All posts from FlowingData

In a follow up to his McDonald's map, Stephen Von Worley of Weather Sealed maps the dominating burger chains across the United States. McDonald's obviously has a stronghold in a lot of areas but not all of them. Most noticeable is Sonic Drive-in with over 900 restaurants in Texas alone. Personally, I'm rooting for Carl's Jr. and In-n-Out.
[via We Love Datavis]

EPCOR, the water utility company that runs the fountains up in Edmonton, Canada released this graph yesterday. It's water consumption during the Olympic gold medal hockey game, overlaying consumption of the previous day. How much do Canadians love their hockey? A lot.
The first period ends. Time to pee. The second period ends. Time to pee. The third period ends. Time to pee. Consumption goes way down when Canada wins and during the medal ceremony.
Finally, when it's all said and done, the rest of the country can relieve itself, figuratively and literally.
[via contrarian | thanks, @statpumpkin]

Big news for all you Edward Tufte fanboys and girls. He will be joining the Recovery Independent Advisory Panel who will advise The Recovery Accountability and Transparency Board. The Board's purpose is to track and explain how the $787 billion in stimulus funds is being put to use.
I'm doing this because I like accountability and transparency, and I believe in public service. And it is the complete opposite of everything else I do. Maybe I'll learn something. The practical consequence is that I will probably go to Washington several days each month, in addition to whatever homework and phone meetings are necessary.
Whether Tufte will have a direct impact on graphs like these, I'm not so sure, but it certainly won't hurt. I mean the man does know a thing or two about dispersing information.

Footprints - Every building footprint, and nothing else, in Montgomery County, Ohio. It's interesting how buildings can define an area.
Data, data everywhere - The Economist reports on the explosion of big data and the challenges that come with it.
Q&A With Shawn Allen of Stamen Design - Always interesting to hear from these guys [thx, tim].
The Case For An Older Woman - Another thoughtful analysis from the okcupid group on why men should be more open-minded to dating older women.

There's this article on CNN, from The Frisky, that has this little theory about who is most likely to win the Oscar for best actor:
[T]he Oscar generally goes to the dude who has the most best actor and best supporting nominations under his belt already.
That seemed like a curious statement. Didn't Forest Whitaker, Philip Seymour Hoffman, and Jaimie Foxx recently win on their first nominations for the coveted award? Okay, so Hoffman was actually up against a bunch of other newbies, but what about the rest?
Only 10 out of the past 29 winners, or just over a third, had the most nominations their year. Take a look at the data since 1980. Is the theory valid? You decide.

Of course when Jeff Bridges wins tonight, the theory authors will declare victory, but oh well.
Just for fun let's take a poll:

Who will win the Oscar for best actor?





Jeff Bridges



George Clooney



Colin Firth



Morgan Freeman



Jeremy Renner





View Results

It was a good month for FlowingData. We passed the 30k-reader mark, and I think this past month was an all-time high for pageviews. Thanks again. everyone for reading and sharing FlowingData.
I also managed to switch servers (semi-) successfully while updating the FD homepage in the process. Make sure you check that out if you haven't already, and let me know what you think in the comments.
In case you missed them, here are the most popular posts from last month ranked by a combination of views, comments, and trackbacks. I especially enjoyed a lot of the thoughtful discussion that came out of these posts.

Track Mouse Activity On Your Computer
How a Giant Shark Took Down an Airplane
Data Underload #9 - Big Graphic Blueprint
Where Bars Trump Grocery Stores
Excessively Labeled Airplane Tells You Where the Big Cheese Sits
Think like a statistician - without the math
Road to Recovery - Is the Recovery Act working?
Data Underload #8 - Unsolicited
An Easy Way to Make a Treemap
Challenge: make this graph easier to read

From the Forums
There was also some good stuff going on in the forums with a couple of job postings and some data goodies.
Data Visualization Guru - Energy group EnergyHub is looking for someone who can help visualize their data.
Interactive Data Visualization help needed - So is FrogDesign, but for a smaller project.
Visual Architects Contest - Do you have what it takes to win?
WinterOlympicMedals - The Olympics are over, but that doesn't mean you have to stop playing with several decades of medal data [thanks, annie]

