Last month on another Friday I shared some graphics from a video by CCP Grey that looked at the origin and history of the name Tiffany. It’s a great video and I highly recommend it. But last week he published…an addendum I guess you could call it.
The piece takes a look at a research path he took for the video. It happened to involve some history and genealogy, two things I personally enjoy, and found it to be a fascinating insight into his research process.
The screenshot above hints at the idea that sometimes work is not linear and, especially when I’m doing genealogy work, there are often tangents and dead ends. In other words, to an extent, I can relate.
Today we have a quick piece, but one that I read at the weekend, you know, the 9/11 20th anniversary one. The article served as a quick summary of the day for those who either don’t know or don’t remember. After 20 years, there are a lot of people who have come of age in a post-9/11 world that were either not born or too young to recall those before times.
And so this map helped to identify the location of the three sites impacted by the planes: the World Trade Centre in New York, the Pentagon in Washington, and a field in Shanksville, Pennsylvania.
Except look closely at the graphic.
Little is where it belongs. The World Trade Centre marker is on Sandy Hook, New Jersey. The Pentagon is nearer to Fredericksburg than Washington. And Shanksville is in Maryland.
You can leave the dots for Washington and New York, as they are correctly placed. But why not just use some typography to put the World Trade Centre beneath New York and the Pentagon beneath Washington?
What makes it peculiar is that Shanksville is in Maryland, so it’s dot is just wrong. And so here’s a rough fix for that part of the graphic.
It was just an odd graphic for an article about one of those days that will be long-remembered in history.
Credit for the piece goes to the BBC graphics department.
When the remnants of Hurricane Ida rolled through the Northeast two weeks ago, here in the Philadelphia region we saw catastrophic flooding from deluges west of the city and to the east we had a tornado outbreak in South Jersey. At a simplistic level we can attribute the differences in outcomes to the path of the storm. As Ida was no longer a hurricane she developed what we call warm and cold sectors along the frontal boundary. Long story short, we can see different types of weather in these setups with heavy rain in the cold and severe weather in the warm. And that’s what we saw with Ida: heavy, flood-causing rains in the cold sector north and west of Philadelphia and then severe weather, tornadoes, in the warm sector south and east of the city.
But I want to talk about the tornadoes, and one in particular: an EF3 tornado that struck the South Jersey town of Mullica Hill. EF3 refers to the enhanced Fujita scale that describes the severity of tornadoes. EF1s are minor and EF5s are the worst. The Philadelphia region has in the past rarely seen tornadoes, but even moderate strength ones such as an EF3 are almost unheard of in the area.
This tornado caused significant damage to the area, but was also remarkable because it persisted for 12 miles. Most tornadoes dissipate in a fraction of that length. The Mount Holly office of the National Weather Service (NWS) produced a few graphics detailing tornadoes Ida spawned. You can see from the timeline graphic that the Mullica Hill tornado was particularly long lasting in time as well as distance travelled, surviving for twenty minutes.
To briefly touch on the design of this graphic, I think it generally works well. I’m not certain if the drop shadow adds anything to the graphic and I might have used a lighter colour text label for the times as they fight with the graphical components for visual primacy. Secondly, I’m not certain that each tornado needs to be in a different colour. The horizontal rules keep each storm visually separate. Colour could have instead been used to indicate perhaps peak severity for each tornado or perhaps at specific moments in the tornadoes’ lives. But overall, I like this graphic.
NWS Mt Holly also produced a graphic specifically about the tornado detailing its path.
Here we have a graphic incorporating what looks like Google Earth or Google Maps imagery of the area and an orange line denoting the path. At various points faint text labels indicate the strength of the tornado along its path. A table to the left provides the key points.
From a graphical standpoint, I think this could use a bit more work. The orange line looks too similar to the yellow roads on the map and at a quick glance may be too indistinguishable. Compare this approach to that of the Philadelphia Inquirer in its writeup.
Here we have a map with desaturated colours with a bright red line that clearly sets itself apart from the map. I think a similar approach would have benefitted the NWS graphic. Although the NWS graphic does have a stroke weight that varies depending upon the path of the tornado.
