Today’s piece is nothing more than a line chart. But in the aftermath of this past weekend’s gun violence—and the inability of this country to enact gun control legislation to try and reduce instances like them—the Economist published a piece looking at public polling on gun control legislation. Perhaps surprisingly, the data shows people are broadly in favour of more restrictive gun laws, including the outlawing of military-style, semi-automatic weapons.
These trendlines are heading in the right direction
In this graphic, we have a line chart. However the import parts to note are the dots, which is when the survey was conducted. The lines, in this sense, can be seen as a bit misleading. For example, consider that from late 2013 through late 2015 the AP–NORC Centre conducted no surveys. It is entirely possible that support for stricter laws fell, or spiked, but then fell back to the near 60% register it held in 2015.
On the other hand, given the gaps in the dataset, lines would be useful to guide the reader across the graphic. So I can see the need for some visual aid.
Regardless, support for stricter gun laws is higher than your author believed it to be.
Credit for the piece goes to the Economist graphics department.
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.
There are some clear geographic patterns to see here
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.
It could be worse
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.
Better numbers than Philly
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.
For Pennsylvania, state-wide
Credit for the piece goes to Armand Emamdjomeh, Kevin Schaul, Jake Crump and Chris Alcantara.
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.
Just explore the rails…
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.
More rail riding…
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.
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.
But all those segments?
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.
Ebola, which killed 11,000 people in West Africa in 2014 (whichIcoveredinacoupleofdifferentposts), is back and this time ravaging the Congo region, specifically the Democratic Republic of the Congo (DRC). The BBC published an article looking at the outbreak, which at 1,400 deaths is still far short of the West Africa outbreak, but is still very significant.
That’s looking like a tenuous border right now…
The piece uses a small multiples of choropleths for western Congo. The map is effective, using white as the background for the no case districts. However, I wonder, would be more telling if it were cases per month? That would allow the user to see to where the outbreak is spreading as well as getting a sense of if the outbreak is accelerating or decelerating.
The rest of the article features four other graphics. One is a line chart that also looks at cumulative cases and deaths. And again, that makes it more difficult to see if the outbreak is slowing or speeding up. Another is how the virus works and then two are about dealing with the virus in terms of suits and the containment camps. But those are graphics the BBC has previously produced, one of which is in the above links.
Credit for the piece goes to the BBC graphics department.
This piece was published Monday, so it’s one round out of date, but it still holds true. It looks at the betting odds of each of the candidates looking to enter No. 10 Downing Street. And yeah, it’s going to be Boris.
That’s a pretty sizable gap
The thing that strikes me as odd about this piece however, is note the size of the circles. Why are they larger for Boris Johnson and Rory Stewart? It cannot be proportional to their odds of victory or else Boris’ head would be…even bigger. Is that even possible? Maybe it relates to their predicted placement of first and second, the two of which go to the broader Tory party for a vote. It’s really unclear and deserves some explanation.
The graphic also includes a standard line chart. It falls down because of spaghettification in that all those also rans have about the same odds, i.e. slim, to beat Boris.
Perhaps the most interesting thing to follow is who will be the other person on the ballot. But then who remembers Andrea Leadsom was the runner up to Theresa May?
Credit for the piece goes to the Economist graphics department.
One of the things we missed covering last week whilst I was on holiday? The dust up in the Gulf of Oman, located near the Strait of Hormuz, where two foreign ships were attacked by mines or other explosive devices. The United States blames Iran and, of course, Iran denies it. The thing is, an inordinate amount of oil flows through the Strait, connecting the petroleum-driven economies of the West to the instability in the Middle East. Thankfully we have a graphic from the Guardian to explain just what is going on there.
Not shown: the US, the EU, China, and Russia
The above is a screenshot from the article, one of several graphics. There is a stacked bar chart showing the total volume of oil in transit, and the Strait’s share of it. Spoiler: it’s significant. We all know how I feel about stacked bars: not the biggest fan.
There are, of course, locator maps showing the locations of the attacked ships. We also have some photographs showing the damage inflicted upon the tankers, as well as some evidence of what the US claims is Iranian activities. (Side note: isn’t it great that when the US really wants the world to trust its intelligence agencies the White House has been doing nothing but trashing said intelligence agencies?)
The above, however, is a simple map showing the political fault line in the Middle East. It gets to the heart of the potential conflict here being not a US vs. Iran war, but a Saudi Arabia vs. Iran war. After all, relations between the Saudis and the Trumps have warmed significantly since the Obama administration. And not shown in the map is the role of Israel, which, again has seen a significant warming in relations between Trump and Netanyahu, and which has also been quietly supporting Saudi Arabia in its undeclared war against Iran, to date fought only with proxies, most notably in Yemen.