Simple yet effective. Any questions? [via 9gag | Thanks, Barry]

I call myself a statistician, because, well, I'm a statistics graduate student. However, ask me specific questions about hypothesis tests or required sampling size, and my answer probably won't be very good.
The other day I was trying to think of the last time I did an actual hypothesis test or formal analysis. I couldn't remember. I actually had to dig up old course listings to figure out when it was. It was four years ago during my first year of graduate school. I did well in those courses, and I'm confident I could do that stuff with a quick refresher, but it's a no go off the cuff. It's just not something I do regularly.
Instead, the most important things I've learned are less formal, but have proven extremely useful when working/playing with data. Here they are in no particular order.
Attention to Detail
Oftentimes it's the little things that end up being the most important. There was this one time in class when my professor put up a graph on the projector. It was a bunch of data points with a smooth fitted line. He asked what we saw. Well, there was an increase in the beginning, a leveling off in the middle, and then another increase. However, what I missed was the little blip in the curve in the first increase. That was what we were after.
The point is that trends and patterns are important, but so are outliers, missing data points, and inconsistencies.
See the Big Picture
With that said, it's important not to get too caught up with individual data points or a tiny section in a really big dataset. We saw this in the recent recovery graph. Like some pointed out, if we took a step back and looked at a larger time frame, the Obama/Bush contrast doesn't look so shocking.
No Agendas
This should go without saying, but approach data as objectively as possible. I'm not saying you shouldn't have a hunch about what you're looking for, but don't let your preconceived ideas influence the results. Because if you go to length looking for some specific pattern, you're probably going to find it. It'll just be at the sacrifice of accurate results.
Look Outside the Data
Context, context, context. Sometimes this will come in the form of metadata. Other times it'll come from more data.
The more you know about how the data was collected, where it came from, when it happened, and what was going on at the time, the more informative your results and the more confident you can be about your findings.
Ask Why
Finally, and this is the most important thing I've learned, always ask why. When you see a blip in a graph, you should wonder why it's there. If you find some correlation, you should think about whether or not it makes any sense. If it does make sense, then cool, but if not, dig deeper. Numbers are great, but you have to remember that when humans are involved, errors are always a possibility.
*Photo by misterbisson

Frederik Seiffert provides this nifty tool, LastHistory, to visualize your Last.fm listening history. Mouse over songs and find repeated track sequences. The visualization itself isn't all that useful, but it gets interesting when you hook your calendar and photos in with music. LastHistory lets you replay songs synched with your photos, and your slideshow suddenly gains a new dimension.

I don't use Last.fm, so LastHistory isn't much use to me, but I'd be interested in hearing what others think. Give it a try for your self though (for Mac only), and leave your thoughts in the comments below. Is it a reason to start using Last.fm?

FloatingSheep, a fun geography blog, looks at the beer belly of America. One maps shows total number of bars, but the interesting map is the one above. Red dots represent locations where there are more bars than grocery stores, based on results from the Google Maps API. The Midwest takes their drinking seriously.
Of course there are plenty of possible explanations for the distribution. Maybe people get all their food from superstores like Walmart in the red dot areas, so there are fewer gigantic stores than there are small local bars.
Then again, the FlowingSheep guys did their homework and found, according to Census, that the number of drinking places in those red dots are really skewed compare to the average. So it's also possible that area of the country just likes to drink a lot.
Anyone who lives in the area care to confirm? I expect your comment to be filled with typos and make very little sense. And maybe smell like garbage.
[Thanks, Michael]

...

Like everyone, I've been watching the Olympics, and it continues to amaze me how hundredths of a second can make up the difference between a gold medal and nothing at all. Amanda Cox of The New York Times visualizes and audiolizes(?) these tiny differences. She got creative with this one.
Each row is an event and going from left to right, the first dot is the gold medal winner. The amount of space between the first dot and the dots that follow is how many seconds athletes finished after the winner.
Visually, this only sort of works, but click on play to hear how these differences sound, and it puts everything in perspective.
See the rest of NYT interactive Olympic coverage here. You know, just in case NBC coverage doesn't cut it for you.