Remember SimCity 2000? That was a great game. That was probably the last computer game I played for any significant length of time, and if my Macbook Pro were able to read 5-inch floppies, I’d totally pop it in and build myself a city called Yau Town.

Put the look of SimCity 2000 together with Google Maps, and you get OnionMap. Most of the site is in Korean, but from what I gather it aims to be something of a tourist guide with a little bit of social network mixed in. That part of OnionMap is a little fuzzy, but it was worth the five minutes for the maps.

[Thanks, Tim]

Google announced Insights for Search yesterday. Think Google Trends but with more information and more useful. Type in some search terms and get the rundown on interest over time based on search volume, regional interest, and related searches. It’s geared towards advertisers using AdWords, but it can still be interesting to outsiders.

For example, I put in a search for data + visualization + design + statistics and got the above. Apparently interest for all of those subjects (i.e. FlowingData) is on the decline and India sure loves its data. I’m packing my bags to India as we speak.

[via TechCrunch]

Lee Byron, Amanda Cox and Matthew Ericson of the New York Times graphics department map Olympic medals starting from the first one hosted by the International Olympic Committee in 1896 up to the most recent one in Athens. It looks like someone has an affinity for the colliding ball effect. Not that that’s bad or anything.
Read More

Circos is a project by Martin Krzywinski that lets you upload genomic data and visualize it as a network like the one above.

It is easy to plot, format and layer your data with Circos. A large variety of plot and feature parameters are customizable, helping you make the image that best communicates your data. You supply your data to Circos as flat files (e.g. GFF format), tell Circos what you want plotted using the configuration file, and then create the image.

While Circos is developed in the interest of visualizing genomic data, it is general enough that you can use it with other types of data that show relationships. The New York Times debate graphic is the first thing that comes to mind. Anyone want to give Circos a spin? Post a link to your image in the comments.

[Thanks, Max]

Our FlowingData community went up from 2,641 subscribers last month to about 4,100, so more than a third of you are new. Welcome (and thanks to the those of you who have obviously been spreading the word :). As a new reader, you might not know where to begin, so let me show you around.
Read More

Christopher Nolan’s Dark Knight, starring Christian Bale and the late Heath Ledger, has been breaking records left and right. After only 10 days, the movie passed the $300 million mark – faster than any move before it. Pirates of the Caribbean: Dead Man’s Chest was the previous record holder. Pirates did it in 16 days.

So the next record that everyone’s wondering about is — Will Dark Knight make more than $600 million to beat Titanic as the highest grossing film of all time? So far it’s been 12 days and has grossed $333,929,159. Punch your answer in the poll below.

How much do you think Dark Knight will make (domestically)? I say it won’t do it — $525 million tops.

A new version of Flare, the data visualization toolkit for Actionscript (which means it runs in Flash), was just released yesterday with a number of major improvements from the previous version. The toolkit was created and is maintained by the UC Berkeley Visualization Lab and was one of the first bits of Actionscript that I got my hands on. The effort-to-output ratio was pretty satisfying, so if you want to learn Acitonscript for data visualization, check out Flare. The tutorial is a good place to start.

Here are some sample applications created with Flare:

[Thanks, Jeff]

Are you ready for another deconstruct/reconstruct exercise? I just posted a time series plot in the FlowingData forums that shows suicide rates and unemployment rates in Japan. Here are questions worth considering:

• What is the graph trying to show? Does it succeed?
• Is this the appropriate type of plot of this type of data?
• What would make the data more clear?

At a glance, the graph almost looks fine, but on a slightly deeper than superficial look, there are some clear problems.

Through the Internet, sharing data has — you know what, I’m not even going to try to make this relevant. A car exploded in my driveway!!!!

It was 6am and I was laying in bed. There was a continuous honking horn that was annoying the crap out of me. I figured someone was trying to get someone else to move their car so that they could pull out, but after a minute of one long honk, there was a huge BOOOOMMMM!

I ran to my office window, and I saw a car on fire!! I managed to get some of it on camera:

It was quite the sight – and now my apartment smells like smoke. Luckily no one was hurt.