I also have a graphic made by a guy I know who lives in the area. He took that maximum tornado width of 400 yards and used screenshots of Google Maps in combination with his own direct evidence and photos and videos from his neighbours and their social media posts to try and plot the tornado’s path more granularly. Each red mark represents storm damage and a width of 400 yards.
I’m not going to critique this graphic because he made it more for himself to try and understand how close he was to the storm. In other words, it wasn’t meant to be published. But I’m thankful he allowed me to share it with you. But even here you can see he chose a colour that contrasted strongly with the background satellite views.
All of this just goes to show you the path and devastation one tornado caused. And that one tornado was just a fraction of the devastation Ida wrought upon the Northeast let alone the rest of the United States.
A few weeks ago I wrote about the United Nation’s Intergovernmental Panel on Climate Change (IPCC) latest report on climate change, which synthesised the last several years’ data. If you didn’t see that post, suffice it to say things are bad and getting worse. At the time I said I wanted to return to talk about a few more graphics in the release. Well, here we are.
In this piece we have a map, three technically. In a set of small multiples, the report’s designers show the observed change, i.e. what’s happening today, and the degree of scientific consensus on whether humans are causing it.
What I like about this is that, first, improved data and accuracy allows for sub-continental breakdowns of climate change’s impacts. That breakdown allowed the designers to use a tilemap consisting of hexagons to map those changes.
Since we don’t look at the world in this kind of way, the page also includes a generous note where it defines all these acronyms. Of course even with those, it still doesn’t look super accurate—and that is fine, because that’s the point—so little strokes outside clusters of hexagons are labelled to further help the reader identify the geographic regions. I really like this part.
I also like how little dots represent the degree of confidence. The hexagons give enough space to include dots and labels while still allowing the colours to shine. These are really nice.
But then we get to colour, the one part of this graphic with which I’m not totally thrilled. The first map looks at temperature, specifically heat extremes. Red means increase in heat extremes and blue means decrease. Fair enough. Hatched pattern means there is low consensus and medium grey means there’s little data. I like it.
Moving to the second map we look at heavy precipitation. Green means an increase and yellow a decrease. Hatched and medium grey both mean the same as before. I like this too. Sure, with clear titling you could still use the same colours as the first map, but I’ll buy if you’re selling you want visual distinction from the red–blue map above.
Then we get to the third map and now we’re looking at drought. Hatched and grey mean the same. Good. But now we have green and yellow, the same green and yellow as the second map. Okay…but I thought the second map showed we need a visual distinction from the first? But what makes it really difficult is that in this third map we invert the meaning of green and yellow. Green now means a decrease in drought and yellow an increase.
I can get that a decrease in drought means green fields and an increase in drought means dead and dying fields, yellow or brown. And sure, red and blue relate to hot and cold. But the problem is that we have the exact same colours meaning the opposite things when it comes to precipitation.
Why not use two other colours for precipitation? You wouldn’t want to use blue, because you’re using blue in the first map. But what about purple and orange, like I often do here on Coffeespoons? This is why I don’t think the designers needed to switch up the colours from map to map. Pick a less relational colour palette, say purple and orange, and colour all three maps with purple being an increase and orange being a decrease.
Colour is my big knock on these graphics, which unfortunately could otherwise have been particularly strong. Of course, I can’t blame designers for going with red and blue for hot and cold temperatures. I’ve had the same request in my career. But it doesn’t make reading these charts any easier.
Credit for the piece goes to the IPCC graphics team.
After a rainy weekend in Philadelphia thanks to Hurricane Henri, we are bracing for another heat wave during the middle of this week. Of course when you swelter in the summer, you seek out shade. But as a recent article in the Philadelphia Inquirer pointed out, not all neighbourhoods have the same levels of tree cover, or canopy.
From a graphics standpoint, the article includes a really nice scatter plot that explores the relationship between coverage and median household income. It shows that income correlates best with lack of shade rather than race. But I want to focus on a screenshot of another set of graphics earlier on in the article.
