So admittedly this post should have been up last week, but I liked the lunar cycle one too much. But today is Friday and who cares. We made it to the end of the week.
In the wake of the shootings last week, someone on Twitter posed the question:
Legit question for rural Americans – How do I kill the 30-50 feral hogs that run into my yard within 3-5 mins while my small kids play?
And with that the Internet was off. Memes exploded across the social media verse. Thankfully the Washington Post took it seriously and found data on the expanding footprint of hogs in the United States.
Pig problems
The article also points out, however, that the firearm that prompted the discussion, the now infamous AR-15, would also be a poor choice against feral hogs as its too small a calibre to effectively deal with the animals.
Credit for the piece goes to the US Department of Agriculture.
As all my readers probably know, I love London. And in loving London, I love the Tube and the Oyster Card and all that goes along with Transport for London. But, I have noticed that sometimes when I take the Underground, there are segments where it gets a bit loud, especially with the windows open. The Economist covered this in a recent article where they looked at some data from a London-based design firm that makes noise protective gear. (For purposes of bias, that seems important to mention here.)
The data looks at decibels in a few Underground lines and when the levels reach potentially harmful levels. I took a screenshot of the Bakerloo line, with which I am familiar. (At least from Paddington to Lambeth.) Not surprisingly, there are a few segments that are quite loud.
I definitely recall it being loud
I like this graphic—but like I said about bias, I’m biased. The graphic does a good job of using the above the 85-decibel line area fill to show the regions where it gets loud. And in general it works. However, if you look at the beginning of the Bakerloo line noise levels the jumps up in down in noise levels, because they happen so quickly in succession, begin to appear as a solid fill. It masks the importance of those periods where the noise levels are, in fact, potentially dangerous.
I have had to deal with this problem often in my work at the Fed, where some data over decades is available on a weekly basis. One trick that works, besides averaging the data, is thinning out the stroke of the line so the overlaps do not appear so thick. It could make it difficult to read, but it avoids the density issues at the beginning of that chart.
All in all, though, I would love a London-like transport system here in Philly. I’d rather some loud noises than polluting cars on the road.
Credit for the piece goes to the Economist Data Team.
If you haven’t heard, President Trump wants to buy Greenland from Denmark. So is Greenland going to beat Puerto Rico to joining the Union as the 51st state?
No.
Not even close.
It would be the smallest state in terms of population, but also one of the smallest US territories. But in terms of area, Greenland dwarfs every state but Alaska. Though it still beats Alaska by almost 50% of its land area.
It’s like a super-charged Seward’s Folly
I had hoped to include some more economic data, but that will have to wait for a different post. Acquiring the population data was actually the most difficult—the US Census Bureau does not actually have easy to access data on the populations of US territories not called Puerto Rico.
Moving away from climate change now, we turn to the lovely land of Afghanistan. While the Trump administration continues to negotiate with the Taliban in hopes of ending the war, the war continues to go worse for Afghanistan, its government, and its allies, including the United States.
It is true that US and NATO ally deaths are down since the withdraw of combat troops in 2014. But, violence and sheer deaths are significantly up. And as this article from the Economist points out, the deaths in Afghanistan are now worse than they are in Syria.
The beginning of the article uses a timeline to chart the history of Afghan conflicts as well as the GDP and number of deaths. And it is a fascinating chart in its own right. But I wanted to share this, a small multiples featuring graphic looking at the geographic spread of deaths throughout the country.
Getting hotter (because red obviously means heat)
It does a nice job by chunking Afghanistan into discrete areas shaped as hexagons and bins deaths into those areas. All the while, the shape remains roughly that of Afghanistan with the Hindu Kush mountain range in particular overlaid. (Though, I am not sure why it is made darker in the 2003–04 map.)
To highlight particular cities or areas, hexagons are outlined to draw attention to the population centres of interest. But overall, the rise in violence and deaths is clear and unmistakable. And it has spread from what was once pockets in the south to the whole of the country that isn’t mountains or deserts.
