I’m not a music downloading monster like some, so I personally haven’t had any problems organizing and finding my music. However, for those who are downloading music every day (legally, I hope), I can imagine your music collection is getting quite out of hand. You probably can’t even remember what songs and albums you’ve downloaded over the past two years. What’s that High School Musical album doing there?

That’s why this tool is in development. I haven’t tried it out, but from the screenshots, it looks like there is potential. Although it looks like the screen can get cluttered very quickly, and with too many songs, you might just end up with a big bubble cloud. If that actually is a problem, it kind of defeats the tool’s purpose since I don’t really care about visualizing only 20 songs. But like I said, I haven’t tried it.

In the usual fashion that we’ve come to expect from Stamen Design, Digg Pics shows us what pictures are being dugg as well as provides an opportunity to discover new pictures. As with its Digg Labs siblings, Digg Pics offers three streams — popular, newly submitted, and all activity.

I always like to read posts that discuss the experimental phases and how a viz came to whatever it is; it’s kind of like when you know the history of a piece of art, you can appreciate it more. Eric goes into the design process at the Stamen blog. There’s screenshots of Stamen’s experimental layouts, and from what I see on Digg, I’d say everything came together quite nicely.

The picture streams are split up into Digg categories where the number of times a picture is repeated represents the number of times the picture was recently dugg. The display is clean and smooth, and of course the interaction is quite nice (and useful).

Another good one, Stamen!

Andrew Vande Moere writes in his 2005 paper Form Follows Data:

[W]e can perceive a current trend in portable input and output devices that trace, store and make users aware of a rich set of informational sources. So-called ubiquitous computing is moving into the direction of location-based information awareness, enabling users to both access and author dynamic datasets based upon a geographical context through electronic communication media.

With this growing trend of streaming data in mind, Andrew goes on to say

Building automation services enable spaces to react to dynamic, physical conditions or external data sources in real time. Currently, these interactions are programmed by engineers, and imply simple action-reaction rules, such as the control of lights, security or climate control: what would be possible if these tools are offered to designers, concerned with the emotional experience of people?

If you’re an engineer, you might be wondering, “Hey! Why can’t I design ambient systems? I care about emotional experience too. Somewhat. Sort of.” As someone who majored in electrical engineering and computer science and still works with a lot of engineer types, I will tell you why. Engineers are generally not very good at the visual display of data. To engineers, the most beautiful part of a data visualization installation might be the hardware, elegant code, or the hours spent tweaking the system’s logic. Engineers are fascinated with the guts of the system.

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I love to look at how the current week’s movies are doing at the box office. I’m not really sure what it is. I think it’s kind of like a gauge for what good movies are out; or maybe I’m just constantly amazed by the millions of dollars that movies make; or I think it could be my addiction to numbers?

Something that always strikes me as interesting is how movies are always breaking records at the box office. So and so movie just broke the record for most money made over a single weekend or a month or a long holiday weekend or for a Thursday when there was at least 2 inches of rain and a dog skateboarded two miles.

I took a look at the 25 highest grossing American films, adjusted for inflation. I’m so tired of hearing statistics for money comparisons over time that don’t adjust for inflation. Wow, gasoline prices are at an all time high. Well guess what — so are milk, bread, burgers, televisions, light bulbs, paper, cars, and everything else on the planet. Sorry, slight tangent.

Download the Wallpaper

As an early birthday gift to you, here are my results in wallpaper form:

1024 x 768

1280 x 1024

1440 x 900

The movie titles are color coded for genre and the higher grossing films are in a larger font. Drama and action/adventure clearly dominate — The hills are alive. Luke, I am your father. Phone home. I’ll never go hungry again.

Surprisingly (at least to me), only 7 of the top 25 films won the Oscar for best picture and of the top 50, only 9 won best picture.

With the start of a new year, it only seems right to open with John Tukey and his work with interactive graphics. In 1972, when computers were giant and screens were green, John Tukey came up with PRIM-9, the first program to use interactive dynamic graphics to explore multivariate data. PRIM-9 allowed picturing, rotation, isolation, and masking. In other words, PRIM-9 allowed users to see multivariate data from different angles and identify structures in a dataset that might otherwise have gone undiscovered (kind of like the more recent GGobi).