I enjoyed this graphic in particular. It starts with a “simple” map of tree coverage in Philadelphia and then overlays city zip codes atop that. Two zip codes in particular receive highlights with bolder and larger type.
Those two zip codes, presumably the minimum and maximum or otherwise broadly representative, then receive call outs directly below. Each includes an enlarged map and then the data points for tree cover, median income, and then Black/Latino percentage of the population.
I don’t think the median income needs to be in bar chart form here, especially given the bars do not line up so that you can easily compare the zip codes. The numbers would work well enough as factettes or perhaps a small dot plot with the zip codes highlighted could work instead.
Additionally, the data labels would be particularly redundant if a small scale were used instead. That would work especially well if the median income were moved to the lowest place in the table and the share charts were consolidated in one graphic. Conceptually, though, I enjoy the deep dive into those two zip codes.
Then I wanted to highlight some great design work on the maps. Note how in particular for Chestnut Hill, 19118, the outline of the zip code is largely in a thicker, black stroke than the rest of the map. At the upper right, however, you have two important roads that define the area and the black stroke breaks at those points so the roads can be clearly and well labelled. The other map does the same thing for two roads, but their breaks are shorter as the roads run perpendicular to the border.
Overall this was just a great piece to read and I thoroughly enjoyed the graphics.
After twenty years out of power, the Taliban in Afghanistan are back in power as the Afghan government collapsed spectacularly this past weekend. In most provinces and districts, government forces surrendered without firing a shot. And if you’re going to beat an army in the field, you generally need to, you know, fight if you expect to beat them.
I held off on posting anything about the Taliban takeover of Afghanistan simply because it happened so quick. It was not even two months ago when they began their offensive. But whenever I started to prepare a post, things would be drastically different by the next morning.
And so this timeline graphic from the BBC does a good job of capturing the rapid collapse of the Afghan state. It starts in early July with a mixture of blue, orange, and red—we’ll come back to the colours a bit later. Blue represents the Afghan government, red the Taliban, and orange contested areas.
The graphic includes some controls at the bottom, a play/pause and forward/backward skip buttons. The geographic units are districts, sub-provincial level units that I would imagine are roughly analogous to US counties, but that’s supposition on my part. Additionally the map includes little markers for some of the country’s key cities. Finally in the lower right we have a little scorecard of sorts, showing how many of the nearly 400 districts were in the control of which group.
Skip forward five weeks and the situation could not be more different.
Almost all of Afghanistan is under the control of the Taliban. There’s not a whole lot else to say about that fact. The army largely surrendered without firing a shot. Though some special forces and commando units held out under siege, notably in Kandahar where a commando unit held the airport until after the government fell only to be evacuated to the still-US-held Hamid Karzai International Airport in Kabul.
My personal thoughts, well you can blame Biden and the US for a rushed US exodus that looks bad optically, but the American withdrawal plan, initiated by Trump let’s not forget, counted on the Afghan army actually fighting the Taliban and the government negotiating some kind of settlement with the Taliban. Neither happened. And so the end came far quicker than anyone thought possible.
But we’re here to talk graphics.
In general I like this. I prefer this district-level map to some of the similar province-level maps I have seen, because this gives a more granular view of the situation on the ground. Ideally I would have included a thicker line weight to denote the provinces, but again if it’s one or the other I’d opt for district-level data.
That said, I’d probably have used white lines instead of black. If you look in the east, especially south and east of Kabul, the geographically small areas begin to clump up into a mass of shapes made dark by the black outlines. That black is, of course, darker than the reds, blues, and yellows. If the designers had opted for white or even a light shade of grey, we would enhance the user’s ability to see the district-level data by dropping the borders to the back of the visual hierarchy.
Finally with colours, I’m not sure I understand the rationale behind the red, blue, yellow here. Let’s compare the BBC’s colour choice to that of the Economist. (Initially I was going to focus on the Economist’s graphics, but last minute change of plans.)
Here we see a similar scheme: red for the Taliban, blue for the government. But notably the designers coloured the contested areas grey, not yellow. We also have more desaturated colours here, not the bright and vibrant reds, blues, and yellows of the BBC maps above.