Tamerlane would be proud.
Credit for the piece goes to the Economist graphics department.
Last week we looked at a few posts that showed the future impact of climate change at both a global and US-level scale. In the midst of last week and those articles, the Washington Post looked backwards at the past century or so to identify how quickly the US has changed. Spoiler: some places are already significantly warmer than they have been. Spoiler two: the Northeast is one such place.
The piece is a larger and more narrative article using examples and anecdotes to make its point. But it does contain several key graphics. The first is a big map that shows how temperature has changed since 1895.
The Southeast is an anomaly, but its warming has accelerated since the 1960s
The map does what it has to and is nothing particularly fancy or groundbreaking—see what I did there?—in design. But it is clear and communicates effectively the dramatic shifts in particular regions.
The more interesting part, along with what we looked at last week, is the ability to choose a particular county and see how it has trended since 1895 and compare that to the baseline, US-level average. Naturally, some counties have been warming faster, others slower. Philadelphia County, the entirety of the city, has warmed more than the US average, but thankfully less than the Northeast average as the article points out.
This ain’t so good
But, not to leave out Chicago as I did last week, Cook County, Illinois is right on line with the US average.
Nor is this, but it’s average
But the big cities on the West Coast look very unattractive.
Tinseltown is out of the question
The interactive piece does a nice job clearly focusing the user’s attention on the long run average through the coloured lines instead of focusing attention on the yearly deviations, which can vary significantly from year to year.
And for those Americans who are not familiar with Celsius, one degree Celsius equals approximately 1.8º Fahrenheit.
Overall this is a solid piece that continues to show just what future generations are going to have to fix.
Credit for the piece goes to Steven Mufson, Chris Mooney, Juliet Eilperin, John Muyskens, and Salwan Georges.
Yesterday in the early hours of the morning was technically the latest full moon. And so since today is Friday and we all made it to the end of the week, it seems like a good time to let xkcd educate us all on lunar periodicity.
Yesterday we looked at a nice piece from the BBC showing how big cities across the world will warm from the impact of climate change. It did a really nice job of showcasing the numbers. But it was admittedly number heavy. (And for the Americans in my audience, you probably were left out in the…cold…because the rest of the world uses Celsius to talk temperature.)
But this piece from the University of Maryland is something I have been raving about for weeks now. Generally speaking, people are able to better internalise data and information when they can compare it to something tangible or familiar. And degrees of Celsius, whilst accurate, fail to do that. So this piece takes their 2080 forecast and compares it to today, but in terms of place.
Ew. Just eeww.
The above map is for Philadelphia. It shows how by 2080, according to a current emissions model, the city’s climate will best resemble that of Memphis, Tennessee and the lower Mississippi River Valley. Or, similar to the tidal regions of North Carolina. Having been to Memphis in the summer once, none of those are pleasant comparisons.
And for those of you in Chicago, it does not get a whole lot better.
Not as ew-y. But still ew.
So while these might not be as bad, it still is a swath of the plains and the lower Ohio River Valley. And…yes, a little like today’s climate here in Philadelphia.
From a design standpoint, I probably would have used a light or greyed out map. The colours used to represent the topography are too similar to those used to define the similarity. And that can make it tricky to read.
But the true strength of this piece is the designers’ ability to link tomorrow’s climate to today’s by use of space. And as I said at the beginning, I have been talking about this piece offline for weeks. And I likely will for weeks to come.
Credit for the piece goes to Matthew C. Fitzpatrick and Robert R. Dunn .
In Philadelphia, this summer has been warmer than average. But with most recent years being warmer than average, that might not mean much. However, a valid question is that with climate change, how much warmer will the city get on average? The BBC recently published an article that explored the temperature changes in cities around the world according to several different models for best to worst case scenarios.
The raw data so to speak
It does a nice job via scrolling of showing how the averages work as a rolling average and the increase over time. It runs through each scenario, from best case to worst case, as a dotted line and then plots each in comparison to each other to show the range of possible outcomes.
