infographic – Page 22 – Coffee Spoons

On a Line. Or Not.

Two weeks ago I was reading an article in the BBC that fact checked some of President Biden’s claims about the economy. Now I noted the other day in a post about axis lines and their use in graphics. Axis lines help ground the user in making comparisons between bars, lines, or whatever, and the minimum/maximum/intervals of the data set.

I was reading the article and first came upon this graphic. It’s nothing crazy and shows job growth in the aggregate for the first three months of a presidential administration. A pretty neat comparison in the combination of the data. I like.

Pay attention to what you see here. There will be a quiz.

I don’t like the lack of grid lines for the axis, however. But, okay, none to be found.

I keep reading the article. And then a couple of paragraphs later I come upon this graphic. It looks at the monthly figures and uses a benchmark line, the red dotted one, to break out those after January 2021 when Biden took office.

But do you notice anything?

The lines for the y-axis are back!

The article had a third graphic that also included axis lines.

I don’t have a lot to say about these graphics in particular, but the most important thing is to try and be consistent. I understand the need to experiment with styles as a brand evolves. Swap out the colours, change the styles of the lines, try a new typeface. (Except for the blue, we are seeing different colours and typefaces here, but that’s not what I want to write about.)

First, I don’t know if these are necessarily style experiments. I suspect not, but let’s be charitable for the sake of argument. I would refrain from experimenting within a single article. In other words, use the lines or don’t, but be consistent within the article.

For the record, I think they should use the lines.

Another point I want to make is with the third graphic. You’ll note that, like I said above, it does use axis lines. But that’s not what I want to mention.

Instead I want to look at the labelling on the axes. Let’s start with the y-axis, the percentage change in GDP on the previous quarter. The top of the chart we have 30%. As I’ve said before, you can see in the Trump administration, the bar for the initial Covid-19 rebound rises above the 30% line. It’s not excessive, I can buy it if you’re selling it.

But let’s go down below the 0-line. Just prior to the rebound we had the crash. Similarly, this extends just below the -30% line. But here we have a big space and then a heavy black line below that -30% line. It looks like the bottom line should be -40%, but scanning over to the left and there is no label. So what’s going on?

First, that heavy black line, why does it appear the same as the baseline or zero-growth line? The axis lines, by comparison, are thin and grey. You use a heavier, darker line to signify the breaking point or division between, in this case, positive and negative growth. Theoretically, you don’t need the two different colours for positive and negative growth, because the direction of the bar above/below that black line encodes that value. By making the bottom line the same style as the baseline, you conflate the meaning of the two lines, especially since there is no labelling for the bottom line to tell you what the line means.

Second, the heaviness of the line draws visual attention to it and away from the baseline, especially since the bottom line has the white space above it from the -30% line. Consider here the necessity of this line. For the 30% line that sets the maximum value of the y-axis, we have the blue bar rising above the line and the administration labels sit nicely above that line. There is no reason the x-axis labels could not exist in a similar fashion below the -30% line. If anything, this is an inconsistency within the one chart, let alone the one graphic.

Third, is it -40%? I contend the line isn’t necessary and that if the blue bar pokes above the 30% line, the orange bar should poke below the -30% line. But, if the designer wants to use a line below the -30% line, it should be labelled.

Finally, look at the x-axis. This is more of a minor quibble, but while we’re here…. Look at the intervals of the years. 2012, 2014, 2016, every two years. Good, make sense. 2018. 20…21? Suddenly we jump from every two years to a three-year interval. I understand it to a point, after all, who doesn’t want to forget 2020. But in all seriousness, the chart ends at 2021 and you cannot divide that evenly. So what is a designer to do? If this chart had less space on the x-axis and the years were more compressed in terms of their spacing, I probably wouldn’t bring this up.

However, we have space here. If we kept to a two-year interval system, I would introduce the labels as 2012, but then contract them with an apostrophe after that point. For example, 2014 becomes ’14. By doing that, you should be able to fit the two-year intervals in the space as well as the ending year of the data set.

