We are going to start this week off with a nice small multiple graphic that explores the reducing resistance to women in positions of leadership in Arab countries. The graphic comes from a BBC article published last week.
These kinds of graphics allow a reader to quickly compare the trajectory of a thing between a start and an endpoint. The drawback is it can obscure any curious or interesting trends in the midpoints. For example, with Libya, is its flat trajectory always been flat? You could imagine a steep fall off but then rapid climb back up. That would be a story worth telling, but a story obscured by this type of graphic.
I do think the graphic could use a few tweaks to help improve the data clarity. The biggest change? I would work to improve the vertical scale, i.e. stretch each chart taller. Since we care about the drop in opposition to women leaders, let’s emphasise that part of the graphic. There could be space constraints for the graphic, but that said, it looks like some of the spacing between chart header and chart could be reduced. And I think for most of the charts except for the first, the year range could be added as a data definition to the graphic and removed from each chart. Similar to how every row only once uses the vertical axis labels.
Another way this could be done is by reducing the horizontal width of each chart in an attempt to squeeze the nine from three rows down to two. That would mean two additional chart positions per row. Tight fit? Probably, but there is also some extraneous space to the right and left of each chart and a large gap between the charts themselves. This all appears to be due to those aforementioned x-axis labels. An additional benefit to reducing the horizontal dimensions of each chart is it increases the vertical depth of the chart as each line’s slope, its rise over run, sees its horizontal distance shrink.
Overall this is a really smart graphic that works well, but with a few extra tweaks could take it to the next level.
Credit for the piece goes to the BBC graphics department.
Yesterday I focused on the big graphic from the New York Times that crossed the full spread of the front/back page. But the graphic was merely the lead graphic for a larger piece. I linked to the online version of the article, but for this post I’m going to stick with the print edition. The article consists of a full-page open then an entire interior spread, all in limited colour. The remainder of the extensive coverage consists of photo essays and interviews that understandably attempt to humanise the data points, after all, each dot from yesterday represented one individual, solitary, human being. That is an important element of a story like this and other national and international tragedies, but we also need to focus on the data and not let the emotion of the story overwhelm our rational and logical analysis.
From a data visualisation standpoint the first page begins simply enough with a long timeline of the Covid-19 pandemic charting the number of absolute deaths each day. As we looked at yesterday, the absolute deaths tell part of the story. But if we were to have looked at the number of absolute cases in conjunction with the deaths, we could also see how the virus has thus far evolved to be more transmissible but less lethal. Here the number of daily deaths from Omicron surpassed Delta, but fell short of the winter peak in early 2021. But the number of cases exploded with Omicron, making its mortality rate lower. In other words, far more people were getting sick, but as far fewer were dying.
An interesting note is that if you take a look at the online version, there the designers chose a more stylised approach to presenting the data.
Here they kept the dot approach and simply stacked and reordered the dots. However, I presume for aesthetic reasons, they kept the stacking loose dots and dropped all the axis lines because it does make for a nice transition from the map to this chart. But they also dropped all headings and descriptors that tell the reader just what they are looking at. These decisions make the chart far less useful as a tool to tell the data-driven element of the story.
There are three annotations that label the number of deaths in New York, the Northeast, and the rest of the United States. But what does the chart say? When are the endpoints for those annotations? And then you can compare the scale of the y-axis of this chart and compare it to the printed version above. A more dramatic scale leads to a more dramatic narrative.
This sort of visual style of flash and fancy transitions over the clear communication of the data is why I find the print piece more compelling and more trustworthy. I find the online version, still useful, but far more lacking and wanting in terms of information design.
The interior spread is where this article shines.
From an editorial design standpoint, the symmetry works very well here. It’s a clear presentation and the white space around the graphic blocks lets that content shine as it should in this type of story. Collectively these pieces do a great job telling the story of the pandemic thus far across the nation. The graphics do not need a lot of colour and make do with sparse flash. Annotations call the reader’s attention to salient points and outliers.
From a content standpoint, I would be particularly curious if we have robust data for deaths by education level. Earlier this year I recall reading news about a study that said education best correlated to Covid cases, and I would be curious to see if that held true for deaths. Of course these charts do a great job of showing just how effective the vaccines were and remain. They are the best preventative measure we have available to us.
Here I disagree with the design decision of how to break down the states into regions. The Census Bureau breaks down the United States into four regions using the same names as in the graphic above. However, if you look closely at the inset map, you will see that Delaware, Maryland, and West Virginia in particular are included as part of the Northeast. (I cannot tell if the District of Columbia is included as part of the Northeast or South.)