Barcodes. We all know what they look like. They’re the black stripes that vary in thickness with numbers that indicate something or another, but what is that something? Every product has a unique barcode number and when you pass it through an international key database, you get information about the product and the country of origin. Daniel Becker uses this data to create art in Barcode Plantage.

Once a bar code is keyed or scanned in, the program sends a request to the database, which returns a master file data. This master file data is then analysed to define positions, curves and colours of Bezier curves of the tree structure.

The number of these curves will vary correspondence to the number of figures in the code. Simultaneously, the user will hear a melody, which is based on the figures of the bar code.

Because every barcode is unique so is the resulting tree. Pretty.

[via swissmiss]

I’ve always liked twittervision. I’m not sure what it is, but it’s strangely mesmerizing, getting a tiny peak into others’ lives. This weekend, I recreated twittervision with a little bit of style for good measure. Say hello to Twitter World.

The Data

Twitter World shows updates from the Twitter public timeline, and makes use of the twittervision API for location. Until I get whitelisted for the Twitter API, I’m polling Twitter and twittervision every six minutes to keep things fresh. Hopefully neither putters out.

The Implementation

Like my visualization showing the spread of Walmart, I used Modest Maps (+ OpenStreetMap) to map things out, and I used TweenFilterLite to animate. I had all the gears in place and everything working nicely a couple of hours in – but that was with a flat XML file. The hard part was feeding the thing live data and then making sure everything was synchronized. That took, um, too much time.

In any case, not bad for a weekend project.

PS. Don’t forget to follow me on Twitter :)

Last week I asked if you could improve a mediocre bar chart showing party majorities by county. There was a resounding yes as many of you deconstructed and then reconstructed your own graphs. For reference, here’s the original chart:

Here are the key flaws to the original that you all caught:

1. The x-axis tick marks were in really weird places;
2. The y-axis label was misleading because the data were number of counties;
3. Red and blue would make more sense for Democrats and Republicans;
4. Counts for counties don’t match the years, because they are reversed;
5. We see a different story when we bring in data for undecided “other” and “declined to declare.”

What was the graph trying to show? It was trying to show party registration in California over the past five presidential elections. Did it succeed? No. It failed miserably; however, you did much better. Here are all the reworks.

Brijesh made a stacked chart for Democrats and Republicans:

Tyler made a horizontal stacked bar chart with a useful majority line down the middle:

Blair provided some R code:

David used a tornado chart, which turned out well:

Amos went with a stacked line chart:

Kevin sent this one in:

John put together a few versions – this being one of about five:

Jorge went with simplicity:

Stack created a time series for the Dems and Reps:

Jake put up a fan favorite:

Nate, the graphic designer, embedded a stacked line chart inside the California boundaries:

This is the one I made at the workshop:

Personally, I like Jake and David’s the best, but who gets the golden star for best graph? I’ll let you be the judge.

Lee Byron, recent Carnegie Mellon grad and newly inducted New York Times graphics intern, maps walkability in San Francisco. He scraped Walk Score for uh, walk scores, which are scores from 0-100 based on the amenities around a location like “nearby stores, restaurants, schools, parks, etc” – how easy it is to live without a car.

Color was calculated on a per pixel basis using bicubic interpolation. From there he let Processing do the graphical labor to construct a map overlay. The result, which is accurate to the block, is a pretty one.

If you want data (sans map) for your own neighborhood, Lee has kindly provided the scraper.

In the FlowingData forums, Ryan asks a really good question about data design:

What simple rules should we all follow when we present data?

I came up with three rules of thumb a while back, but surely there are more. Context, clarity, and real data are clear winners, but what else is there? Those are really broad and can be broken down a few ways – like reducing the number of variables could contribute to clarity. If you have any ideas, please do post your ideas to the forum thread.

Ah yes, I can hear you flipping through your Tufte books.

Notice anything new at the top of this page? FlowingData readers, say hello to FlowingData forums. FlowingData forums, say hello to FlowingData readers. Tada, you’re not strangers anymore. Now you can go post your interesting finds in the brand new FlowingData forums.