Overall, I have to say that this piece shocked me. The lack of attention to detail, the inconsistency, the clumsiness of the design and presentation. I would expect this from a lesser oganisation than the BBC, which for years had been doing solid, quality work.

The first chart is conceptually solid. If Biden spoke about job creation in the first three months of the administration vs. his predecessor, aggregate the data and show it that way. But the presentation throughout this piece does that story a disservice. I wish I knew what was going on.

Credit for the piece goes to the BBC graphics department.

I’ve Got the Subtlest of Blues

As I prepared to reconnect and rejoin the world, I spent most of the weekend prior to full vaccination cleaning and clearing out my flat of things from the past 14 months. One thing I meant to do more with was printed pieces I saw in the New York Times. Interesting pages, front pages in particular, have been piling up and before recycling them all, I took some photos of the backlog. I’ll try to publish more of them in the coming weeks and months.

You may recall this time last month I wrote about a piece from the New York Times that examined the politicisation of vaccinations. I meant to get around to the print version, but didn’t, so let’s do it now.

I noted last time the use of ellipses for the title and the lack of value scales on the x-axis. Those did not change from the online version. But look at the y-axis.

For the print piece I noted how the labels were placed inside the chart. I wondered at the time—but didn’t write about—how perhaps that could have been a technical limitation for the web. But here we can see the labels still inside. It was a deliberate design decision.

Keeping with the labelling, I also pointed out Wyoming being outside the plot and it is here too, but I finally noted the lack of a label for zero on the first chart. Here the zero does appear, as I would have placed it. That does make me wonder if the lack of zero online was a technical/development issue.

Finally, something very subtle. At first, I didn’t catch this and it wasn’t until I opened the image above in Photoshop. The web version I noted the use of tints, or lighter shades, for two different blues and two different reds. When I looked at the print, I saw only one red and one blue. But they were in fact different, and it wasn’t until I had zoomed in on the photo I took when I could see the difference.

The dots do have two different blues. But it’s very subtle. Same with the red.

So all in all the piece is very similar to what we looked at last month, but there were a few interesting differences. I wonder if the designers had an opportunity to test the blues/reds prior to printing. And I wonder if the zero label was an issue for developers.

Credit for the piece goes to Lauren Leatherby and Guilbert Gates.

Covid Update: 23 May

Last week I wrote about how we were seeing new cases continuing to rapidly decline. This week we can say cases are still declining, but perhaps a bit less rapidly than earlier.

New case curves for PA, NJ, DE, VA, & IL.

The charts above show that slowdown in the tail at the right of the chart. First some points to note, Delaware reported that several hundred cases had not been entered into their database, and so we saw a one-time spike midweek. But note that after the spike, the numbers continue to trend down. In other words, the rapid decline was probably a bit less rapid than we saw, but it was still a decline.

Pennsylvania’s chart has a problem of your author’s own design. Now that I’m fully vaccinated I was able to leave the flat this weekend and the Pennsylvania data wasn’t ready by the time I left on Saturday. But by the Sunday data, it was and so the 2500 new cases is probably split somehow between those days—accounted for by the seven-day average. This points to a broader question for which I do not yet have an answer: as life increasingly returns to normal, how much longer will I continue to update these charts?

I started these graphics as a way for myself to track the spread of the virus in my home state and the state where I still have a large number of friends. At the time, there were few if any visualisations out there doing this. Now most media outlets have them and my work at home led to a similar project at work. The reason I continued to make these was you, my readers here and in other places where I post this work. Your comments, messages, texts, and emails made it clear you valued the work. First, I know there are still many people left to be fully vaccinated, nearly half the population, and due to bias, some of the people most likely to follow these posts are those most likely to get vaccinated as early as possible. But please let me know, readers, if you’re still getting value out of these graphics.

But back to the data, in two of the remaining three states, Virginia and Illinois, we saw numbers continue to decline. New Jersey, however, shows a tail with a slight uptick in the seven-day average of new cases. This will be something I follow closely this coming week.

Deaths finally appear to be dropping.

