animation

What Works

Your sense of who’s single and when they’re single will grow immensely in three or four minutes of playing around with this interactive map of single-ness in the United States, by age and gender. Men get married later and die younger. This means that at young ages, there are more single men than single women because some men who will eventually get married won’t marry until later, on average, than the women they end up marrying. This is just a complicated way of saying that men often marry younger women. In old age, there are more single women than men (the imbalance is because the men start dying younger). During the decade of the twenties and then after about age 65 you’ll find the largest proportions of single-ness. People in the middle decades, from 30-60 or so, are more likely to be coupled. But don’t take my word for it, click through and play around. This data actually understates the number of people who are functionally single because single is measured here as never married. So the folks who have been divorced or widowed and haven’t remarried do not count as single for the purposes of this graphic.

The writer of the text accompanying the graphic is interested in the geographical distribution of single women and single men so there’s more on that if you click through.

What Needs Work

I like this one a whole lot so I don’t have much to say except that I wish the designer wouldn’t have gone with the red/blue, female/male color scheme. How about purple and green? Or orange and teal?

I also think I would have counted people who are divorced/widowed and NOT remarried as single.

The graphic designer is careful to note that since homosexual couples cannot get married, they will erroneously be counted as single, even if they are partnered. That’s a problem with the underlying data collected by the census, not the graphic design.

Relevant Resources

American Community Survey (2006)

Soma, Jonathan. (2008) The Interactive Singles Map

What Works

This is an animation based on twitter data from Obama’s inauguration day in the US – Inauguration was at noon. In case you weren’t a twitter user at the time, it is worthwhile to point out that twitter had partnered with Facebook for the day to increase usage. Both twitter and facebook were encouraging users to point their comments towards the topic of the inauguration.

I like it because its like watching fireworks from above and gives a tangible sense of the excitement amongst Obama fans that day. Best thought of as an emotional animation of political temperature than as any kind of quantitative data. I wouldn’t even call it an information graphic/animation. I would call it popcorn, animated.

What Needs Work

I have the same problem with this animation that I have with twitter which is that I really don’t know what good they do, even though I’m intrigued. I’ve been trying to figure twitter out by using it and I still don’t see the appeal. Thus, it is quite alright to think this animation is pretty, but dumb.

Relevant Resources

Flowing Data (2008) Worldwide Inauguration via Twitter

What Works

First, the elegant sophistication of this graphic is breathtaking. I love watching it and I have watched it for long enough to start asking questions about it. Maybe I am different than other people, an outlier of some sort, but in this case I don’t think so and that’s why my own fascination indicates a larger virtue of the graphic. If it draws people in and gets them asking questions, it is doing something right. Holding eyeballs in this media saturated world is a triumph in itself. Having answers to the questions that are posed is a secondary but even more critical step. To figure out what you’re looking at, here’s what the folks who made it have to say for themselves: “Cabspotting traces San Francisco’s taxi cabs as they travel throughout the Bay Area. The patterns traced by each cab create a living and always-changing map of city life. This map hints at economic, social, and cultural trends that are otherwise invisible. The Exploratorium has invited artists and researchers to use this information to reveal these “Invisible Dynamics.” The core of this project is the Cab Tracker. The Tracker averages the last four hours of cab routes into a ghostly image, and then draws the routes of ten in-progress cab rides over it.”

Second, they are right that just knowing where cabs go is more than knowing where cabs go. It’s knowing about urban space over time. It’s certainly knowing where the airport is (and that airports are far away). Looking at this we get to see the grid of the city and the longer stretch of highways and bridges bringing people in/out. It would be nice to see what this sort of ghostly cab mapping technique would reveal about cities I know a little better than San Francisco. Keep this site tucked in your back pocket for later this year, all you ASA meeting-goers.

What Needs Work

I just wish there were a simple way to say a little more about the cabbies themselves, who end up looking like infrastructure or phantoms, rather than actual people. In New York, 91% of the cab drivers are immigrants and only 1% are women (2006 Schaller Consulting). Is there a way that this cab-tracker could become a little more about the humans in the city?