To fully appreciate the revolutionary nature of PRIM-9 one has to view it against the backdrop of its time. When Statistics was widely taken to be synonymous with inference and hypotheses testing, PRIM-9 was a purely descriptive instrument designed for data exploration. When statistics research meant research in statistical theory, employing the tools of mathematics, the research content of PRIM-9 was in the area of computer-human interfaces, drawing on tools from computer science. When the product of statistical research was theorems published in journals, PRIM-9 was a program documented in a movie.

John W. Tukey’s Work on Interactive Graphics. The Annals of Statistics, Vol. 30 No. 6. 2002.

Luckily, you can appreciate Tukey’s work here at the ASA video library. It’s even more amazing when you consider where computers and technology were at back then. Who knows where Statistics would be if it weren’t for Tukey and his brilliance and creativity. I can’t imagine, or maybe I just don’t want to.

Tukey was someone who truly understood data — structure, patterns, and what to look for — and because of that, he was able to create something amazing.

It’s been a little over six months since I put up my first FlowingData post about creating effective visualization. Going through the archive, I’m amazed by how much this blog has developed and more importantly, by the people I’ve found who have many of the same academic interests that I do. For that, I’m extremely grateful.

I’m also pretty impressed with how consistent I’ve been with the posts, because to be honest, I wasn’t sure if I’d be able to keep it up when I first started. Had I known about all of the interesting data visualization work and research going on, I wouldn’t have had such sour thoughts. Now I know better, and I hope others are benefiting.

So here we are — the top 10 most viewed posts for 2007:

1. Three Designers, a Statistician, and Migration Inflows Data
2. What is the Best Way to Learn Flash/Actionscript for Data Visualization?
3. News Flowing Through Moveable Type at The New York Times Building
4. Visualizar Showcase Officially Opened at Medialab
5. Yahoo Charts Control Library Now Available
6. Sharing Personal Data to Push Social Data Analysis
7. Netflix Prize Dataset Visualization
8. 100 Reasons You Should Be Interested in, Want to Share, and Get Excited About Data
9. Bars as an Alternative to Bubble Charts
10. Use Flare Visualization Toolkit to Build Interactive Viz for the Web

Happy new year! See you in 2008.

Zachary Pousman et al. write in their paper Casual Information Visualization: Depictions of Data in Everyday Life

Information visualization has often focused on providing deep insight for expert user populations and on techniques for amplifying cognition through complicated interactive visual models. This paper proposes a new subdomain for infovis research that complements the focus on analytic tasks and expert use. Instead of work-related and analytically driven infovis, we propose Casual Information Visualization (or Casual Infovis) as a complement to more traditional infovis domains. Traditional infovis systems, techniques, and methods do not easily lend themselves to the broad range of user populations, from expert to novices, or from work tasks to more everyday situations.

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I stumbled across this article about Aili Malm, a GIS specialist (I think) who uses social network analysis to find the most probably locations of organized crime.

“I look at where organized crime groups are located and I study how these groups are linked to one another,” she explained. “I can chart their cell phone use or e-mail communication or with whom they co-offend. Based on these connections, I try to isolate the important players. Then I take the social and make it spatial. I look at individuals important to the criminal network and map where they live and where they commit their crimes.”

It’s just like that show Numb3rs on CBS. Albeit, math and statistics is a bit glorified on the show, but hey, at least it’s loosely based on reality.

Merry Christmas Bedford Falls! Merry Christmas you old Savings and Loan! Merry Christmas Mr. Potter! Merry Christmas! Gosh, I love that movie. I watch it every year, and it never gets old. That scene where he comes home so happy to be alive, his children are hanging off of him, and he’s embracing his wife… wonderful.

On that note, posts here on FlowingData will be sparse through January 1 as I buckle down and focus on relaxing and having fun. I can’t wait to see what Santa brings me. I am going to make sure I leave him extra cookies and a big glass of milk. I suggest you do the same. Santa wasn’t so nice last year. He gave me a pair of used socks, a half-eaten candy cane, and a note that asked, “Where are my cookies and milk?” I am sorry Santa. It will never happen again.

Merry Christmas and have a happy new year!

