Yesterday scientists announced the discovery of a likely rocky planet within the habitable zone of Proxima Centauri, Sol’s (the Sun’s) nearest star. The New York Times covered the discovery with a piece full of nice explanatory graphics.
Now if we can only get onto the whole matter–anti-matter warp engine thing we could go explore the place.
Credit for the piece goes to the New York Times graphics department.
Today’s post is about religion. One of the two things you are never supposed to talk about in good company. And since the other is politics and since I cover that here frequently, let’s just go all in, shall we?
FiveThirtyEight has an interesting piece about religious diversity and a corresponding lack of religiousness. From a graphics standpoint, the central piece is this chart below.
What I would love, however, is for the plot to be interactive. It would be great to let people check out their own individual home states and see how they compare to the everyone else.
Credit for the piece goes to the FiveThirtyEight graphics department.
The Olympics are over and Team GB did rather well, coming in second in the medals table with 27 gold medals, more than they won back in 2012 when they hosted the Olympics. (See my piece four years ago where a colleague of mine and I accurately predicted the UK’s total medal count.)
Consequently the BBC put together an article with several data-driven graphics exploring the performance and underpinnings of Team GB. This screenshot captures a ranking chart that generally works well.
However, the use of the numbers within the dots is redundant and distracting. A better decision would have been to label the lines and let the eye follow the movement of the lines. A good decision, however, was to label the grey lines for those countries entering and falling out of the Top-5.
Credit for the piece goes to the BBC graphics department.
But not likely. As this FiveThirtyEight piece explains, steroids are not likely the cause of the increased power exhibited this year by Major League Baseball. The article goes into a bit of detail, but this set of small multiples does a nice job comparing several other factors that could be at play.
What I like about the piece is how each line chart is centred on the year where the factor came into play. And then to the right and left are ten years before and after. Maybe a little bit more could have been done to highlight the differing years—I admit that I missed that at first—but the concept itself is solid.
Credit for the piece goes to the FiveThirtyEight graphics department.
One of the things I like about Chicago’s WGN network is its weather blog. They often include infographic-like content to explain weather trends or stories. But as someone working in the same field of data visualisation and information design, I sometimes find myself truly confused. That happened with this piece last Friday.
The map in the upper-right in particular caught my attention and not in the good way. The overall piece discusses the heavy rainfall in the Chicago area on Thursday and the map looks at the percentage increase in extreme weather rainfall precipitation. All so far so good. But then I look at the map itself. I see blue and thing blue > water > rainfall. The darker/more the blue, the greater the increase. But, no—check out Hawaii. So blue means less rainfall. But also no, look at the Midwest and Southeast. So does green mean anything? Beyond being all positive growth, not that I can tell. As best I can tell, the colour means nothing in terms of rainfall data, but instead delineates the regions of the United States—noting of course they are not the standard US Census Bureau regions.
So here is my quick stab at trying to create a map that explains the percentage growth. I have included a version with and without state borders to help readers distinguish between states and regions.
And what is that at the bottom? A bar chart of course. After all, with only eight regions, is a map truly necessary especially when shown at such an aggregate level? You can make the argument that the extreme rainfall has, broadly speaking, benefitted the eastern half of the United States. But, personally speaking, I would prefer a map for a more granular set of data at the state or municipality level.
Credit for the piece goes to Jennifer Kohnke and Drew Narsutis.
As I stated earlier this week, I spent the first half of the week in Wisconsin research my family history. And I really will try to get to it next week. But, beyond the vital records and recorded stories, I am also intrigued by how our ethnic histories break down genetically. That gets us to today’s post from xkcd.
Today’s post features a simple set of graphics on the BBC, however the creators were actually the Asia Maritime Transparency Initiative. The background? The increasingly tense geopolitical situation in the South China Sea, where China claims numerous islands and reefs claimed by other countries—and to a smaller extent other countries make similar such claims. Just a few weeks back, the Hague ruled against Chinese claims against islands within the Philippines territorial waters. But as these graphics show, it takes more than a legal decision to effect change on the ground.
Satellite photography shows military installations on numerous Chinese-held islands. But what makes the images potent in the communicative sense is the simple overlay of white plane illustrations. They show how many fighter jets, support aircraft, patrol aircraft, &c. that China can base at the various military installations. It is a simple but incredibly effective touch.
Credit for the piece goes to the Asia Maritime Transparency Initiative.
Apologies for the lack of posts the last two days. I visited Wisconsin to trace some of the courthouse records of the Spellacys. And while I will try to return to them later next week, today we go to China.
During my recent holidays, the media made much ado about a new straddling bus in China. Except that it’s not that new. And now it might not be real or at least really viable. I recalled this graphic from 2012 via the Guardian and decided it would be relevant to try and explain how the bus should work.
Another day in Philadelphia, another post of Philly data visualisation work. Here we have a piece from 2015 that was updated earlier this spring. It looks at overdose rates in the Philadelphia region, including parts of New Jersey. It does include a map, because most pieces like this typically do. However, what I really find interesting about the piece is its use of small multiple line charts below to take a look at particular counties.
The piece overall is not bad, and the map is actually fairly useful in showing the differences between Jersey and Philadelphia (although why New Jersey is outlined in black and the Philly suburban counties are not I do not know). But I want to take a look at the small multiples of the piece, screenshot below.
You can see an interesting decision in the choice of stacked line charts. Typically one would compare death rates like for like and see whether a county is above, below, or comparable to the state, local, or national averages. But combining the three gives a misleading look at the specific counties and forces the user to mentally disentangle the graphic. I probably would have separated them into three separate lines. And even then, because of the focus on the counties, I would have shifted the colour focus to the specific counties and away from the black lines for the national average. The black is drawing more attention to the US line than to the county line.