First the grey vs. yellow. It depends on what the designers wanted to show. The grey moves the contested districts into the background, focusing the reader’s attention on the (dwindling) number of districts under government control. If the goal is to show where the fighting is occurring, i.e. the contest, the yellow works well as it draws the reader’s attention. But if the goal is to show which parts of the country the Taliban control and which parts the government, the grey works better. It’s a subtle difference, I know, but that’s why it would be important to know the designer’s goal.
I’ll also add that the Economist map here shows the provincial capitals and uses a darker, more saturated red dot to indicate if they’d fallen to the Taliban. Contrast that with the BBC’s simple black dots. We had a subtler story than “Taliban overruns country” in Afghanistan where the Taliban largely did hold the rural, lower populated districts outside the major cities, but that the cities like the aforementioned Kandahar, Herat, Mazar-i-Sharif held out a little bit longer, usually behind commando units or local militia. Personally I would have added a darker, more saturated blue dot for cities like Kabul, which at the time of the Economist’s map, was not under threat.
Then we have the saturation element of the red and blue.
Should the reds be brighter, vibrant and attention grabbing or ought they be lighter and restrained, more muted? It’s actually a fairly complex answer and the answer is ultimately “it depends”. I know that’s the cheap way out, but let me explain in the context of these maps.
Choropleth maps like this, i.e. maps where a geographical unit is coloured based on some kind of data point, in this instance political/military control, are, broadly speaking, comprised of large shapes or blocks of colour. In other words, they are not dot plots or line charts where we have small or thin instances of colour.
Now, I’m certain that in the past you’ve seen a wall or a painting or an advert for something where the artist or designer used a large, vast area of a bright colour, so bright that it hurt your eyes to look at the area. I mean imagine if the walls in your room were painted that bright yellow colour of warning signs or taxis.
That same concept also applies to maps, data visualisation, and design. We use bright colours to draw attention, but ideally do so sparingly. Larger areas or fields of colours often warrant more muted colours, leaving any bright uses to highlight particular areas of attention or concern.
Imagine that the designers wanted to highlight a particular district in the maps above. The Economist’s map is better designed to handle that need, a district could have its red turned to 11, so to speak, to visually separate it from the other red districts. But with the BBC map, that option is largely off the table because the colours are already at 11.
Why do we have bright colours? Well over the years I’ve heard a number of reasons. Clients ask for graphics to be “exciting”, “flashy”, “make it sizzle” because colours like the Economist’s are “boring”, “not sexy”.
The point of good data visualisation, however, is not to make things sexy, exciting, or flashy. Rather the goal is clear communication. And a more restrained palette leaves more options for further clarification. The architect Mies van der Rohe famously said “less is more”. Just as there are different styles of architecture we have different styles of design. And personally my style is of the more restrained variety. Using less leaves room for more.
Note how the Economist’s map is able to layer labels and annotations atop the map. The more muted and desaturated reds, blues, and greys also allow for text and other artwork to layer atop the map but, crucially, still be legible. Imagine trying to read the same sorts of labels on the BBC map. It’s difficult to do, and you know that it is because the BBC designers needed to move the city labels off the map itself in order to make them legible.
Both sets of maps are strong in their own right. But the ultimate loser here is going to be the Afghan people. Though it is pretty clear that this was the ultimate result. There just wasn’t enough support in the broader country to prop up a Western style liberal democracy. Or else somebody would have fought for it.
Credit for the BBC piece goes to the BBC graphics department.
Credit for the Economist piece goes to the Economist graphics department.
Happy Friday, all. We made it through another week of Covid, vaccinations, asteroids, and all that pleasant stuff. So let’s end with an upbeat note.
Over on YouTube there’s a channel I have long enjoyed, CCP Grey, who creates videos about, well lots of things, but sometimes really interesting historical, geographical, and political topics.
This week he released a video about Tiffany. As in the name Tiffany.
In addition to some great 80s aesthetics, the video touches on a couple of things that particularly interest me.