In other words, the Middle East is a complicated and complex tinder box, built next to a few nuclear reactors, all of which just happen to sit atop vast reserves of oil and natural gas. So the best thing to do? Clearly start exploding things.
Credit for the piece goes to the Guardian graphics department.
This piece from the New York Times isn’t really even a graphic. It’s a factette, or small fact. The article is about how tariffs are raising the price of certain goods, in this case a bicycle. Tariffs do not add money to the US Treasury, they are instead an additional price paid by US consumers on goods—not services—originating from outside the US.
Thankfully I can’t ride a bike
Sometimes a big chart is not as impactful as one big number. And here, in the context of this story, a graphic showing trade flows between the US and Mexico may have been useful. But the real gut punch is showing how the tariffs on Mexico, for this one particular bike, could cost the US consumer an additional $90. A tariff is just another word for a tax paid by the American consumer.
Credit for the piece goes to the New York Times graphics department.
Last week I had three different discussions with people about some of the impact of climate change upon the United States. However, what did not really come up in those conversations was the environmental changes set to befall the United States. And by environment, I explicitly mean how the flora of the US will change.
Why? Well, as warmer climates spread north, that means tropical and subtropical plants can follow warmer temperatures northward into lands previously too cold. And they could replace the species native to those lands, who evolved adaptations for their particular climate.
Thankfully, last week the New York Times published a piece that explored how those impacts could be felt. Hardiness zones are a concept designed to tell gardeners when and where to plant certain crops. And while the US Department of Agriculture has a detailed version useful to horticulturists, the National Oceanic and Atmospheric Administration produces a very similar version for the purpose of climate studies. And when you group those hardiness levels by the forecast lowest temperatures in an area, you get this.
More palm trees?
There you have it, the forecast change to plant zones.
From a design standpoint, I like the idea of the colour shift here. However, where it breaks seems odd. Though it could be more influenced by the underlying classifications than I understand. The split occurs at 0ºF, which is well below freezing. I wonder if the freezing point, 32ºF could have been used instead. I also wonder if adding Celsius units above the same legend could be done to make the piece more accessible to a broader audience.
Otherwise, it’s a nice use of small multiples. And from the editorial design standpoint, I like how the article’s text above the graphic makes use of a six-column layout to add some dynamic contrast to what is essentially a three-column layout for the graphics.
Climate change is a thing. And facing it will require a lot of our societies. But the longer we choose not to act, the more the impact will be felt by later generations. Consequently, across the world, young students have been walking out of class to shine light on an issue on which they, as children, have little direct impact. Yet. But what about us? The ones who can vote and make lifestyle decisions?
The BBC had a piece where, after soliciting questions from their readership, they answered questions. One question being, what can individuals do to reduce their impact. And while clearly individuals need to do more than one thing, one facet can be examining one’s diet. The article included this graphic on the climate impact of various food types, vis-a-vis greenhouse gas emissions.
Is this saying I should drink more beer?
Essentially we are looking at a simplified box plot of greenhouse gas emissions per serving of food (and drink) type. The box plot looks at a range of values for a specific item. It usually shows the extremes at both ends; the range of a significant number of the data points, e.g. 80% of the set, or by decile, or by quartile; and then lastly the average, be it mean or median. Here we have only low impact, high impact, and average impact. Presumably the minimum, maximum, and then either mean or median.
And it works really well. Chocolate is a great example of how on average, chocolate isn’t terrible. But certain chocolates can have far worse ramifications than low-impact beef, or average-impact lamb and prawns. And beef is well known to be one of the most impactful types of food.
From a design standpoint, I don’t know if the colours necessarily help. The average beef impact, for example, is worse than the high-impact maximum of every other food listed. But the association of green=good and red=bad here has little value because by that logic, the average=gold beef should be red as it sits above the high-impact everything else. A less editorial choice could be made of say a light grey or blue and then have the bright colour, maybe still orange, indicate where the average sits on that spectrum.
I do like the annotations on the chart. It highlights particular stories, like the aforementioned chocolate one, that the casual, i.e. skimming, reader may miss.
I could probably do without the little food illustrations. But the designer did a good job of making them all recognisable in such a small space—far from an easy task. And being so small, they don’t really distract or take away from the whole graphic.
Overall, this is a strong graphic.
Credit for the piece goes to the BBC graphics department.
