data visualisation – Page 8

The Potential Impacts of Throwing Out Roe v Wade

Spoiler: they are significant.

Last night we had breaking news on two very big fronts. The first is that somebody inside the Supreme Court leaked an entire draft of the majority opinion, written by Justice Alito, to Politico. Leaks from inside the Supreme Court, whilst they do happen, are extremely rare. This alone is big news.

But let’s not bury the lede, the majority opinion is to throw out Roe v. Wade in its entirety. For those not familiar, perhaps especially those of you who read me from abroad, Roe v Wade is the name of a court case that went before the United States Supreme Court in 1971 and was decided in 1973. It established the woman’s right to an abortion as constitutionally protected, allowing states to enact some regulations to balance out the state’s role in concern for women’s public health and the health of the fetus as it nears birth. Regardless of how you feel about the issue—and people have very strong feelings about it—that’s largely been the law of the United States for half a century.

Until now.

To be fair, the draft opinion is just that, a draft. And the supposed 5-3 vote—Chief Justice Roberts is reportedly undecided, but against the wholesale overthrow of Roe—could well change. But let’s be real, it won’t. And even if Roberts votes against the majority he would only make the outcome 5-4. In other words, it looks like at some point this summer, probably June or July, tens of millions of American women will lose access to reproductive healthcare.

And to the point of this post, what will that mean for women?

This article by Grid runs down some of the numbers, starting with laying out the numbers on who chooses to have abortions. And then ultimately getting to this map that I screenshot.

That’s pretty long distances in the south…

The map shows how far women in a state would need to travel for an abortion with Roe active as law and without. I’ve used the toggle to show without. Women in the south in particular will need to travel quite far. The article further breaks out distances today with more granularity to paint the picture of “abortion deserts” where women have to travel sometimes well over 200 miles to have a safe, legal abortion.

I am certain that we will be returning to this topic frequently in coming months, unfortunately.

Credit for the piece goes to Alex Leeds Matthews.

The B-52s

Not the band, but the long-range strategic bomber employed by the United States Air Force. This isn’t strictly related to Ukraine, but it’s military adjacent if you will.

I thought about creating a graphic a few years ago to celebrate the longevity of the B-52 Stratofortress, more commonly called the BUFF, Big Ugly Fat Fucker. Obviously I did not, but over at Air Force Magazine, they created a graphic timeline showing the history of the aircraft, specifically as it relates to its engines, which will now be replaced in an effort to extend the life of the bombers.

I don’t love the image of the bomber behind the graphic, but I understand why it’s there given the B-52 is the focus of the timeline. I wonder if a different layout could have highlighted the placement of the engines and separated the timeline from the image of the bomber.

Overall I like the graphic, but it could just be that right now I’m spotlighting and working on a lot of graphics dealing with military issues and Ukraine in particular.

Credit for the piece goes to Dash Parham and Mike Tsukamoto.

Battalion Tactical Groups

As Russia redeploys its forces in and around Ukraine, you can expect to hear more about how they are attempting to reconstitute their battalion tactical groups. But what exactly is a battalion tactical group?

Recently in Russia, the army has been reorganised increasingly away from regiments and divisions and towards smaller, more integrated units that theoretically can operate more independently: battalion tactical groups. They typically comprise less than a thousand soldiers, about 200 of which are infantry. But they also include a number of tanks, infantry fighting vehicles (IFVs), armoured personnel carriers (APCs), artillery, and other support units.

In an article from two weeks ago, the Washington Post explained why the Russian army had stalled out in Ukraine. And as part of that, they explained what a battalion tactical group is with a nice illustration.

Russia’s problem is that in the first month of the war, Ukrainian anti-armour weapons like US-made Javelins and UK-made NLAWs have ripped apart Russian tanks, IFVs, and APCs. Atop that, Ukrainian drones and artillery took out more armour. The units that Russia withdrew from Ukraine now have to be rebuilt and resupplied. Once fresh, Russia can deploy these into the Donbas and southern Ukraine.

