. Watching Bass Shapes, you’d swear that you were seeing a custom, hand-drawn animation that served as some kind of old-school-ish intro to an animated film. But you’d be wrong.

Download Bass Shapes and try it yourself.

Mike Bostock, who you might recognize from such things as Data-Driven Documents or the New York Times, writes on the value of visualizing algorithms for entertaining, teaching, learning, and debugging.

Algorithms are a fascinating use case for visualization. To visualize an algorithm, we don’t merely fit data to a chart; there is no primary dataset. Instead there are logical rules that describe behavior. This may be why algorithm visualizations are so unusual, as designers experiment with novel forms to better communicate. This is reason enough to study them.

But algorithms are also a reminder that visualization is more than a tool for finding patterns in data. Visualization leverages the human visual system to augment human intellect: we can use it to better understand these important abstract processes, and perhaps other things, too.

At the very least, you’ll have fun scrolling through the animated visuals that show how various algorithms work, but read the whole thing. It’s good.

Watch the notes rain down on those piano keys. Made with MIDItrail, a MIDI player with a 3-d visualization component. The program (and this video) has been around for a while, but it’s new to me.
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By Matthew Freeman. This is important.

Blitzortung is a community of volunteers who install inexpensive lightning sensors and transmit their data to a central server. In return, those who run the sensors have access to the network’s data. The map that runs on the site shows the data in near real-time, providing a view of lightning strikes around the world. Pretty neat that this exists.

DataShine Census provides a detailed view into United Kingdom 2011 census data. Population, housing, income, commute, and other variables are available.

The DataShine mapping platform is an output from an ESRC Future Research Leaders Project entitled “Big Open Data: Mining and Synthesis”. The overall project seeks promote and develop the use of large and open datasets amongst the social science community. A key part of this initiative is the visualisation of these data in new and informative ways to inspire new uses and generate insights. Phase one has been to create the mapping platform with data from the 2011 Census. The next phases will work on important issues such as representing the uncertainty inherent in many population datasets and also developing tools that will enable the synthesis of data across multiple sources.

They’re off to a good start.

Not sure where this is from, but feel that tingle in the back of your head? That’s the feeling of your mind blowing up.

Mirador, a collaborative effort led by Andrés Colubri from Fathom Information Design, is a tool that helps you find correlative patterns in datasets with a lot of variables and observations. It’s in the early stages of development, but is available to use and test on Windows and Mac. Colubri explains the process, from its early stages to its current iteration.

Although fields like Machine Learning and Bayesian Statistics have grown enormously in the past decades and offer techniques that allows the computer to infer predictive models from data, these techniques require careful calibration and overall supervision from the expert users who run these learning and inference algorithms. A key consideration is what variables to include in the inference process, since too few variables might result in a highly-biased model, while too many of them would lead to overfitting and large variance on new data (what is called the bias-variance dilemma.)

Leaving aside model building, an exploratory overview of the correlations in a dataset is also important in situations where one needs to quickly survey association patterns in order to understand ongoing processes, for example, the spread of an infectious disease or the relationship between individual behaviors and health indicators.

Download Mirador to try it for yourself.

Many of us aren’t aware of how one country compares to others or public policy that has been around for decades. How Wrong You Are is a simple quiz game by Moiz Syed and Juliusz Gonera that tests such knowledge.

How Wrong You Are is a collection of important questions that people are sometimes misinformed about. We poll you to measure how right — or how wrong — the public is about these important questions.

Every week, we will add a new question. These are all questions that we hope you already know. But if you don’t, don’t worry! You learned something. Share your results, successful or not. Chances are, if you didn’t know this question, other people might not, either.

Play the game here. At the very least, you’ll learn something new.

While we’re on the topic of NYC taxi data, Eric Fischer for Mapbox mapped all 187 million trips. Each observation contains the start and end location of a trip, so blue dots represent the former and orange represent the latter. My favorite bit is on the data collection artifacts, such as the map above.

The patterns at JFK and LaGuardia airports show interesting artifacts of the data collection process. Almost all of the trips there must have really begun or ended right at the terminals, but many of them are attributed to the roads leading to and from the airports, where the last good GPS fix must have occurred.