Not by large numbers, no, but in Virginia and Illinois we saw declines of 5 deaths per day. Pennsylvania was even greater with a decline of 7. We are still above rates we saw last summer, but it does appear that finally we have hit the inflection point we have been waiting for the last several weeks.

Finally we have vaccinations. These charts look at the cumulative number of people fully vaccinated.

Fully vaccinated curves for PA, VA, & IL.

And in that the number keeps going up, and that’s good. But they can also only keep going up. But if you look closely at the right tail of the curve, you begin to see it flattening out as the rate of daily vaccinations begins to drop. Unfortunately we’re well below levels we think we’d need for herd immunity. But, to try and look at the positive, we’re almost halfway there and that is certainly playing a role as we can see with the rapid decline in numbers of new cases. But we need to keep trying to get more people vaccinated.

Credit for the piece is mine.

Some Data on Deaths in Gaza and Israel

I’ve seen an uptick in traffic to the blog the last few days, specifically my older content on the Middle East. I don’t exactly have the bandwidth to track the conflict between Israel and Gaza in addition to Covid-19 and my other projects. But as we approached the ten-day mark since Hamas first fired rockets into Israel, I wanted to get a sense of the death toll and so here we are.

The biggest thing to note is that we should take all this data with a grain of salt. For example, the Israeli Defence Force will likely talk up the effectiveness of its Iron Dome air defence system and downplay total civilian deaths. Conversely, Hamas will likely talk up civilian deaths while not detailing at all the deaths of its fighters. And when it comes to deaths in Gaza, it’s not clear what share of those reported by civilian authorities, i.e. the hospital systems, are militant fighters vs. civilians.

Not at all covered by any of this is a discussion of the opportunity costs involved, particularly when it comes to Israeli air strikes. For example, if a Gaza household contains a known Hamas fighter, one can certainly regret an Israeli drone strike that kills the fighter and his non-combatant son whilst in a field. But that strike may be a better outcome than striking the fighter’s home and along with killing not just him and his son, but now his wife, daughters, and the rest of his family.

Credit for the piece is mine.

Israel’s Palestine Trilemma

In what feels like forever ago, I wrote about the trilemma facing the British government as it related to Brexit. Brexit presented Westminster with three choices, of which they could only make two as all three were, together, impossible. Once made, those two choices determined the outcome of Brexit. For better or worse, Prime Minister Boris Johnson made that decision.

We can apply the same trilemma system to Israel in relation to the circumstances of Israel and Palestine. I will skip the long history lesson here. Israel faces some tough decisions. I will also skip the critique of Israeli government policy over the last few decades that brought us to this point. Because here is where we are.

Israel needs to balance three things: the importance of being a representative democracy, of being a Jewish state, and of security control of Gaza and the West Bank for the security of Israel. Here is how that looks.

If Israel wants to remain an ethnically Jewish state—I’m going to also skip the discourse about Jewishness as an ethnicity, though I will point to Judaism as an ethnic religion as opposed to the other Abrahamic universal religions of Christianity and Islam—and it wants to be retain security control over Palestine, i.e. the Gaza Strip and the West Bank, you have what we have today.

If Israel wants to remain an ethnically Jewish state and it wants to be a representative democracy, you get the Two-State Solution. In that scenario, Palestine, again conceived as Gaza and the West Bank, becomes a fully-fledged independent and sovereign state. Israel remains Jewish and Palestine becomes Arab. But, Israel loses the ability to police and militarily control Gaza and the West Bank, instead relying on its newfound partners in the Palestinian Authority or whatever becomes the executive government of Palestine. This has long been the goal of Middle East peace plans, but over the last decade or so you hear Two-State Solution less and less frequently.

Finally, if Israel wants to be a representative democracy, in which case both Jewish citizens and Arab–Israelis and Palestinians all have the right to full political representation without reservations, e.g. the loyalty oath, and it wants to maintain security control over Gaza and the West Bank, you get something I don’t hear often discussed outside foreign policy circles: a non-Jewish, multi-ethnic Israel. Today Arab–Israelis and Palestinians nearly—if not already—outnumber Jewish Israelis. In a representative democracy, it would be near impossible to maintain an ethnically Jewish state in a county where the Jewish population is in the minority. Consequently, Israel would almost certainly cease being a Jewish state.

