Dataterrific: Time Travel in TV and Movies Visualized February 19, 2010Posted by dataduchess in Uncategorized.
Tags: beautiful, data, David McCandless, infographics, information, movies, time travel, tv, visualization
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In case you didn’t already know, let’s be clear about something: Information Is Beautiful – especially when it’s collected and displayed by David McCandless (and friends). This graphic can be found in his new book (available now from Amazon.UK) – but he generously has also shared it on his site!
You can find a HUGE version of the image directly on his website.
Also, he talks about how he (and his collaborators) went through 36 drafts before arriving at this final version in this post about his data graphing process. It’s incredibly impressive.
We’ve shown you some of McCandless’ work before, but it’s even more fascinating now, knowing how much work goes into the details of an infographic.
What do think? Are you impressed? Any information you think would make a compelling infographic?
Remember the Blackout in August 2003? December 23, 2009Posted by dataduchess in Uncategorized.
Tags: data, memories, NYC, power_failure, visualization, wikipedia
Yesterday’s post about the increase in visits to the New York Times website on the day of Michael Jackson’s death sparked an interesting comment from pupfiction about other notable days in recent history. I, in turn, recalled the Blackout of August 2003, including some some fascinating satellite images of the northeast from before and after the failure.
In trying to find the images shown above, I read the Wikipedia entry about the Blackout, and it was really interesting. Through an official investigation, they created a sequence of events that caused the blackout, and even traced the cause back to some overgrown trees near an Ohio power plant. This is worth reading.
I remember the blackout quite vividly: I lived in NYC at the time and was lucky enough to have only my regular 15-block walk home. My brother who was commuting to a summer job in the city was about to get on a subway when he felt the city shut down around him, and he turned and climbed the stairs back to the street before he could be trapped. He walked about 50 blocks to my apartment and then we set out to find a payphone (cell towers were overloaded, and I didn’t have a land line in my apartment) to call our parents and let them know we were together and safe. We spent the evening playing cards by candlelight, and turned a crazy event into a fun memory. The next morning, we ventured down to Grand Central, in the hopes that trains would be running, and fortunately we were able to get on one. We couldn’t buy tickets with no power in the station, but the conductors were just letting everyone on anyway… it was kind of nice that they just wanted to help people get home.
Do you remember where you were during the blackout? Did it affect you? Did the event turn out to be a positive, like it did for me?
How the Death of Michael Jackson Proved the Need for Quality Journalism December 22, 2009Posted by dataduchess in Uncategorized.
Tags: data, internet, journalism, media, michael_jackson, NYTimes, visualization
The Guardian’s Digital Content Blog picked up the awesome graphic linked to below. It shows the visits made to the New York Times’ website over the course of the entire 24 hour day June 25, 2009. This day is notable because of the death of Michael Jackson, and if you watch the time closely, you can see the spikes in visits in the minutes following the breaking of the news by TMZ.com, where people went to the New York Times site for confirmation, or more credible reporting. The Guardian uses this as proof that even with the seemingly unlimited access to news from all kinds of sites, there is still a need for reliable journalistic reporting. Whether you agree or not, you must admit, this is a neat visualization of the data. Do you remember how you heard about Michael Jackson’s death? Where did you go to find news coverage?
What is the point of all this beautiful data? December 9, 2009Posted by dataduchess in Amazing.
Tags: data, graphs, swine_flu, visualization
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Click on the graph above to see a truly amazing collection of data. This graph plots the Income per person of every country vs. the Life Expectancy of a person born in that country, with the relative size of the data point reflecting the size of the population. But wait – there’s more. The circles are color coded by region to more easily distinguish among areas of the world. But wait – there’s still more – you can adjust the year shown on the graph to view data from any of the last 207 years. Or hit “Play” and watch the circles grow and shrink and move across the graph on their own. If you select one or more countries on the list, and hit the play button, you end up with a colored path across the grid charting the growth, income changes and life expectancy changes of just that country. Of course, I checked out the path for the United States and noticed a dip in the life expectancy trend in the early 20th century. Pupfiction made a guess that the seeming abnormality was a result of an epidemic of disease that had a significant impact on the country that year. That made sense to me at first, but then I was wondering if widespread disease could so significantly affect the life expectancy like that. To put it in modern terms for comparison, say the life expectancy of an American born in the US last year was 85. With technological and medical developments being made all the time, the age ought to be getting older and older – maybe 86 this year (hypothetically). BUT- the H1N1 virus (swine flu) is spreading quickly and fatalities are mounting. Perhaps the levels of swine flu do not yet compare with previous plagues, but could the potential for epidemic be enough to lower the the life expectancy of an American baby born today? The part where I get stuck is that the actual average age of people who die in a given year ought not be that strong of a factor in determining the life span of people born in that year – I do not know know how life expectancy is calculated, but I imagine there must be more to it than that.