Baseball (or all sports for that matter) statistics are all over the place. You can easily find data for pretty much whatever sport and for whichever player you want at any given time. The problem is that if you want to download all of the data at once, you usually have to write a script and do some parsing. Who wants to do that? I don’t.
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Jonathan Corum and Farhana Hossain created a network visualization that shows readers who has spoken about who in presidential debates. Scroll over each candidate name to isolate the connections; important/interesting points are highlighted. Candidates are colored blue and red for their respective political parties.

There are three main things that this thing shows — who has spoken about who (lines), who has been talking the most (circle segments), and finally, attention by party (red and blue). In usual fashion, The New York Times churns out another beautiful graphic. Not only is the visualization attractive, but unlike so many network diagrams before it, this graphic is also useful and informative.

Noah Kalina took a picture of himself every day for six years (and still going); above is all of the pictures put together into a time lapse. Now that’s diligence.

When I was collecting my own step data with a pedometer, I would constantly forget, and eventually, I just got bored with it. I think my interest faded because collecting one number per day wasn’t satisfying enough. This on the other hand, seems more personal, it takes a little less effort, and it only takes a second to take a picture, and like they say, a picture is worth a thousand words. String them together and you get a story.
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YouTube (or should I say Google), released their visualization for related videos. It’s essentially a ball and stick graph without the sticks. The above is a screenshot of the videos related to Marty McFly playing Johnny B. Goode in Back to the Future, the greatest movie of all time.

Some of the video bubbles that circle around the Marty clip are the same as those in the “Related Videos” section of the usual page while others are not. Place the cursor over a bubble for about two seconds, and related videos for the one you have your mouse over will bubble up.

I’m not sure if the distance between the bubbles have to do with similarity level. So far it seems not, because I’ve refreshed the Marty visualization a few times and the bubbles’ initial positions have always been different.

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Time Magazine’s multimedia section has a fun, little piece showing some statistics for a day in the life of the average American. There’s some mapping for average commute time, annual traffic delays, and city population shifts. I’m not a huge fan of the map on the third dimension, but oh well.

There’s also a simple grid ranking jobs by level of happiness. Priests are apparently the happiest with gas station attendants at the very bottom. Poor gas station attendants. I guess I might classify myself as a computer programmer which is somewhere in between waiters and dress makers. Maybe I should consider a change in focus. Although, I could also consider myself an engineer which is towards the top of the rankings. Alright, I’m an engineer. The title of “computer programmer” has a weird stigma attached to it anyways.

For our humanflows visualization, we used data from the United Nations Common Database and the Migration Information Source. The great thing about these types of sources is that they are publicly available so that everyone gets to have fun with the data. The downside is that the data is accessible via a user interface that often makes it a chore to get all of the data.

Hence, to save you some time, you can now download the migration database that we used. I don’t see any reason why you have to go through the whole data importing process when we already did it. Enjoy!

Disclaimer: Keep in mind that the data is from the United Nations and Migration Information Source, so you should refer to the two sites for any documentation. In a nutshell, the inflows table is from MIS and the rest is from United Nations. If you’re looking for more, you might also want to check out OECD. I really wanted to use their data at the time, but was having trouble accessing it from Spain.

You used to only be able to get a small thumbnail to “share” the visualization you found or created on Many Eyes (well, outside of taking screenshots and emailing), but Many Eyes just announced the embed feature. In the same way you can embed YouTube videos, you can embed Many Eyes visualizations. This is a really big step forward, because users can share what they’ve found or seen more easily and as a result, it’s more likely others will become drawn in. You know, it’s that whole viral marketing thing.

Just one weird thing. I had to change the single quotes in the copy and paste snippet to double quotes for the embedding to work, because my version (or all versions?) of WordPress escapes the single quotes.

Studies on names and performance seem to be all the rage right now:

We like our names. And that preference can have negative repercussions, according to research published last month. Major leaguers with “K” initials tend to strike out more, perhaps reflecting the batters’ unconscious pull to appear next to the strikeout symbol “K” on scorecards. Students with initials C and D have worse grades than the A’s and B’s and everyone else, gravitating toward the grades their initials represent.

Of course, I’m a little skeptical about all of these studies, and with tiny effects like 0.02, these studies probably deserve it. In any case, they’re still interesting to read about. I wonder how one could get his hands on such data. The data’s probably just an email away, but in my current half-asleep stooper, I’ll leave that for another time. I’m sure it’d be really interesting to play with though.

