Autumn arrived this week in Philadelphia. And with the cooler weather came blustery winds blowing yellowing leaves from city trees. The yellows and reds of trees beneath blue skies makes for some great photography. But what is really going on? Thankfully, the Washington Post published an article exploring where and why the leaves change colour (or don’t).
The star of the piece is the large map of the United States that shows the dominant colours of forests.
Little illustrations and annotations dot the map showing how particular trees (whose leaf shapes are shown) turn particular colours. The text in the piece elaborates on that and explains what is going on with pigments in the leaves. It adds to that how weather can impact the colour change.
Later on in the piece, a select set of photos for specific locations show at a more micro-level, how and where leaf colours change.
Overall, a solid piece for those of you who enjoy leaf peeping to read before this weekend.
Credit for the piece goes to Lauren Tierney and Joe Fox.
The UN climate summit begins in New York today. So let’s take a look at another data visualisation piece exploring climate change data. This one comes from a Washington Post article that, while largely driven by a textual narrative, does make use of some nice maps.
There is nothing too crazy going on with the actual map itself. I like the subtle use here of a stepped gradient for the legend. This allows for a clearer differentiation between adjacent regions and just how, well, bad things have become.
But where the piece shines is about halfway through. It takes this same map and essentially filters it. It starts with those regions with temperature changes over 2ºC. Then it progressively adds slightly less hotter regions to the map.
It’s a nice use of scrolling and filtering to highlight the areas worst impacted and then move down the horrible impact scale. And because this happens in the middle of the piece, giving it the full column width (online) allows the reader to really focus on the impacts.
Credit for the piece goes to Chris Mooney and John Muyskens.
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.
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.
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 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.
But, not to leave out Chicago as I did last week, Cook County, Illinois is right on line with the US average.
But the big cities on the West Coast look very unattractive.
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.
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.
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.
Yesterday we looked at the New York Times coverage of some water stress climate data and how some US cities fit within the context of the world’s largest cities. Well today we look at how the Washington Post covered the same data set. This time, however, they took a more domestic-centred approach and focused on the US, but at the state level.
Both pieces start with a map to anchor the piece. However, whereas the Times began with a world map, the Post uses a map of the United States. And instead of highlighting particular cities, it labels states mentioned in the following article.
Interestingly, whereas the Times piece showed areas of No Data, including sections of the desert southwest, here the Post appears to be labelling those areas as “arid area”. We also see two different approaches to handling the data display and the bin ranges. Whereas the Times used a continuous gradient the Post opts for a discrete gradient, with sharply defined edges from one bin to the next. Of course, a close examination of the Times map shows how they used a continuous gradient in the legend, but a discrete application. The discrete application makes it far easier to compare areas directly. Gradients are, by definition, harder to distinguish between relatively close areas.
The next biggest distinguishing characteristic is that the Post’s approach is not interactive. Instead, we have only static graphics. But more importantly, the Post opts for a state-level approach. The second graphic looks at the water stress level, but then plots it against daily per capita water use.
My question is from the data side. Whence does the water use data come? It is not exactly specified. Nor does the graphic provide any axis limits for either the x- or the y-axis. What this graphic did make me curious about, however, was the cause of the high water consumption. How much consumption is due to water-intensive agricultural purposes? That might be a better use of the colour dimension of the graphic than tying it to the water stress levels.
The third graphic looks at the international dimension of the dataset, which is where the Times started.
Here we have an interesting use of area to size population. In the second graphic, each state is sized by population. Here, we have countries sized by population as well. Except, the note at the bottom of the graphic notes that neither China nor India are sized to scale. And that make sense since both countries have over a billion people. But, if the graphic is trying to use size in the one dimension, it should be consistent and make China and India enormous. If anything, it would show the scale of the problem of being high stress countries with enormous populations.
I also like how in this graphic, while it is static in nature, breaks each country into a regional classification based upon the continent where the country is located.
Overall this, like the Times piece, is a solid graphic with a few little flaws. But the fascinating bit is how the same dataset can create two stories with two different foci. One with an international flavour like that of the Times, and one of a domestic flavour like this of the Post.
Credit for the piece goes to Bonnie Berkowitz and Adrian Blanco.
Two weeks ago the Washington Post published a fascinating article detailing the prescription painkiller market in the United States. The Drug Enforcement Administration made the database available to the public and the Post created graphics to explore the top-line data. But the Post then went further and provided a tool allowing users to explore the data for their own home counties.
