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.
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.)
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.
Credit for the piece goes to Haeyoun Park.
Following on last week’s posts on immigration 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.
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?
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.
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.
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.
Credit for the piece goes to Wolfram Alpha.
Following from yesterday’s post about the undocumented immigrant paths to citizenship, today’s post is a graphic from a New York Times article that looks at the integration of Latinos into the United States vis-a-vis all immigrants.
Credit for the piece goes to the New York Times.
Continuing this week’s map theme, we have an example of a cartogram from the New York Times. This piece supplements an article about how some manufacturing companies are starting to look away from China as a place for their facilities. There are two maps, the first (not shown here) looks at economic output overall. The second (below) takes that output and accounts for population.
Hexagons are used instead of the more familiar squares to represent 500,000 people and the colour is the GDP per capita. The text accompanying the graphic explains how this is a measure of economic potential being (or not being) realised. But what the hexagons allow the map to do is better represent the shapes of the countries. Squares, more common in cartograms, create awkward box-like outlines of countries. That would be fine if countries were often shaped like squares, but they are not.
I am not often a fan of cartograms, but I find this one well executed and the annotations and explanatory text make what might otherwise be confusing far simpler to understand. All in all, a solid piece.
Credit for the piece goes to Mike Bostock and Keith Bradsher.
Keeping with maps, they can be useful, but all too often people fall back upon them because it is a quick and easy way of displaying data for geographic entities. This graphic from the New York Times on ADHD is not terribly complex, but it uses a map effectively.
The article discusses how ADHD rates among states vary, but are still higher in the South. The map supports that argument. Consider how it would be different if every other state were darkened to a different shade of purple. There would be neither rhyme nor reason as to why the map was being used.
A subtle point worth noting is that only the states falling into the highest bin are labelled. Those are the states that best support the story. The remainder of the states are left unlabelled so as not to distract from the overall piece.
Credit for the piece goes to the New York Times.
When we talk about new rail projects, or even highway or airport expansions, we like to include maps of new routes and destinations. In that sense this map from the New York Times is not new. However, we often forget in such visualisations that we have the opportunity to add layers of information that show why these expansions are beneficial.
Here, note in particular how the proposed improvement projects link the dense corridor of urban settlements stretching from Boston through Worcester to Springfield. Additional lines connect more distant southerly cities such as Fall River to Boston. These might normally be seen as dots at ends of a line, but by showing the density of the population in these corridors, readers can understand how the proposed lines might benefit more people than just those living at the dots.
Credit for the piece goes to the New York Times.
Today’s post comes via my coworker Jonathan and his subscription to National Geographic. The spread below looks at the gap in life expectancy between men and women in the United States. Outliers are highlighted by drawing lines to the counties in question while the same colour scale is used on a smaller map to look at historic data. And of course for those concerned about how the US places amongst its piers on the international stage, a small selection of countries are presented beneath in a dot plot that looks at the differences and averages.
Credit for the piece goes to Lazaro Gamio.