See also the New York Times animated map from several years ago that shows taxi activity during days of the week.

Based on data compiled from a combination of military records, Defense Department records, and drone manufacturers, Emily Chow, Alberto Cuadra and Craig Whitlock for the Washington Post provide a quick view into drone crashes.

More than 400 large U.S. military drones crashed in major accidents worldwide between Sept. 11, 2001, and December 2013. By reviewing military investigative reports and other records, The Washington Post was able to identify 194 drone crashes that fell into the most severe category: Class A accidents that destroyed the aircraft or caused (under current standards) at least $2 million in damage.

The top row represents where a drone crashed, the second row who owns it, and the third tells the type. Mouse over any of the tick marks, and you get details for the corresponding crash.

Through a Freedom of Information request Chris Whong received and eventually released NYC taxi logs starting in 2013 (about 173 million trips). Vijay Pandurangan looked at the data a little closer and deanonymized the logs to link hashed license numbers to the driver names. It didn’t take much to do it. Pandurangan described the process and lessons organizations can learn when they release data.

Someone on Reddit pointed out that one specific driver seemed to be doing an incredible amount of business. When faced with anomalous data like that, it’s good practice to weed out data error before jumping to conclusions about cheating taxi drivers. Also, I couldn’t shake the feeling that there was something about that encoded id number: “CFCD208495D565EF66E7DFF9F98764DA.” After a little bit of poking around, I realised that that code is actually the MD5 hash of the character ‘0’. This proved my suspicion that this was actually a data collection error, but also made me immediately realise that the entire anonymization process was flawed and could easily be reversed.

He also provided the code snippet he used to do it.

Gregor Aisch for the New York Times explored how the soccer clubs that play all year connect the national teams in this year’s World Cup.

The best national teams come together every four years, but the global tournament is mostly a remix of the professional leagues that are in season most of the time. Three out of every four World Cup players play in Europe, and the top clubs like Barcelona, Bayern Munich and Manchester United have players from one end of the globe to the other.

My browser buckled a few times as I scrolled, but even without smooth transitions, it’s an interesting dive into player connections.

Based on a column by Tim McEown, the animated video Modern Love by Freddy Arenas elegantly illustrates a relationship.
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Sir Francis Galton, creator of the concept of correlation and regression toward the mean, wrote a letter to the editor of Nature in 1906 on the best way to cut a circular cake. The result is moist cake with every slice, even if you eat it days later. Alex Bellos for Numberphile demonstrates in the video below.

I don’t get it. I typically just eat a full cake in one sitting with a really big fork.

According to the U.S. census, the mean center of the population shifted west every decade since 1790. They show the change in a simple animation.

The mean center of population, traditionally referred to as simply the center of population, is provided for the 2010 Census and each census since 1790. In 2010, the mean center of population was located at 37°31’03” North latitude, 92°10’23” West longitude in Texas County, Missouri, 2.7 miles northeast of Plato, Missouri.

The inclination might be to read this as people moving west, which is partially true, but don’t forget immigration increasing the populations too.

I tracked steps and sleep for the past month wearing the Jawbone UP24 wristband. It works. It’s straightforward to use. It’s not perfect. I’m gonna keep wearing it.

Jawbone UP24 with SD card for scale
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Visits, a research project by Alice Thudt, Dominikus Baur, and Sheelagh Carpendale from the University of Calgary, is an exploration of your personal location history.

With visits you can browse your location histories and explore your trips and travels. Our unique map timeline visualization shows the places you have visited and how long you have stayed there. Add photos from Flickr to your visits and share your journey with your family and friends!

Visits works with geo-tagged Flickr albums, data from Openpaths and Google Location Histories. It runs locally in your browser, so no sensitive data is uploaded to our servers. When you share your history, it is up to you how much detail visits reveals and what remains private.

Simply plug your data in and explore short trips or even better, look at long-term location memories. The focus is less on analytics and numbers and more on helping you remember where you’ve been. [Thanks, Dominikus]