The top line data visualisation is what you would expect: choropleth maps showing the prescription and death rates. This article is a great example of when maps tell stories. Here you can clearly see that the heaviest hit areas of the crisis were Appalachia. Though that is not to say other states were not ravaged by the crisis.
For me, however, the true gem in this piece is the tool allowing you the user to find information on your county. Because the data is granular down to county-level information on things like pill shipments from manufacturer to distributor, we can see which pharmacies were receiving the most pills. And, crucially, which manufacturers were flooding the markets. For this screenshot I looked at Philadelphia, though I only moved here in 2016, well after the date range for this data set.
You can clearly see, however, the designers chose simple bar charts to show the top-five. I don’t know if the exact numbers are helpful next to the bars. Visually, it becomes a quick mess of greys, blacks, and burgundies. A quieter approach may have allowed the bars to really shine while leaving the numbers, seemingly down to the tens, for tables. I also cannot figure out why, typographically, the pharmacies are listed in all capitals.
But the because I lived in Chicago for most of the crisis, here is the screenshot for Cook County. Of course, for those not from Chicago, it should be pointed out that Chicago is only a portion of Cook County, there are other small towns there. And some of Chicago is within DuPage County. But, still, this is pretty close.
In an unrelated note, the bar charts here do a nice job of showing the market concentration or market power of particular companies. Compare the dominance of Walgreens as a distributor in Cook County compared to McKesson in Philadelphia. Though that same chart also shows how corporate structures can obscure information. I was never far from a big Walgreens sign in Chicago, but I have never seen a McKesson Corporation logo flying outside a pharmacy here in Philadelphia.
Lastly, the neat thing about this tool is that the user can opt to download an image of the top-five chart. I am not sure how useful that bit is. But as a designer, I do like having that functionality available. This is for Pennsylvania as a whole.
Credit for the piece goes to Armand Emamdjomeh, Kevin Schaul, Jake Crump and Chris Alcantara.
Happy Friday, all. We made it to the end of the week. Though if you are like me, i.e. living on the East Coast, welcome to Hell. As in so hot and humid.
So last month President Trump visited the United Kingdom on a state visit. He drew attention to himself not just because of his rhetoric, but also for his fashion choices. Consequently, the Washington Post published a piece about those fashion choices from the perspective of a professional tailor.
The overall piece is well worth a read if you find presidential fashion fascinating. But how does it qualify for Coffeespoons? A .gif that shows how Trump would look in a properly tailored suit.
Since this is a screenshot, you miss the full impact. The piece is an animation of an existing photo and how that then morphs into this for comparison’s sake.
I really enjoy the animated .gif when it works for data visualisation and story-telling.
Hurricane/tropical storm Barry has been dumping rain along the Gulf Coast for a few days now. But prior to this weekend, the biggest concern had been for the city of New Orleans, which sits besides the swollen Mississippi River. The river was running already high at 17 feet above normal, and with storm surges and tropical rain levels forecast, planners were concerned not with the integrity of the city’s levee system, rebuilt in the aftermath of Hurricane Katrina, but simply whether they would be tall enough.
So far, they have been.
The Washington Post tracked Barry’s course with the usual graphics showing forecast rainfall amounts and projected tracks. However, the real stunner for me was this cross section illustration of New Orleans that shows just how much of the central city sits below sea level. The cross section sits above a map of the city that shows elevation above/below sea level as well as key flood prevention infrastructure, i.e. levees and pumping stations.
The unmentioned elephant remains however. The National Oceanic and Atmosphere Administration’s extreme climate change impact forecast says the water around New Orleans might rise by nearly 13 feet by 2100. Clearly, that is still well below the 20 feet levees of today. But what if there were to be a 17 feet high Mississippi River atop the additional 13 feet? 30 feet would flood the city.
Credit for the piece goes to John Muyskens, Armand Emamdjomeh, Aaron Steckelberg, Lauren Tierney, and Laris Karklis.
This isn’t really a graphic so much as it is an x-ray photograph. But I also can’t get it out of my head. We all know that mobile phones has changed the way we live. But now we have evidence that our use of them is changing us physically. Young people are growing horns or spikes at the back of their skull. Don’t believe, photo:
The article in the Washington Post from which I screen captured the image is well worth a read. But I advise you to not do it on a mobile phone.