Ew. Just ew.
I know that dark or black background is in style for big pieces. But I still do not love them. Thankfully the choice of these two colours work here. The dotted lines also work for showing the projections. And in the intermediate steps, not screencaptured, the previous projections go dark and only the current one is highlighted.
Thankfully the text boxes to the right capture the critical numbers: the actual projection numbers for the monthly average. And they tie them to the lines via the colours used.
Not shown here are a few other elements of the piece. The top of the article starts with a spinning globe that shows how the average temperature across the globe has already changed. Spoiler: not well. While the spinning globe adds some interactivity to the article, it by definition cannot display the entire world all at once, like flat, two-dimensional projections do. This makes it difficult to see impacts across the globe simultaneously. A more standard projection map could have worked really well.
Lastly, the article closes with a few stories about specific locations and how these temperature increases will impact them. These use more illustrations and text. The exception, however, is a graphic of the Arctic that shows how summer sea ice coverage has collapsed over the last few decades.
Overall this is a strong piece that shows some global impacts while allowing the user to dive down into the more granular data and see the impact on some of the world’s largest cities.
Credit for the piece goes to BBC Visual and Data Journalism team.
Today we look at a piece from the Guardian about the blossoming of some cities from, essentially, out of nowhere. Think similar to how there is really no reason for Las Vegas or Phoenix to exist—cities of hundreds of thousands situated smack in the middle of the desert. But most of these new growth cities, cities from scratch as the Guardian calls them, are sprouting in Africa and Asia.
The piece uses two pretty straight-forward graphics to show the scale of the growth problem.
A lot of urban area growth is yet to come.
I don’t love the area chart, but even for all its flaws, it it still massively obvious just how much Africa will contribute to population growth in the coming decades. And the line chart, which I find far more effective despite its borderline spaghetti-ness, shows just how much of that growth will likely be urban in nature.
But the star of the piece, for which you will need to click over to the original article to enjoy, are the motion graphics. They capture year-by-year the satellite views showing how the cities have grown from almost nothing. This is a screencapture of Ordos, China. But go back a couple of years and it’s almost an empty desert.
Check this out from decades ago and you’ll see nothing.
Credit for the piece goes to Antonio Voce and Nick Van Mead.
A few weeks ago here in the United States, we had the mass shootings in El Paso, Texas and Dayton, Ohio. The Washington Post put together a piece looking at how mass shootings have changed since 1966. And unfortunately one of the key takeaways is that since 1999 they are far too common.
The biggest graphic from the article is its timeline.
Getting worse over time
It captures the total number of people killed per event. But, it also breaks down the shootings by admittedly arbitrary time periods. Here it looks at three distinct ones. The first begins at the beginning of the dataset: 1966. The second begins with Columbine High School in 1999, when two high school teenagers killed 13 fellow students. Then the third begins with the killing of 9 worshippers in a African Episcopal Methodist church in Charlestown, South Carolina.
Within each time period, the peaks become more extreme, and they occur more frequently. The beige boxes do a good job of calling out just how frequently they occur. And then the annotations call out the unfortunate historic events where record numbers of people were killed.
The above is a screenshot of a digital presentation. However, I hope the print piece did a full-page printing of the timeline and showed the entire timeline in sequence. Here, the timeline is chopped up into two separate lines. I like how the thin grey rule breaks the second from the third segment. But the reader loses the vertical comparison of the bars in the first segment to those in the second and third.
Later on in the graphic, the article uses a dot plot to examine the age of the mass shooters. There it could have perhaps used smaller dots that did not feature as much overlap. Or a histogram could have been useful as infrequently used type of chart.
Lastly it uses small multiples of line charts to show the change in frequency of particular types of locations.
Overall it’s a solid piece. But the timeline is its jewel. Unfortunately, I will end up talking about similar graphics about mass shootings far too soon in the future.
Credit for the piece goes to Bonnie Berkowitz, Adrian Blanco, Brittany Renee Mayes, Klara Auerbach, and Danielle Rindler.
