I am a graphic designer who focuses on information design. My day job? Well, they asked me not to say. But to be clear, this blog is my something I do on my own time and does not represent the views of…my employers. I think what I can say is that given my interest in information design—be it in the shape of clear charts, maps, diagrams, or wayfinding systems—I am fortunate that my day job focuses on data visualisation. Outside of work, I try to stay busy with personal design work. Away from the world of design, I have become an amateur genealogist and family historian. You will sometimes see that area of work bleed into my posts.
The New York Times has recently done good work with interactive infographics that weave a narrative through their chosen form of data visualisation. I covered one such work back in February that looked at girls in science. Today, a similarly structured piece looks at university admissions and graduation rates for ethnic minorities.
Admissions Gap at Universities
Navigation in the top-right guides the user through the story with key schools highlighted. Of course at any time the user can dive into the data and find specific schools that interest them. Overall the piece is less about data exploration, however, and instead merely uses the wealth of data to paint a context for the broader narrative.
Today’s post is a location map that comes from Nasa. What locations? Those of its various spacecraft, such as Rosetta and Cassini. Humanity is all over the solar system.
Right here
Admittedly, I think the map could be cleaner and clearer, but that the subject matter can be mapped is still just pretty cool.
Well, actually, your author is driving back from Ganister today. Unfortunately, while on holiday I was not working (nor was I planning to.) So while I could of run silent today, I wanted to share with all of you again a project I created last year about my return drive from Ganister. For all of who familiar with the piece, I apologise for my re-posting of previous work. For those of you unfamiliar with the work or with Ganister and its distance/remoteness, enjoy. (It’s full-size, so no click-for-higher-resolution.)
The Kentucky Derby is this weekend, but your humble author is out until next week. So here is a work from David Yanofsky at Quartz that looks at the average horse times in the one and three-quarter miles at the Kentucky Derby. (I’m a baseball guy, so ask me about Pedro’s strikeout rates in the late 1990s and I’m much better equipped with answers.)
But he takes decade-long averages and shows how horse speeds have essentially plateaued since the 1960s.
Earlier this year, the mobile phone (or cell phone for many Americans) turned 40. Today’s infographic comes from the National Post and looks at the history and the near future of the mobile phone market, mobile phones, and related technologies. A nice touch is a actual-scale drawing (best seen in print) comparing a modern iPhone to an “old school” mobile phone, as shown in the cropping of the original below.
The history of the mobile phone
Credit for the piece goes to Mike Faille and Kristopher Morrison.
Of 2048. Well, kind of. Lately the country has been talking a lot about immigration and its impacts because of this bipartisan desire to achieve some kind of result on an immigration bill working its way through the Senate. One of the common thoughts is that if we legalise a whole bunch of illegals or document most of the undocumented (I’ll leave the language for you to decide), the new American citizens will overwhelmingly vote Democratic and there goes the Republic(an Party).
Nate Silver—yes, that Nate Silver who accurately predicted the presidential results and a whole bunch of other stuff too—looked at a more complex and more nuanced set of demographic variables and found that the aforementioned argument greatly oversimplifies the results. The problem is not entirely the entry of newly documented or illegal workers. Instead, there are systemic demographic issues.
So here comes the New York Times with an excellent data explorer and forecast modeller. You can set the year to examine and then set the results of the immigration debate with how many immigrants are made legal/documented and then how many of them vote. After that you can begin to adjust population growth, voting patterns, &c. to see how those affect the elections. (The obvious caveats of acts of god, party platforms, candidates, &c. all hold.)
2048 Results
The fascinating bit is that if you keep the demographic patterns as they are currently, adjusting the immigration factors at the outset have very little impact on the results. The country is moving towards the current Democratic platform. Even if 0% of the undocumented/illegal immigrants become documented/legal, and if 0% of 0% vote, the result is still a landslide for the Democrats. The real changes begin to happen if you adjust the population growth rates of the legal/documented citizens and voters. But those patterns/behaviours are a lot more difficult to adjust since you can’t legislate people to have more babies.
All in all a fascinating piece from the New York Times. The controls are fairly intuitive, drag sliders to adjust percentages. The sliders have clear labels. And the results on the map are instantaneous. Perhaps the only quirk is that the ranges of the colours are not detailed. But that might be a function of forecasting the data so far into the future and having growing ranges of certainty.
Credit for the piece goes to Matthew Bloch, Josh Keller, and Nate Silver.
Today I have more immigration-related information graphics and data visualisation for you. Earlier this week the New York Times looked at immigration to California, but this time the focus was on Asian population growth and not Hispanic. The graphic here supports an article looking at where the growth has been focused in California. And given that emphasis, the map accompanying the article makes sense. And as the reader can clearly see, much of that growth has been centred in the San Gabriel Valley and Orange County.
Following on last week’s posts onimmigration comes today’s post on how that might impact Republican politics. Well I say might but pretty much mean definitely. The graphic comes from the Wall Street Journal and it takes a look at the demographic makeup of states, House congressional districts and then survey data on immigration broken into Republicans vs. Democrats.
The GOP's Tricky Terrain
I think the piece is a good start, but at the end of the introductory paragraph is the most salient point about the piece. And unfortunately the graphic does not wholly embody that part. Of course within limited time and with limited resources, achieving that sort of completeness is not always possible. That said I think overall the piece is successful, it just lacks that finishing graphical point.
Credit for the piece goes to Dante Chinni and Randy Yeip.
Earlier this week, Wolfram Alpha released some findings from its analytics project on Facebook. While the results offer quite a bit to digest, the use of some data visualisation makes it a little bit easier. And a lot more interesting.
The results offer quite a bit of detail on interests, relationship statuses, geographic locations, and ages. Below is just one of the small multiple sets, this one looks at the number of friends of different ages for people of different ages. Basically, how many young or old people are friends of young people? Friends of old people?
Friends of Friends for the Ages
But I was most interested in the analysis of social networks. The mosaic below is indicative of the sheer size of the survey, but also begins to hint at the variance in the social structures of the data donors.
Just Some of the Networks
While these views are all neat, where it begins to get really interesting is Wolfram Alpha’s work on classifying the different types of social networks. By aggregating and averaging out clusters, simple forms begin to emerge. And after those forms emerged, they were quantified and the results are a simple bar chart showing the distribution of the different types of networks.
Simplified Cluster Distribution
Overall, some very interesting work. But one might naturally wonder how their own networks are structured. Or just be curious to look at the data visualisation of their own Facebook profile. Or maybe only some of us would. Fortunately, you still can link your account to a Wolfram Alpha account (you have to pay for advanced features, however) and get a report. Below is the result of my network, for those who know me semi-well I have labelled the different clusters to show just how the clustering works.
The other day I posted an example of a good cartogram, actually a pair of good ones from the New York Times. Today, I wanted to share another good example. The Economist created this cartogram, map of Great Britain’s constituencies. What is perhaps most effective in this chart, even more so than in the Times’, is its use of a “traditional” map form for comparison. You quickly get a sense of how large rural Britain’s constituencies are compared to those of London.