One can tinker around the edges, e.g. what are the borders of a Two-State Solution West Bank, but broadly the policy choices above determine the three outcomes.

The outstanding question remains, what future does Israel want?

Credit for the piece is mine.

Covid Update: 16 May

Last week I wrote about how new cases in the five states we cover (Pennsylvania, New Jersey, Delaware, Virginia, and Illinois) were falling and falling rapidly. And this week that pattern continues to hold.

If we look at the Sunday-to-Sunday numbers, daily new cases were down in all five states. If we look at the seven-day averages, cases are down in all states. Pennsylvania and Illinois are now down below 2000 new cases per day, Virginia is just over 500 per day, New Jersey is below 400, and Delaware is over 100. These are all levels we last saw last autumn. In other words, we’re not quite back to summer levels of low transmission, but this time next month, I wouldn’t be surprised if we were.

Deaths remain stubbornly resistant to falling.

In fact, if we compare the Sunday-to-Sunday numbers we see that the numbers yesterday were largely the same as last Sunday, except in Pennsylvania where they were up significantly. The seven-day average?

Here’s where it gets interesting, because deaths are up slightly. Not by much, for example, Illinois was at 29.1 deaths per day last Sunday, this Sunday? 30.9. Illinois isn’t alone. Pennsylvania, Delaware, and Virginia all have reported slight upticks in their death rates.

But the biggest concern is the continuing slowdown in vaccinations. We’re perhaps halfway to the point of herd immunity in the three states we track. All three are between 37% and 38%. The thing to track this coming week will be if the rate continues to slow.

Total full vaccination curves for PA, VA, & IL.

Credit for the piece is mine.

Baseball’s Injury Problem

Last week, Ken Rosenthal of the Athletic wrote an article examining the recent spate of injuries in Major League Baseball. For those interested in the sport, the article is well worth the read. For the unfamiliar, baseball played only about 1/3 of the number of games as usual last year due to Covid-19. This year, pitcher after pitcher seems to be falling prey to arm troubles. Position players are straining hamstrings, quads, and other muscles I’ve never heard of let alone used over the last year. And joking aside, therein is thought to be the problem.

And the evidence, in part, shows that we are seeing an increase in the numbers of injuries. But 2020 may not be as much of a problem as youngsters throwing baseballs near 100 mph. But I digress. The article contained a table detailing the numbers of injuries for certain body parts in the first month (April) of the season in both 2021 and 2019, the last comparable season due to Covid-19.

To be fair, the table was nice, but in the exhaustion of post-second dose shot last weekend, I sketched out some things and decided to turn it into a proper post.

Credit for the piece is mine.

2020 Census Apportionment

Every ten years the United States conducts a census of the entire population living within the United States. My genealogy self uses the federal census as the backbone of my research. But that’s not what it’s really there for. No, it exists to count the people to apportion representation at the federal level (among other reasons).

The founding fathers did not intend for the United States to be a true democracy. They feared the tyranny of mob rule as majority populations are capable of doing and so each level of the government served as a check on the other. The census-counted people elected their representatives for the House, but their senators were chosen by their respective state legislatures. But I digress, because this post is about a piece in the New York Times examining the new census apportionment results.

I received my copy of the Times two Tuesdays ago, so these are photos of the print piece instead of the digital, online editions. The paper landed at my front door with a nice cartogram above the fold.

Each state consists of squares, each representing one congressional district. This is the first place where I have an issue with the graphic, admittedly a minor one. First we need to look at the graphic’s header, “States That Will Gain or Los Seats in the Next Congress” and then look at the graphic. It’s unclear to me if the squares therefore represent the states today with their numbers of districts, or if we are looking at a reapportioned map. Up in Montana, I know that we are moving from one at-large seat to two seat, and so I can resolve that this is the new apportionment. But I am left wondering if a quick phrase or sentence that declares these represent the 2022 election apportionment and not those of this past decade would be clearer?