Have you read Freakonomics? If you have, all of these name studies remind me of that chapter about the two brothers named Winner and Loser. If you haven’t read the book, uh, there’s a chapter on two brothers named Winner and Loser.

Most are familiar with the Netflix Prize. If you’re not, Netflix has offered a one million dollar prize to whoever improves their movie recommendation by a certain amount. It’s been going on for a little over a year with still no grand prize winner. The dataset is 100 million ratings.

The above is a visualization of the Netflix dataset. Each dot represents a movie, and the closer two dots are the more similar the two corresponding movies are based on Netflix ratings. I’m guessing the orientation of the dots was decided by some variant of multidimensional scaling.

It’s kind of fun to scroll over the clusters. Like in the bottom right we see Babylon 5, Buffy the Vampire Slayer, Alias, and Battlestar Galactica clumped together. The giant blob in the middle, however, is pretty useless; it’d probably benefit from some zoom functionality.

The Need to Explore

I’m kind of surprised that I haven’t seen more Netflix visualizations like this (or ones better than this), because I’m pretty sure it would help see some relationships that typical analysis won’t provide. I was browsing the forum and saw someone ask if others had had success loading the 100 million observation dataset into R. Silly undergrad.

A computer scientist, designer, and statistician walk into a bar; they discuss how they would boost the Netflix recommendation system. The punchline is that they win a million dollars, but I’m not sure what happens in between.

The Visualizar Showcase is officially open and ready for public viewing, so if you’re in Madrid (and I’m about 80% sure you will be) from now until January 5, 2008 check out the projects spawned from two weeks of hard work. You can find a complete list of the projects at the Visualizar website, but here are a few of my favorites in no particular order.

Mail Garden

Mail Garden, from Kjen Wilkens, explores emails under a garden metaphor with the implication that our email is in someway living (like all data). In the visualization, emails exist as plants and as you scroll over you can read each email. The best part of of Mail Garden though is probably when you’re not using it. When the system is idle, you can watch your plants (your emails) gently sway back and forth in the wind.

TweetPad

As if Twitter weren’t playful enough, TweetPad, by Elie Zananiri, is a visualization that lets you playfully explore the live Twitter feed. Elie’s main interest was in word interaction, and you can see that clearly in the TweetPad. Move the cursor clockwise for synonyms, back and forth to shuffle words, and counter clockwise to revert back to the original tweet all the while the Twitter feed is coming at you live.

Spamology

This visualization, as you might have guessed, explores one of the most popular canned meats in the world. No, just kidding. Spamology, by Irad Lee, explores email spam. The visualization is nice as you explore the small and giant buildings of spam, but it’s the sound accompaniment that really makes it. Sound corresponds to the height of each spam building. Usually, pieces like this end up sounding like noise, but this was more like beautiful music.

Now before I cover every work, which I’m a little tempted to do, I’ll stop here. If you happen to be in Madrid, Spain, go check it out. If you read this blog, you’re more than likely to enjoy the projects on display at the Medialab… or you can watch it on the news. Visualizar was also featured on some news show in Madrid. Be patient. The segment on the workshop comes some time around ten and a half minutes.

Yahoo: Look Google, I’ve got a Flash charts API now. I make it easier for people to plot their data, and look, pretty colors.

Google: So what. Look what I’ve got. I have URL-based chart creation with fun, cartoon-ish Google colors. My API is way easier, and plus, since I’m Google, everyone will use my API and not yours.

Y: Why are you so mean to me? We both have two O’s in our name. Can’t we be friends?

G: No. That’s right, you heard me. I’m better. Now kiss my feet.

Sigh, poor Yahoo. Right after Yahoo released their flash-based charting API, Google proudly announces a super simple charting API of their own. The idea is very straightforward. It all starts with the URL http://chart.apis.google.com/chart and from there

1. Add parameters to URL
2. Link to URL as an image

That’s it.

For example, this URL

http://chart.apis.google.com/chart?chs=520×225&chd=s:helloWorld&cht=lc&chxt=x,y&
chxl=0:|Mar|Apr|May|June|July|1:||50+Kb

gives you

You have the usual options of line, bar, pie, venn, and scatter; and you can change the colors, labels, size, etc.

With all the charting available, could this be a sign that data is becoming more popular?

[via Blogoscoped]