This map, a collaboration between Good and Gregory Hubacek, shows three metrics from the most recent American Community Survey by the US Census: high school graduates, college graduates, and median household income. The goal was to see if there’s a correlation between education and income. Does it work?
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Data is rarely in the format you want it. Dan Nguyen, for ProPublica, provides a thorough guide on how to scrape data from Flash, HTML, and PDF. [via @JanWillemTulp]
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Focus too hard on individual points, and ignore what’s around, and you too might fall in the metaphorical fountain. Yes, this is my excuse for posting this hilarious video, and I’m sticking to it.
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Designer Jessica Hische gets her flowchart on to help you decide whether you should work on that project for free. In short, if the client is not your mom or a friend you owe big, they better pay up. Love the blunt honesty from Hische, who I’m sure gets all sorts of odd offers. See full flowchart here.
[Jessica Hische via @MacDivaONA]
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Nick Foster has some fun with t-shirt classification after cleaning out his closet:
I have a T-shirt problem: after years of buying them, my house is now full. Whilst recently trying to tidy up the situation I realised that I was subconsciously categorising them.
Ten types are identified, although half of my shirts probably belong in the above three. I’m a bum. But I have golden voice.
[sleepinginmyhead via kottke]
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According to polls from the Pew Research Center, the Internet gained on Television as the public’s primary news source in 2010. Poll results are shown in their graph below.
The graph isn’t too bad, but it’s kind of busy and could use some design. Can we do better? I think so. Here’s the data as a CSV file. Get your graph on, and link to your efforts in the comments below.
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When you invest in stocks, it’s not just what you invest in, but also when you put the money in and when you get it out. The New York Times explains with this grid diagram:
This chart at right shows annualized returns for the S.& P. 500 for every starting year and every ending year since 1920 — nearly 4,000 combinations in all. Read across the chart to see how money invested in a given year performed, depending on when it was withdrawn.
Darker red represents greater loss while darker green represents the greater gains. Tan color indicates more modest gains.
The method probably isn’t new, but it’s the first time I’ve seen it. I like it. I’ve only seen those “what if” calculators where you enter a value to see how it would’ve paid off. That only lets you see one scenario at a time. This type of chart lets you see multiple time spans at once.
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Haiti’s earthquake in 2010 was by far the most devastating in a long time. There were an estimated 222,570 casualties as a result. However, as Peter Aldhous shows in this graphic, the Haiti quake was not the most powerful.
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In memory of the devastating earthquake that hit Haiti one year ago on January 12, 2010, the New York Times shows aerial photos of Port-au-Prince from GeoEye and Google in this interactive. See views form before the earthquake, a few days after, and now.
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Cartographer Andy Woodruff documents all the places he goes, resulting in the pretty map above.
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New gadgets, from Web-connected TVs, to smartphones, to Fax machines, always seem to start expensive and then decrease in price a few months later. We all know this. But by how much? Alicia Parlapiano of The Washington Post takes a look in this interactive. It shows units sold by year for different gadgets. Bubble size indicates average price.
Poor tape player. The Consumer Electronics Association didn’t even bother tracking its sales after 2005.
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Fact is not always clear cut. Sometimes fact is driven by opinion. People might have conflicting points of view or maybe the truth is simply unknown. We can see this via Wikipedia, where anyone can edit and create documents. Sometimes people propose that articles should be taken down, and if the proposal is approved, people can discuss. Dario Taraborelli, Giovanni Luca Ciampaglia, and Moritz Stefaner have a look at the most active of these discussions.
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Map of London colored by what team the majority supports. Not much of a soccer… ahem, sorry, football fan. Accurate? [QPRdotorg via We Love Datavis]
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In this series of interactive cartograms, FedEx shows our changing world (and I guess, how they are changing with it) through a variety of worldwide demographics such as access to mobile Web, growth, and happiness. Above is the cartogram for richest countries i.e. GDP. Choose a topic, press play, and the cartogram changes accordingly to match the current metric.
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When you slow down your car, energy dissipates into the air as heat. What if your car could instead make use of that energy? Your car could run that much more efficiently and get more miles per gallon. GE explains such a system called dynamic braking (video below).
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Looking to get more serious about your data analysis? Data Analysis with Open Source Tools by Philip K. Janert can help you with that.
The back cover reads:
Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You’ll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.
It’s a little over 500 pages and thorough about describing how to analyze your data. However, it is light in the “with open source tools” part of the title. Most of the time is spent explaining concepts, and then each chapter ends with a workshop, which includes some code. There are examples throughout, but few provide an explanation of how a plot was made or the implementation of a method. So definitely not a book for beginners.
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In case you haven’t heard, O’Reilly’s new Strata Conference on “making data work” is coming soon February 1-3 in Santa Clara, California. It’s three days chock-full of data talks, tutorials, and events. And January 9 is the last day to get the early registration price.
So if you’re thinking about going, I’d register soon. You might as well save a couple hundred bucks. Plus if you register via FlowingData, you get 25% off (and support your favorite data site in the process), which is applied at checkout.
There are a ton of speakers, including DJ Patil from LinkedIn, Philip Kromer from Infochimps, Hilary Mason from bit.ly, and Kim Rees from Periscopic (and technical editor for the upcoming FlowingData book). No doubt you will learn a lot and meet plenty of interesting people who are also into data.
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This Tract, by Michal Migurski of Stamen, with some help from Craig Mod, lets you view details of your block by way of Census data. It’s still using 2000 data but was built in anticipation of the 2010 release, which should come in a couple of months. So we’ll probably see some improvements from now until then.
Enter your location or browse the slippy map for information on race, income, gender, education, age, and housing. There are also aggregates for your Census tract, county, state, and country.
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Zachary Seward for the Wall Street Journal gives some thought to what he does online via applications like Twitter and Foursquare. He notes, “[I just] ended up with this wealth of data.”
Lifelogging is often attached to obsessive tickmarking in notebooks and counting things that don’t need to be quantified. It keeps getting easier to collect data about yourself though, and in due time, lifelogging will feel so natural, you won’t even have to think about it until you’re reviewing your very own [insert name here]-tron report.