Now compare that to the Census Bureau’s definition:
If you ask me to include Delaware and Maryland as part of the Northeast, well, if you’re selling it, I’ll buy it. After all, just because the Census Bureau defines the United States this way does not mean the New York Times has to. Both are connected to the Northeast Corridor via Amtrak and I-95 and are plugged into the Megalopolis economy. Maybe the Potomac should be the demarcation between Northeast and South. But I struggle to understand West Virginia. Before you go and connect it to the Northeast, I would argue that West Virginia has far more in common with the Midwest geographically, economically, and culturally.
More critically, given this issue, it strikes me as a serious problem when the online version of the chart—with the aforementioned issues—does not even include the little inset to highlight this at best unusual regional definition.
And so while I have reservations about the data—how would the data have looked if the states were realigned?—the design of the line charts overall is good.
Again, I am talking about the print version, not that online graphic. I would argue that the above screenshot is barely even a chart and more “data art” or an illustration of data. Consider here, for example, that for the South we have that muted slate blue for the dots, but the spacing and density of the dots leads to areas of lighter slate and darker slate. But a lighter slate means more space between stacked dots and darker slate means a more compact design. A lighter colour therefore pushes the “edge” of the line further up the y-axis and artificially inflates its value, not that we can understand what that value is as the “chart” lacks any sort of y-axis.
Finally the print piece has a set of small multiples breaking down deaths by income in the three largest American cities: New York, Los Angeles, and Chicago. These are just great little charts showing the correlation between income and death from Covid, organised by Zip code.
But this also serves as a stark reminder of just how much better the print piece is over the online version. Because if we take a look at a screenshot from the online article, we have a graphic that addresses all the issues I pointed out earlier.
I am left to wonder why the reader of the online version does not have access to this clearer and more accurate representation of the data throughout the piece?
To me this article is a great example of when the print piece far exceeds that of the online version. Content-wise this is a great story that needed to be told this weekend, but design wise we see a significant gap in quality from print to online. Suffice it to say that on Sunday I was very glad I received the print version.
Credit for the piece goes to Sarah Almukhtar, Amy Harmon, Danielle Ivory, Lauren Leatherby, Albert Sun, and Jeremy White.
We’re back after a nice holiday break. And one of the most fascinating things to happen was the successful—and seemingly easy, more on that in a bit—launch of the James Webb space telescope. The James Webb was developed by NASA with contributions from both the European Space Agency (ESA) and the Canadian Space Agency (CSA). Whilst it did launch behind schedule and at a price tag of $10 billion, the James Webb is the most sophisticated and complex space telescope mankind has yet launched into space. It will look backwards into time to some of the earliest stars and galaxies in the universe. It will also look at the thousands of exoplanets we have discovered in the last three decades. The instruments aboard James Webb will be able to help us identify if any of these planets have water and other ingredients necessary for life as we know it. This could be one of the most monumental space missions yet.
But James Webb’s launch was far from guaranteed. As this great article from the BBC explains, the construction, assembly, launch, and deployment were all incredibly complicated. James Webb is expected to operate for ten years before its fuel, needed to keep the telescope cold, runs out. However, the seemingly easy launch and deployment means that it used less fuel than expected. Some early reports suggest that the telescope may have some additional time left in it now before the fuel runs dry.
I encourage you to read the article, because it explains the advantages of the telescope, how it works, and its deployment with several illustrations. There are five in particular, though I’ll share only two screenshots.
The most important is this, the key distinction between Hubble and James Webb. It shows how the two space telescopes will be operating in different parts of the electromagnetic spectrum.
The graphic fakes the colours, because by definition we can only see the visible portions of the spectrum. Wavelengths get either too short or too long on either side of the visible spectrum—which differs for different species. I would actually really enjoy seeing how these two spectra stack up against other space observatories like Chandra (x-ray) and Spitzer (infrared).
Next we have the deployment, which finished just last week. The graphic summarises how complicated this process was—and how fraught with risk. But in the end it went off without any major hitch.
This uses a nice series of small multiples of illustrations. These simplified drawings show how the tightly packed telescope unfolds and then begins deploying its vital heat shield then its mirror.
The last thing to check out in the article is a slider showing the “before” and “after”. You have seen them before for things like flood or hurricane damages. Here, however, you can compare a photo in Hubble’s visible light to an existing infrared version of the same photo.
Of course, just because the telescope finished deploying its mirror last week doesn’t mean we get photos this week. The Baltimore-based team running the observatory will spend the next few months tuning everything up. But the goal is hopefully to have the first images from James Webb sometime in June.
And then we have the next ten years to hopefully start collecting data.
Credit for the piece goes to the BBC graphics team.
Thankfully today’s forecast calls for cooler temperatures. Your author is not a fan of hot weather, which means being outside in summer is…less than ideal. It also means that the air conditioner runs frequently and on high for a few months. (Conversely, I can probably count on one hand the number of times I turned on the heat this winter.)