This graphic isn’t terribly complicated, but the nice illustrations go a long way to showing what comprises a battalion tactical group. And when you see photos of five or six tanks destroyed along the side of a Ukrainian road, you now understand that constitutes half of a typical unit’s available armour. In other words, a big deal.

I expect to hear more out of Russia and Ukraine in coming days about how Russia is providing new vehicles and fresh soldiers to resupply exhausted units.

Credit for the piece goes to Bonnie Berkowitz and Artur Galocha.

Where’s My (State) Stimulus?

Here’s an interesting post from FiveThirtyEight. The article explores where different states have spent their pandemic relief funding from the federal government. The nearly $2 trillion dollar relief included a $350 billion block grant given to the states, to do with as they saw fit. After all, every state has different needs and priorities. Huzzah for federalism. But where has that money been going?

Enter the bubbles.

I mean bubbles need water distribution systems, right?

This decision to use a bubble chart fascinates me. We know that people are not great at differentiating between area. That’s why bars, dots, and lines remain the most effective form of visually communicating differences in quantities. And as with the piece we looked at on Monday, we don’t have a legend that informs us how big the circles are relative to the dollar values they represent.

And I mention that part because what I often find is that with these types of charts, designers simply say the width of the circle represents, in this case, the dollar value. But the problem is we don’t see just the diameter of the circle, we actually see the area. And if you recall your basic maths, the area of a circle = πr². In other words, the designer is showing you far more than the value you want to see and it distorts the relationship. I am not saying that is what is happening here, but that’s because we do not have a legend to confirm that for us.

This sort of piece would also be helped by limited duty interactivity. Because, as a Pennsylvanian, I am curious to see where the Commonwealth is choosing to spend its share of the relief funds. But there is no way at present to dive into the data. Of course, if Pennsylvania is not part of the overall story—and it’s not—than an inline graphic need not show the Keystone State. In these kinds of stories, however, I often enjoy an interactive piece at the end wherein I can explore the breadth and depth of the data.

So if we accept that a larger interactive piece is off the table, could the graphic have been redesigned to show more of the state level data with more labelling? A tree map would be an improvement over the bubbles because scaling to length and height is easier than a circle, but still presents the area problem. What a tree map allows is inherent grouping, so one could group by either spending category or by state.

I would bet that a smart series of bar charts could work really well here. It would require some clever grouping and probably colouring, but a well structured set of bars could capture both the states and categories and could be grouped by either.

Overall a fascinating idea, but I’m left just wanting a little more from the execution.

Credit for the piece goes to Elena Mejia.

There Goes the Shore

The National Oceanic and Atmospheric Administration (NOAA) released its 2022 report, Sea Level Rise Technical Report, that details projected changes to sea level over the next 30 years. Spoiler alert: it’s not good news for the coasts. In essence the sea level rise we’ve seen over the past 100 years, about a foot on average, we will witness in just thirty years to 2050.

Now I’ve spent a good chunk of my life “down the shore” as we say in the Philadelphia dialect and those shore towns will all have a special place in my life. But that looks more to be like a cherished memory fading into time. I took a screenshot of the Philadelphia region and South Jersey in particular.

Not just the Shore, but also the Beaches

To be fair, that big blob of blue is Delaware Bay. That’s already the inlet to the Atlantic. But the parts that ought to disturb people are just how much blue snakes into New Jersey and Delaware, how much/little space there is between those very small ribbons of land land off the Jersey coast.

You can also see little blue dots. When the user clicks on those, the application presents the user with a small interactive popup that models sea level rise on a representative photograph. In this case, the dot nearest to my heart is that of the Avalon Dunes, with which I’m very familiar. As the sea level rises, more and more of the street behind protected by the dunes disappears.

My only real issue with the application is how long it takes to load and refresh the images every single time you adjust the zoom or change your focus. I had a number of additional screenshots I wanted to take, but frankly the application was taking too long to load the data. That could be down to a million things, true, but it frustrated me nonetheless.

Regardless of my frustration, I do highly recommend you check out the application, especially if you have any connection to the coast.