Relevant Resources

Richards, P. and Schwartzenberg S. Snibbe S. and Balkin A. cabspotting San Francisco.

Schaller Consulting. Repository of Reports on Cabs in New York and beyond

Plaut, M. (2007) Hack: How I Stopped Worrying About What to do with my life and started driving a yellow cab. New York: Random House.

Buddhacab blog written by a New York yellow cab driver

Link rot note

This post used to source a population growth animation from zachofalltrades.net but that website is no more. The University of Kentucky Appalachian Center is better, so count yourselves lucky if you missed the original post in favor of this update.

What Works

First, you must click through and watch the animation. Praise #1: yay for gifs.

Like the previous post that looked at China, this animation is trying to tell a story about population growth over time. The major difference is that the Chinese example was strictly demographic – looking at variables like gender and age but not at all concerned with geography. This one shows both geography and population growth though it does not include information about gender, age, race, etc.

What Needs Work

If this graphic were three dimensional, if density piled up, it would start to ‘feel’ heavier over time so that the same way that the westward expansion of the population just appears without you having to puzzle it out, the density of population in cities would be simply obvious. This is not meant to be a dig to the graphic’s creator. I just offer this critique as a way to think about just why and how ‘seeing is believing’. Watching the population move west is certainly a ‘seeing is believing’ moment because viewers do not have to think, they just have to watch. Realizing that the population of the US is now hugely larger than it was back in the 1880’s actually takes a little thought. You have to realize that not only did people move west, but they continued to live in the east in greater densities which is indicated by the size of the yellow circles, but would be even more obvious if the cities were like little hillocks on the landscape. Big yellow dots equaling density requires a move from the ‘seeing is believing’ to something else. If, however, the map grew in the third dimension as a more direct representation of the mass of humanity sitting on the face of the earth at these locations, we’d be back in ‘seeing is believing’ territory.

A graphic that is a ‘seeing is believing’ creation is instantly legible and can free your brain to think about other things which is a good thing. On the other hand, a graphic that achieves a ‘seeing is believing’ mechanism will end up obscuring complexity. This is good when that complexity does not add to the ability to think through the next set of concerns, but can be a serious drawback. It is good to be able to get a diversity of people able to quickly grasp an argument, but there is a danger in presenting an hermetically sealed glossy image.

Relevant Resources

University of Kentucky Appalachian Center. US Population Growth from 1790-1990

Internet Traffic

This week we’re going to have a look at the internet. Here are two reasons why:

• 1. The not entirely superficial reason is that there are many great visualizations out there dealing with the internet, internet traffic, internet usage patterns, and so on. Many are interactive so you can play around with them yourselves.
• 2. The larger theoretical question about studying the internet and online behavior goes something like this: How much is people’s online behavior reflective of their offline behavior? Are people role-playing when they’re online, trying out personas they may not fully embrace offline (see Sherry Turkle)? Or is online behavior seamlessly integrated with offline behavior? We IM the people we’re about to have dinner with indicating that the people we talk to online are just about the exact same people we talk to offline? And if the relationship between online and offline behavior is somewhere between these two, how can we figure out just what is going on?

What Works

The graphic above is just a screen capture from Akamai’s site. In order to get the full impact, you have to click through and play around with it. Akamai has a slew of other visualizations you can play with that deal with network attacks, latency/network failure, retail data, news traffic, and so on.

