I am a graphic designer who focuses on information design. My day job? I am the data visualisation manager for the Federal Reserve Bank of Philadelphia. (This blog is my something I do on my own time and does not represent the views of the Fed, blah blah blah legal stuff.) And with my main 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 enjoy cooking and reading and am interested in various subjects from history and geography to politics to science to the arts. And I allow all of them to influence my work.
This is a repost of sorts, but it is important. Now prime minister, Boris Johnson had an opportunity to seek a more reasonable approach to Brexit. Unfortunately, he is drawing even harder red lines than his predecessor, Theresa May. And that brings us back to my Brexit trilemma graphic from back in March.
Essentially, Johnson wants three things that are mutually—or whatever the word is for three, maybe tri-mutually—semi-exclusive. In other words, of the three red lines, the United Kingdom can only have two, because those two then make the third impossible.
I made the first version of this back in March. Sad it still applies.
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.
Today Boris Johnson begins his premiership as the next prime minister of the United Kingdom. He might not be popular with the wide body of the British population, but he is quite popular with the Conservative base.
The Economist looked at how Boris polled on several traits, e.g. being more honest than most politicians, compared to his prime minister predecessors before they entered office. And despite being broadly unpopular outside the Tories, he still polls better than most of his predecessors.
Design wise, it’s a straight-forward use of small multiples and bar charts. I find the use of the light blue bar a nice device to highlight Boris’ position amongst his peers.
But now we see where Boris goes, most importantly on Brexit.
Credit for the piece goes to the Economist graphics department.
Yesterday we looked at Billy Penn’s graphics about the cooler stations and I mentioned a few ways the graphic could be improved. So last night I created a graphic where I explored the limited scope of the data, but also showing how low the temperatures were, relative to the air temperature outside, using weather data from the National Weather Service, admittedly from Philadelphia International Airport, not quite Centre City, which I would expect to be warmer due to the urban heat bubble effect.
I opted to exclude the Patco Line since the original dataset did not include it either. However a section of it does run through Centre City and could be relevant.
Credit for the piece goes to me, though the data is all from Billy Penn and the National Weather Service.
Those of you living on the East Coast, specifically the Mid-Atlantic, know that presently the weather is quite warm outside. As in levels of dangerous heat and humidity. Personally, your author has not left his flat in a few days now because it is so bad.
Alas, not everyone has access to air conditioning in his or her abode. Consequently, they need to look to public spaces with air conditioning. Usually that means libraries or public buildings. But here in Philadelphia, have people considered the subway?
Billy Penn investigated the temperatures in Philadelphia’s subsurface stations along the Broad Street and Market–Frankford Lines—Philadelphia’s third and oft-forgot line, the Patco, was untested. What they found is that temperatures in the stations were significantly below the temperatures above ground. The Market–Frankford stations, for example, were less than 100ºF.
Of course that misses the 2nd Street station in Old City, but otherwise picks up all the Market–Frankford stations situated underground.
Then there is the Broad Street Line.
Here, I do have a question about why the line wasn’t investigated from north to south. It ran only as far north as Girard, stopping well short of north Philadelphia neighbourhoods, and then as far south as Snyder, missing both Oregon and Pattison (sorry, corporately branded AT&T) stations. The robustness of the dataset is a bit worrying.
The colours here too mean nothing. Instead blue is used for the blue-coloured Market–Frankford line and orange for the orange-coloured Broad Street line. (The Patco line would have been red.) Here was a missed opportunity to encode temperature data along the route.
Finally, if the sidewalk temperatures were measured at each station, I would want to see that data alongside and perhaps run some comparisons.
This is an interesting story, but some more exploration and visualisation of the data could have taken it to the next level.
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.
Back in April the famed Notre Dame cathedral in Paris caught fire and its roof and spire spectacularly collapsed. At the time I looked at a few different pieces, including two from the New York Times, that explored the spread of the fire. Several months later the Times has just published a look into how the firefighters saved the cathedral from collapse.
The graphics are the same amazing illustrated models from before. Now with routes taken by firefighters and coloured areas indicating key equipment used in the fight to preserve what could be saved. But the real gem in the article are a series of graphics from the firefighters themselves.
Naturally the annotations are all in French. But this French firefighter and sketch artist detailed the progress of the battle during and in the days after the fire. It makes me wish I could read French to understand the five selected sketches the Times chose to use. And I love this line from the Times.
For all the high-tech gear available to big-city fire departments, investigators still see value in old-school tools.
If you are interested in the story of how the cathedral was saved, read the lengthy article. If you just want to see some really amazing and yet wholly practical sketches, scroll through the article until you get to these gems.
Credit for the overall piece goes to Elian Peltier, James Glanz, Mika Gröndahl, Weiyi Cai, Adam Nossiter, and Liz Alderman.
This is a graphic from the Guardian that sort of mystified me at first. The article it supports details how the rising rents across England are hurting the rural youth so much so they elect to stay in their small towns instead of moving to the big city.
The first thing I noticed is that there really is no description of the data. We have a chart looking at something from 1997 and comparing it to 2018. The title is more of a sentence describing the first pair of bars. And from that title we can infer that these bars are income changes for the specified move, e.g. Sunderland to York, for the specified year. But a casual reader might not pick up on that casual description.
Then we have the issue of the bars themselves. What sort of range are we looking at? What is the min? The max? That too is implied by the data presented in the bars. Well, technically not the bars, but in the numbers at the end of each bar. I will spare you the usual rant about numbers in graphics defeating the purpose of graphics and organisation vs. visual relationship. Instead, the numbers here are essential because we can use them to suss out the scale of the grey bars. After looking at a few bars, we can tell that the white lines separating the grey boxes are most likely 10% increments. And from that we can gather the minimum is about -40% and the maximum 100%. But instead of making the reader work to figure this out, would not some min/max labels at the bottom of the chart be far clearer?
And then there is the issue of the grey boxes/bars themselves. Why are they there in the first place? If the dataset were more about an unmet value, say reservoirs in towns were only at x% of capacity, the grey bars could relate the overall capacity and the coloured bars the actual values. But here, income is not a capacity or similar type of value. It could expand well beyond the 100% or decline beyond the -40%. These bars imply the values are trapped within these ranges. I would instead drop the grey bars entirely and let the coloured bars exist on their own.
Overall this is a confusing graphic for a fascinating article. I wish the graphic had been a little bit clearer.
Credit for the piece goes to the Guardian’s graphics department.
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.
The United Kingdom is known for having a large number of accents in a—compared to the United States—relatively small space. But then you add in Ireland and you have an entirely new level of linguistic diversity. Josh Katz, who several years ago made waves for his work on the differences in the States, completed some work for the New York Times on those differences between the UK and Ireland.
Why do I bring it up? Well, your author is going on holiday again, this time back to London. I will be maybe taking some day trips to places outside the capital and maybe I will confirm some of these findings. But if you want, you can take the quiz and see where you fall compared to Katz’s findings.
And it does pretty well. It identified me as being clearly not from the British Isles.
But depending upon how you answer a particular question, the article will show you how your answer compares. Let’s take my answer for scone. In that, I am more Irish.