Credit for the piece goes to NOAA.

Colours for Maps

Today we have an interesting little post, a choropleth map in a BBC article examining the changes occurring in the voting systems throughout the United States. Broadly speaking, we see two trends in the American political system when it comes to voting: make it easier because democracy; make it more restrictive because voter fraud/illegitimacy. The underlying issue, however, is that we have not seen any evidence of widespread or concerted efforts of voter fraud or problems with elections.

Think mail-in ballots are problematic? They’ve been used for decades without issues in many states. That doesn’t mean a new state could screw up the implementation of mail-in voting, but it’s a proven safe and valid system for elections.

Think that were issues of fraudulent voters? We had something like sixty cases brought before the courts and I believe in only one or two instances were the issues even remotely proven. The article cites some Associated Press (AP) reporting that identified only 500 cases of fraudulent votes. Out of over 14 million votes cast.

500 out of 14,000,000.

Anyway, the map in the article colours states by whether they have passed expansive or restrictive changes to voting. Naturally there are categories for no changes as well as when some expansive changes and some restrictive changes were both passed.

Normally I would expect to see a third colour for the overlap. Imagine we had red and blue, a blend of those colours like purple would often be a designer’s choice. Here, however, we have a hatched pattern with alternating stripes of orange and blue. You don’t see this done very often, and so I just wanted to highlight it.

I don’t know if this marks a new stylistic design direction by the BBC graphics department. Here I don’t necessarily love the pattern itself, the colours make it difficult to read the text—though the designers outlined said text, so points for that.

But I’ll be curious to see if I, well, see more of this in coming weeks and months.

Credit for the piece goes to the BBC graphics department.

Where’s the Axis

We’re starting this week with an article from the Philadelphia Inquirer. It looks at the increasing number of guns confiscated by the Transportation Security Administration (TSA) at Philadelphia International Airport. Now while this is a problem we could discuss, one of the graphics therein has a problem that we’ll discuss here.

We have a pretty standard bar chart here, with the number of guns “detected” at all US airports from 2008 through 2021. The previous year is highlighted with a darker shade of blue. But what’s missing?

We have two light grey lines running across the graphic. But what do they represent? We do have the individual data points labelled above each bar, and that gives us a clue that the grey lines are axis lines, specifically representing 2,000 and 4,000 guns, because they run between the bars straddling those two lines.

However, we also have the data labels themselves. I wonder, however, are they even necessary? If we look at the amount of space taken up by the labels, we can imagine that three labels, 2k, 4k, and 6k, would use significantly less visual real estate than the individual labels. The data contained in the labels could be relegated to a mouseover state, revealed only when the user interacts directly with the graphic. Here it serves as a “sparkle”, distracting from the visual relationships of the bars.

If the actual data values to the single digit are important, a table would be a better format for displaying the information. A chart should show the visual relationship. Now, perhaps the Inquirer decided to display data labels and no axis for all charts. I may disagree with that, but it’s a house data visualisation stylistic choice.

But then we have the above screenshot. In this bar chart, we have something similar. Bars represent the number of guns detected specifically at Philadelphia International Airport, although the time framer is narrower being only 2017–2021. We do have grey lines in the background, but now on the left of the chart, we have numbers. Here we do have axis labels displaying 10, 20, and 30. Interestingly, the maximum value in the data set is 39 guns detected last year, but the chart does not include an axis line at 40 guns, which would make sense given the increments used.

At the end of the day, this is just a frustrating series of graphics. Whilst I do not understand the use of the data labels, the inconsistency with the data labels within one article is maddening.

Credit for the piece goes to John Duchneskie.

Can You Hit the High Notes?

This is an older piece that I stumbled across doing some other work. I felt like it needed sharing. The interactive graphic shows the high and low note vocal ranges of major musical artists.

Good to see some of my favourite artists in the mix.

Interactive controls allow the user to sort the bars by the greatest vocal range, high notes, or low notes. Colour coding distinguishes male from female vocalists.