You see names are an important part of genealogical research. After all, almost all of us have names. (Some infants died without names.) Now in my family, on both my mother’s and father’s side I have a lot of Johns. In fact, I broke a line of five consecutive John Barrys. But occasionally a family will have a rarer or more uncommon name that allows you to trace that individual and therefore his or her family through time and space/place.
Grey tracks the history of the name Tiffany from its possible origin to some reasons for its popularity in the 1980s. And that includes some great graphics like this chart tracking the number of children with the name.
In the screenshot above you can see one thought he has on why the name took off in the latter half of the 20th century after languishing for centuries.
But he also examined the family history of one Tiffany and how that became important in the cultural zeitgeist. And to do so he used a family tree.
It’s a nine-minute long video and well worth your time.
I think what’s interesting to consider, however, is how this story could be told for many if not most names. There’s a reason they exist and how, by pure happenstance, they survive and get passed down family lines.
Though I have to say I did a quick search in my family tree and I have not a single Tiffany.
Yesterday I wrote about some new ICBM silos China is building in its western desert. These things clearly interest me and so I was doing a little more digging when I found this even more recent article, this one from the BBC about an entirely different ICBM silo field that China is building in another western desert.
In terms of data visualisation and information design, we are looking at the same kind of graphic: an annotated satellite photograph. But the story it paints is the same: China is rapidly expanding its nuclear missile arsenal.
Similar to the earlier piece we see dots to indicate missile silo construction sites. But the Federation of American Scientists noted these silos appear to be at earlier phase of the construction process given that sites were still being cleared and prepared for construction activity.
But put it together with the publicly available information from yesterday and, again, we can only draw the conclusion that China wants to greatly increase its nuclear arsenal. And like yesterday we’re left with the same question:
How will the United States and her allies respond?
Credit for the piece goes to the Federation of American Scientists.
We can move from the microscopic things that will kill us to the very big things that will kill us. Nuclear missiles.
Because satellite photography from late June indicated that China is presently building over 100 ICBM silos in its western deserts. China has long had nuclear weapons, but has also long kept its arsenal small, compared to the two nuclear behemoths: the United States and the Soviet Union/Russia. But you don’t begin building over 119 missile silos unless you intend to build ICBMs.
To be clear, this doesn’t mean that China will build 119 missiles. More than likely it’ll be a very expensive and potentially deadly shell game. How many missiles are underneath the silo covers? Can you keep track of them? But even if China builds a fraction of 100, modern ICBMs come with multiple independent reentry vehicles (MIRVs) that allow a single missile to target several cities independently.
We also know that China has been building shorter and more intermediate range ICBMs. But some of those are thought to be equipped with conventional warheads, designed to target and sink American supercarriers in the Pacific. The goal to deny American sea and airpower effective bases to defend Taiwan or other allies in the South China Sea.
We know about this most recent buildup because of a Washington Post article that used satellite photography to pinpoint those new silos.
Of course this isn’t news to the defence and intelligence agencies. For sometime now they’ve been warning of China’s build-out of its military capacities. The question will be is how does the United States and her allies respond?
Credit for the piece goes to Planet/Center for Nonproliferation Studies.
For the last two days Philadelphia and much of the East Coast suffered from a heavy haze of smoke that blanketed the region. This wasn’t just any smoke, however, but smoke from the wildfires on the West Coast. This post isn’t about the wildfires, but rather something that exacerbated them. We are talking about the heat domes that formed earlier this summer. The ones that melted trolley cables in Portland.
This was a nice print graphic in the Guardian Weekly, a magazine to such I subscribe that had several articles about the domes.
It does a nice job of showing the main components of the story and sufficiently simplifying them to make them digestible. One quibble, however, is how in the second map how oddly specific the heat dome is depicted.
The first graphic in particular is more of an abstraction and simplified illustration. But here we have contours and shapes that seem to speak with precision about the location of this heat dome. It also contains shades of red that presumably indicate the severity of the heat.
There’s nothing wrong with that, but it stuck me as odd juxtaposed against the top illustration.
Credit for the piece goes to the Guardian Weekly graphics department.