The problem is, the two biggest contributors to US carbon emissions? Heating/cooling and transport. In other words, heating your home in the winter, cooling it in the summer, and then driving your non-electric vehicle.
After the recent heatwave in New England, the Boston Globeexamined the impact of the heatwave on the environment. The article led with the claim it used four charts to do so. I quibble with that distinction because this is a screenshot of the second graphic.
I mean, it’s not prose text. Rather, we have three factettes paired with illustrations. At the top of this post, I mentioned the impact of transport for a reason. In an ideal world, in order to get carbon emissions under control one of the changes we would need to see is getting people out of their personal automobiles and into mass transit. Subways and light rail are far cleaner and can actually be cheaper for households than car ownership. And so we should be encouraging their use and building more of them.
Look above and you’ll see an icon of a subway car. Except it’s not. The graphic/factette is actually talking about rail cars full of coal that transport fuel from mine to generating station. Those look more like this, from James St. James via Wikimedia Commons.
Small, subtle details matter. And so I’d propose a new icon that tries to capture the industrial coal train, ideally something that I spent more than five minutes on.
But it breaks the linkage between passenger train and coal train, which is not ideal for the purposes of an article highlighting the environmental impacts of US households.
That all said, the article did a really good job with the other graphics it used. My favourite was this chart, decidedly not a combination chart.
It looks at the correlation between high temperatures and energy usage. But, instead of lazily throwing the temperatures atop the bars, the designers more carefully placed them below the energy usage chart. The top chart should look familiar to those who have been following my Covid-19 charts, a daily number that then has the rolling seven-day average plotted above it to smooth out any one-day quirks. The designer then chose to highlight the heatwave in red.
For temperatures, I like the overall approach. But I wonder if a more nuanced approach could have taken the graph a step farther to excellent. Presently we have a single red line representing daily average high temperature. But in the plot above we use red to indicate the heat wave of early June, five consecutive days of temperatures in excess of 90ºF. What if that line were black or grey or some neutral colour, and then only the heatwave was coloured in red? It would more clearly link the two together. And it avoids the trap of red implying heat, when you need to only go back to late May when the East Coast had early spring like temperatures near 50ºF, decidedly not red on a temperature scale.
Overall, though, it’s refreshing to see a thoughtful approach taken here instead of the usual slapdash throw one chart atop the other.
And the rest of the article uses restrained, smart graphics as well. Bar charts and small multiples to capture air pollution and EMS calls. You should read the full article for the insights and the feedback loops we have.
After all, it’s not that the heating/cooling is itself the problem, especially since the removal of CFCs since the Montreal Protocol in 1987 that banned those pesky chemicals that harm the ozone layer—remember when that was the big environmental issue in the 1990s? The issue is how we generate the electricity that powers the heating/cooling systems—and if you want to use electric cars, whence comes their electric charge—as if we’re using coal plants, that just exacerbates the problem. But if we use carbon-less plants, e.g. nuclear, solar, or wind, we’re not generating carbon emissions.
Two weeks ago I posted about an article from the BBC that used graphics about which I was less than thrilled. Inconsistent use of axis lines, centring the graphic were two of the things that irked me. Two weeks hence, I do want to draw some positive attention to another article in the BBC. This one discusses the, for many of us, impending return to the office. (I’ve also heard the phrase “return to work”, although a coworker of mine pointed out that’s not a great phrase because many of us never stopped working when we decamped for our flats and houses.)
The article discusses why some think the return to a five-day office week will occur within the next few years. There is some sound logic to the idea and for those like your author who are closely following the issue, I recommend the article.
But that’s not why we’re here, instead I wanted to focus on the one data visualisation graphic in the piece. It displays the amount of office space used in the city centres of six different UK cities outside London.
Here we have small multiples with the same fixed y-axis display. Axis lines are present and consistent and the baseline is distinct from the other lines. Solid improvement over what we discussed two weeks ago.
My only quibble? The colours here are not necessary. A single colour would work because each city’s graphic exists apart from the rest. The charts also all represent the same type of data, occupied office space. If the chart were doubling or tripling up cities somehow—though I wouldn’t want to see this as a stacked area chart—I would buy the need for colours to differentiate the cities. This, however, represents an opportunity to use a single, BBC-branded colour to define the experience whilst not negatively impacting the communication from the data visualisation standpoint.
Again, though, that’s a minor quibble. Of course, the BBC puts out copious amounts of content daily and I see only a fraction, but it is nice to see an improvement. Furthermore, at the end of the article I also spotted a graphic credit, which I don’t often see—and honestly cannot recall when I last saw period—from the BBC.
I wonder if moving forward the BBC intends to highlight the contributors to articles who are not solely the writers, i.e. the people creating the graphics? Of course, if we did that, we should also probably take a look at the copy editors who also play a role. Especially for an online article as opposed to say a print newspaper or magazine where space is money.