In particular I enjoy the bottom of the piece that uses the keyboard to show the range of notes. When the user mouses over a particular singer, the ends of the range display the particular song in which the singer hit the note.

Again, this is an older piece that I just discovered, but I did enjoy it. I would be curious to see how these things could change over time. As an artist ages, how does that change his or her vocal range? Are there differences between albums? This could be a fascinating point at which branching out for further research could be done.

Credit for the piece goes to ConcertHotels.com

Dots Beat Bars

Today is just a quick little follow-up to my post from Monday. There I talked about how a Boston Globe piece using three-dimensional columns to show snowfall amounts in last weekend’s blizzard failed to clearly communicate the data. Then I showed a map from the National Weather Service (NWS) that showed the snowfall ranges over an entire area.

Well scrolling through the weather feeds on the Twitter yesterday I saw this graphic from the NWS that comes closer to the Globe‘s original intent, but again offers a far clearer view of the data.

Whilst we miss individual reports being depicted as exact, that is to say the reports are grouped into bins and assigned a colour, we have a much more granular view than we did with the first NWS graphic I shared.

The only comment I have on this graphic is that I would probably drop the terrain element of the map. The dots work well when placed atop the white map, but the lighter blues and yellows fade out of view when placed atop the green.

But overall, this is a much clearer view of the storm’s snowfall.

Credit for the piece goes to the National Weather Service graphics department.

How Accurate Is Punxsutawney Phil?

For those unfamiliar with Groundhog Day—the event, not the film, because as it happens your author has never seen the film—since 1887 in the town of Punxsutawney, Pennsylvania (60 miles east-northeast of Pittsburgh) a groundhog named Phil has risen from his slumber, climbed out of his burrow, and went to see if he could see his shadow. Phil prognosticates upon the continuance of winter—whether we receive six more weeks of winter or an early spring—based upon the appearance of his shadow.

But as any meteorological fan will tell you, a groundhog’s shadow does not exactly compete with the latest computer modelling running on servers and supercomputers. And so we are left with the all important question: how accurate is Phil?

Thankfully the National Oceanic and Atmospheric Administration (NOAA) published an article several years ago that they continue to update. And their latest update includes 2021 data.

Not exactly an accurate depiction of Phil.

I am loathe to be super critical of this piece, because, again, relying upon a groundhog for long-term weather forecasting is…for the birds (the best I could do). But critiques of information design is largely what this blog is for.

Conceptually, dividing up the piece between a long-term, i.e. since 1887, and a shorter-term, i.e. since 2012, makes sense. The long-term focuses more on how Phil split out his forecasts—clearly Phil likes winter. I dislike the use of the dark blue here for the years for which we have no forecast data. I would have opted for a neutral colour, say grey, or something that is visibly less impactful than the two light colours (blue and yellow) that represent winter and spring.

Whilst I don’t love the icons used in the pie chart, they do make sense because the designers repeat them within the table. If they’re selling the icon use, I’ll buy it. That said, I wonder if using those icons more purposefully could have been more impactful? What would have happened if they had used a timeline and each year was represented by an icon of a snowflake or a sun? What about if we simply had icons grouped in blocks of ten or twenty?

The table I actually enjoy. I would tweak some of the design elements, for example the green check marks almost fade into the light blue sky. A darker green would have worked well there. But, conceptually this makes a lot of sense. Run each prognostication and compare it with temperature deviation for February and March (as a proxy for “winter” or “spring”) and then assess whether Phil was correct.

I would like to know more about what a slightly above or below measurement means compared to above or below. And I would like to know more about the impact of climate change upon these measurements. For example, was Phil’s accuracy higher in the first half of the 20th century? The end of the 19th?

Finally, the overall article makes a point about how difficult it would be for a single groundhog in western Pennsylvania to determine weather for the entire United States let alone its various regions. But what about Pennsylvania? Northern Appalachia? I would be curious about a more regionally-specific analysis of Phil’s prognostication prowess.

Credit for the piece goes to the NOAA graphics department.