Just to be clear, Akamai is a private company providing web-optimization services. In their shareholders’ quick facts, they say they serve up 10-20% of global internet traffic. What does this mean? It’s easy to forget that the internet requires physical structures, but this is part of what Akamai does. They maintain “40,000 servers in 70 countries within nearly 950 networks” all over the world slurping up electricity and information at about equal rates. The reason they do this is because if you are, say, a blogger in New York and you store your files on a server just down the hall (which is unlikely, but play along), if someone in Singapore wants to read your blog, the request is going to have to come all the way from Singapore to the server down the hall from you in New York and then the files will have to be sent all the way back to Singapore. This takes time, there might be network congestion along the way and if you are serving your readers in Singapore something a bit more bandwidth intensive than text (say a little clip of a new car racing around a track or a high quality music download) the person in Singapore may just lose interest before they even get the whole file. Akamai gets around this in part by duplicating files and storing them on servers all over. So if your reader in Singapore wants to access your site and you’re an Akamai customer, they will end up pulling those files from a server much closer to them, maybe in Singapore, but at least somewhere much closer than New York. Akamai’s clients tend to be Fortune 500 companies with global client bases and companies that rely on being able to transfer heavy files reliably and quickly (like music and software downloads). They do more than just the physical infrastructure, they mobilize their resources to detect net attacks, congestion, and then to re-route and avoid those things. The bottom line for us is that they make some of their knowledge of the ‘net available in these visualizations like the one above.

What Needs Work

I would love to have more granularity and access to the actual numbers and the methodology. All these shiny interactive graphical toys run the risk of being too glossy, not data-transparent enough.

Not as Shiny, Quite Helpful

These two graphs give a quick overview of who is using the internet by geographical location. You’ll see that rates of traffic can be a bit misleading – not all continents have the same population. That’s why I included the rate of internet penetration within the continents. A low rate of penetration tells you a lot about how the digital divide which is a very real problem. More on that later this week when we will address the digital divide directly. For now, it’s enough just to notice the difference in looking at the flashy, glossy Akamai graphic and the simple bar graphs. I don’t know about you, but I quite enjoyed playing with the Akamai graphic and encourage interactivity. Still, the combination of these two bar graphs above gave me a clearer answer to the big question about who in the world has access to the internet in the first place.

Relevant Resources

Akamai – Data Visualizations

The Berkman Center for Internet and Society at Harvard University School of Law.

Deibert, Ronald, Palfrey, John; Rohozinsky, Rafal; and Zittrain, Jonathan (2008) Access Denied: The Practice and Policy of Global Internet Filtering Cambridge, MIT Press.

Internet World Stats

Turkle, Sherry. (1984) The Second Self: Computers and the Human Spirit Cambridge, MIT Press.

What Works

Click on the link in the caption to go to Aaron Koblin’s site and watch the animation. It’s mesmerizing and I ended up watching it more than once, trying to pick out the patterns. And, in fact, what works about this approach is it’s ability to help quickly identify patterns. Generally speaking, data that is dynamic (usually the change is happening over time, as in this case) is data that may lend itself to this sort of pattern recognition analysis via visualization.

As you watch the whole visualization, you’ll see that Aaron Koblin experimented with three different ways of displaying the same data. He starts with impermanent white lines over a dark background, then globs of oil-ish substance over a white background, then he applies color to the original white-on-black version. I like the political implication of using the oily blobs – that is what we are collectively doing when we’re flying – burning up vast quantities of fossil fuels by using just about the least fuel-efficient form of transportation we’ve got. Vehicles for traveling outside earth’s orbit are even less efficient. I still think the white-on-black version works best because I couldn’t figure out what the colors represented.

I love the total flight counter and the running clock. Adds a great deal of contextual information very subtly.

What Needs Work

I think this animation does a great job of showing what it sets out to show – the flight patterns in the US over a 24 hour period. If there was an intention to include data about the environmental cost, I would have liked something that isn’t quite as subtle as showing the patterns using blobs of oil-like substance. But modeling that sort of data would be even more complicated than what was done here because it would count on knowing how big each plane was – jets use more fuel than smaller planes – and some estimate of how heavy it was – full flights use more fuel than empty ones.

I also wanted to know if this represents all passenger and cargo flights, or if it is just passenger flights?

Relevant Resources

Aaron Koblin’s website and a link to the specific animation related to this post.

For more on globalisation, see Saskia Sassen who was interviewed about her work by John Sutherland at the Guardian in 2004.

For more on the relationship between aircraft and climate change see this slightly outdated 2001 report from the Intergovernmental Panel on Climate Change (UNEP)