Two Fridays ago I received my second dose of the vaccine. In other words, I’m fully vaccinated and can resume doing…things. Anything. And so this piece from xkcd seemed an appropriate way to wrap up what has been a horrible, no good, terrible year.
Apologies for the lack of posting last week. I’m on deadline for, well, today. Plus I had some technical difficulties on the server side of the blog. But it’s a Monday, so we’re back with Covid updates for Pennsylvania, New Jersey, Delaware, Virginia, and Illinois.
The good news, such that it is during a global pandemic, is that in Pennsylvania, Delaware, and Illinois, the seven-day average appears to be lower than this time last week or, especially in Delaware’s situation, about to break. For the First State, I’m looking at those days prior to the weekend below the average line that, in combination with the weekend, will likely begin to push that trend downward, especially if we keep seeing fewer and fewer cases this week.
Unfortunately, some states like Virginia and New Jersey appear to be, not surging, but experiencing low and slow growth. Low and slow, while great for barbecue, is less than ideal during a pandemic. Granted, it’s better than the rapid infections we saw in March, April, and May, but it still means the virus is spreading in those communities.
When we look at deaths from Covid-19 in these five states, the news is better. The only real significant level of deaths was in Virginia, but we can see that the latest little surge, which was at peak last week, has now all but abated, almost to a level not seen since the spring.
The other states remain low with, at most, deaths average about 20 per day. Again, not good, but better than hundreds per day.
Yesterday was a holiday in the States, and so let’s begin this shortened week with a look at the Covid situation in Pennsylvania, New Jersey, Delaware, Virginia, and Illinois.
If we compare this morning’s charts of yesterday’s data to last Monday’s, we can see some concerning trends.
In Pennsylvania, that includes a rising trend. Anecdotally, that might be tied to the outbreaks in and around universities. We see rising trends in Delaware and Virginia as well, though some of Delaware’s new numbers might be tied to some cases that failed to initially make into the state’s digital database. And so as the state begins to enter them now, it artificially inflates the new case numbers.
Illinois had an enormous spike of cases from a backlog that the state entered, over 5,000 new case in that one day. That’s going to mess with the average trend given the size of the anomaly. So we’ll need to wait until later this week to see where the trend really is.
Then in terms of deaths, the most worrying state was Virginia which last week was mid-peak. But that appears to maybe be trending back down. Though the data we have does include two day’s of weekend numbers and Tuesday’s numbers, instead of the usual “rebound” will be more of the usual weekend depressed numbers.
After dealing with hurricane forecast plots last Monday, we’re back to the nature-made, man-intensified disaster of Covid-19 in the United States. So in the five states we review, where are we with the pandemic?
Compared to the charts from two weeks, looking at daily new cases, in some places we are in a better spot, and in others not much has changed. In fact Illinois is the only place worse off with its seven-day average higher than it was two weeks ago, but not by dramatically much.
In fact we see in Pennsylvania, New Jersey, and Delaware that the average number of daily new cases is lower than it was two weeks ago. Virginia dipped lower, but has recently returned to approximately the same level and in that sense is in no different a place. Of course the key factor is how those trends all change over the coming week.
But what about in terms of deaths?
Well here there is bad news in Virginia. Two weeks ago a spike in deaths there had largely subsided. Two weeks hence? We are in the middle of a third spike of deaths, reaching nearly 20 deaths per day.
Fortunately, the other four states remain largely the same, and that means few deaths per day. Indeed, for Pennsylvania and New Jersey that means deaths in the low double-digits or often in the single digits. Delaware has not reported a new death in four days. And Illinois, while up a little bit, is in the low single-digits, but generally just a few more deaths per day than Pennsylvania and New Jersey.
So here are the charts from the last week of Covid data in Pennsylvania, New Jersey, Delaware, Virginia, and Illinois.
When we compare last week’s update to today’s, we can see that Pennsylvania did indeed bottom out and is back on the rise and the same can probably be said for Delaware. Although a fair amount of the one-day spikes in those numbers we see today are from an outbreak in a correctional system.
Whilst Virginia did go up, by week’s end, it had settled back down to a point not dissimilar to last week. So nothing really changed and time stood still in Virginia. The same can also be loosely said for New Jersey, where it was more about fluctuations than determined rises or falls.
In Illinois, however, we finally saw a plateauing of the new cases numbers and with the slightest of declines .
Then in deaths we have not much to say as they remain low in New Jersey and Delaware and stable and moderate in Illinois.
Virginia’s recent spike appears to have subsided, as it’s back to nearly 10 deaths per day from the virus.
But most concerning is Pennsylvania. Here, while the numbers are still relatively low, they are on a slow and gradual rise. At this point the seven-day average is beginning to rise above 20 deaths per day.