Well – clearly you see this graph has challenged me to consider things I do not normally think about. Do you spot anything interesting that you can’t quite wrap your head around? Or something enlightening that makes you wonder about the data or what it represents? Do you think I must be crazy for loving a graph I do not understand? No matter what, though, you must admit – this graph is an impressive piece of work.
Numbers Don’t Lie, or Do They? Simpson’s Paradox Explains December 2, 2009Posted by dataduchess in Uncategorized.
Tags: baseball, data, graphs, statistics, unemployment, WSJ
The Numbers Guy over at The Wall Street Journal had a really interesting article today. He explains a concept called Simpson’s Paradox, which essentially says aggregated data is sometimes misleading. For example,
… in both 1995 and 1996, Derek Jeter of the New York Yankees had a lower batting average for each season than David Justice, then of the Atlanta Braves.
Combining the two years, however, Mr. Jeter had a better average. The paradox resulted from the fact that in 1995 Mr. Jeter had only 48 at-bats with a .250 average while Mr. Justice had more at-bats (411) with a .253 average. The following year, Mr. Jeter had 582 at-bats with a .314 average while Mr. Justice had only 140 at-bats with a higher average of .321, pushing the two-year average in Mr. Jeter’s favor.
Other examples of the paradox can be found in all types of data, from air travel delay statistics and medical procedure success statistics, to education and unemployment data.
In the graph below, you can see that although the unemployment rates for each of the separate groups are higher now than they were in 1983, because the size of the group with the lower rate is so much bigger, the overall unemployment rate is lower than it was in 1983.
Confused? Don’t worry about it. The lesson here is to be wary of “hard data,” and remember that statistics can still be spun to fit any argument. This WSJ graph shows that unemployment is both better than in 1983, and worse. It only depends on which point you want to make.
Yankees in 6? October 28, 2009Posted by dataduchess in Uncategorized.
Tags: baseball, data, statistics
We’re in the home stretch here of baseball season with Game One of the World Series later tonight, if it ever stops raining in the Northeast. Baseball is a great sport for fans of physics, data and statistics. WhatIfSports is a site affiliated with Fox Sports on MSN that runs simulations of every and any game you can think of, in all different sports, past and present. They ran a simulation of this World Series, between the Yankees and Phillies, 10,000 times – and Yankees won 72.3% of the time. Those are some good odds.
A GOOD visualization of information October 16, 2009Posted by pupfiction in Uncategorized.
Tags: data, information, site, visualization
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A trend that seems to be abounding all over the web (and the world) is the visualization of information in new and innovative ways. One that I happened to stumble upon on the somewhat utopian-named good.is (a company, site and print magazine that describes itself as an “integrated media platform for people who want to live well and do good”) is this collection of people, ideas, companies, projects, etc. who are “changing the way we live”. The information is organized in the form of a virtual poster collection of icons which link to concise entries on the topic. This “top 100″ continues to be updated daily until October 22nd so check back for updates!
High Cost of Hoarding Data October 16, 2009Posted by dataduchess in Uncategorized.
Tags: california, data, legal, wired
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A California County tried to charge an exorbitant amount of money for mapping data it had collected, and when the non-profit coalition seeking access decided to sue instead of pay – the county claimed the data was withheld for reasons of National Security.
Well, not only did the court not believe it, but after a 3-year and thus very costly trial, the county has been ordered to release the data, and pay the coalition’s legal fees to the tune of $500,000.
So, instead making $250,000, the county is paying at least twice that, and that’s not counting their county’s own legal fees.