Six Forums to Post In

I’ve created six categories, all of which are tightly coupled to the blog:

• Statistical Visualization
• Infographics
• Mapping
• Artistic Visualization
• Statistics
• Data Sources

I got the ball rolling in the mapping forum with this animated carbon map from NASA. Nice.

Interact With Other Readers

One of my favorite parts about FlowingData is the interaction. I love comments that help me see and understand data differently, and I love getting emails from readers that point me to interesting stuff that I never would’ve found on my own. Recently, I’ve even met some readers in person. I hope that the FlowingData forums provide the same opportunities for all of you, and of course – make for some good fun.

So please do join the club, grab your favorite link from your hundreds of del.icio.us bookmarks, and post it to the forums. Only about five of you will actually do this, but hey, that’s still growth and enough for me to think that this is a good idea. Sometimes it’s a good thing that it doesn’t take much to keep me motivated.

I’ve been using Mozilla Firefox for years and have nothing but good things to say about the most recently released Firefox 3. Whenever I borrow someone else’s computer, and all he has is Internet Explorer, I feel wrong and dirty.

When I think Internet Explorer, I think vulnerability, crashing, spyware, adware, sluggishness, and more crashing. I imagine running AdAware on my mom’s laptop over and over again.

This calendar graphic on the Mozilla front page captures that idea nicely. While a bar graph, pie chart, or just the numbers alone would have shown the data just fine, the calendars put the numbers into perspective. The calendars give readers a way to relate to the data, which makes the story all that much more clear.

[via Cool Infographics]

Martin Wattenberg, one of the creators of Many Eyes, in reply to “Why is a numbers guy like you so interested in large textual data sets?”

The entire literary canon may be smaller than what comes out of particle accelerators or models of the human brain, but the meaning coded into words can’t be measured in bytes. It’s deeply compressed. Twelve words from Voltaire can hold a lifetime of experience.

Martin Wattenberg = smart guy.

A few days ago, FlowingData’s subscriber count shot up to 3,100+ subscribers, moving past the three thousand mark for the first time. I just wanted to take this chance to thank everyone for reading. Thank you. FlowingData wouldn’t be the same without you, and I’m really happy with the community that’s developing around this modest, little blog of mine, or maybe I should say of ours.

Thank you for reading, thank for commenting, thank you for linking here, and thank you for sending me post ideas. I appreciate it ALL. FlowingData is well on its way to 5,000.

BedPost – I put this up earlier for the FlowingData personal visualization project, but for those who missed out, Kevin recently put up a sign up form so that you get a notification for when the grown up activities tracker is ready for public use.

Bible Belt Got Back – We see fatness by state in this fun map by CalorieLab. The map title says percentage of obese adult population, but I think it really meant percentage of adult population that is obese. [Thanks, tarheelcoxn | via The Daily Dish]

Movie Color Spectrum – I couldn’t find more details for this, but from what I gather, we see the dominant colors of selected movies that range from rated G to NC-17. Notice a pattern as we start from happy go-lucky movies for children to the uh, more grown up movies? [Thanks, Tim]

Pew Study on Religion – USA Today uses horizontal stacked bar charts to show results from the Pew Forum on Religion and Publilc Life. What do you think – easy or hard to read? Do all the charts make the data more clear?

I’m on my way back home from the workshop Integrating Computing into the Statistics Curricula in Berkeley (and this time I managed to get through the line without getting yelled at). During one of the labs, there was an assignment called Deconstruct-Reconstruct which was a great way to learn how to improve statistical graphics. Basically, we picked apart (deconstruct) a graphic from Swivel and then created a better version (reconstruct).

Your Mission, If You Choose to Accept it…

As I was making my own version, I thought to myself, “I bet FlowingData readers would do really well with this exercise.” Let’s see if I’m right. Can you deconstruct-reconstruct the above graphic? Here are questions worth considering:

• What is the graphic (trying) to show?
• Does the graphic achieve its goal?
• Are there other data that could make the plot more informative?
• How can we improve the bar chart?

I’ll put my version a little later…This post will self-destruct in ten seconds…