Or if you want a graphic treatment, you could have kept all the states grey, but used an unfilled square in those states, like Pennsylvania and Illinois, losing seats, and then a filled square in the states adding seats.

Inside the paper, the article continued and we had a few more graphics. The above graphic served as the foundation for a second graphic that charted the changing number of seats since 1910, when the number of seats was fixed.

I really like this graphic. My issue here is more with my mobile that took the picture. Some of these states appear quite light, and they are on the printed page. However, they are not quite as light as these photos make them out to be. That said, could they be darker? Probably. Even in print, the dark grey “no change” instances jump out instead of perhaps falling to the background.

The remaining few graphics are far more straightforward, one isn’t even a graphic technically.

First we have two maps.

Nothing particularly remarkable here. The colours make a lot of sense, with red representing Republicans and blue Democrats. Yellow represents independent commissions and grey is only one state, Pennsylvania, where the legislature is controlled by Republicans and the governorship by Democrats.

Finally we have a table with the raw numbers.

Tables are great for organising information. Do you have a state you’re most curious about, Illinois for example? If so, you can quickly scan down the state column to find the row and then over to the column of interest. What tables don’t allow you to do is quickly identify any visual patterns. Here the designers chose to shade the cells based on positive/negative changes, but that’s not highlighting a pattern.

Overall, this was a really strong piece from the Times. With just a few language tweaks on the front page, this would be superb.

Credit for the piece goes to Weyi Cai and the New York Times graphics department.

The May Jobs Report

Last Friday, the government released the labour statistics from April and they showed a weaker rebound in employment than many had forecasted. When I opened the door Saturday morning, I got to see the numbers above the fold on the front page of the New York Times.

What I enjoyed about this layout, was that the graphic occupied half the above the fold space. But, because the designers laid the page out using a six-column grid, we can see just how they did it. Because this graphic is itself laid out in the column widths of the page itself. That allows the leftmost column of the page to run an unrelated story whilst the jobs numbers occupy 5/6 of the page’s columns.

If we look at the graphic in more detail, the designers made a few interesting decisions here.

First, last week I discussed a piece from the Times wherein they did not use axis labels to ground the dataset for the reader. Here we have axis labels back, and the reader can judge where intervening data points fall between the two. For attention to detail, note that under Retail, Education and health, and Business and professional services, the “illion” in -2 Million was removed so as not to interfere with legibility of the graphic, because of bars being otherwise in the way.

My issue with the axis labels? I have mentioned in the past that I don’t think a designer always needs to put the maximum axis line in place, especially when the data point darts just above or below the line. We see this often here, for example Construction and Manufacturing both handle it this way for their minimums. This works for me.

But for the column above Construction, i.e. State and local government and Education and health, we enter the space where I think the graphic needs those axis lines. For Education and health, it’s pretty simple, the red losses column looks much closer to a -3 million value than a -2 million value. But how close? We cannot tell with an axis line.

And then under State and local government we have the trickier issue. But I think that’s also precisely why this could use some axis lines. First, almost all the columns fall below the -1 million line. This isn’t the case of just one or two columns, it’s all but two of them. Second, these columns are all fairly well down below the -1 million axis line. These aren’t just a bit over, most are somewhere between half to two-thirds beyond. But they are also not quite nearly as far to -2 million as the ones we had in the Education and health growth were near to -3 million.

So why would I opt to have an axis line for State and local governments? The designers chose this group to add the legend “Gain in April”. That could neatly tuck into the space between the columns and the axis line.

Overall it’s a solid piece, but it needs a few tweaks to improve its legibility and take it over the line.

Credit for the piece goes to Ella Koeze and Bill Marsh.

Covid Update: 9 May

Last week I wrote about how, for new cases, we had seen a few consecutive days of increasing cases. Were we witnessing an aberration, a one-off “well, that was weird”? Or was this the beginning of a trend towards increasing new cases?

A week later and we have our answer. Just a one-off.

New cases curves for PA, NJ, DE, VA, & IL.

If we focus on just the seven-day average, in just one week the numbers in New Jersey have fallen by half. In Pennsylvania, Virginia, it’s by one quarter. Illinois is a little less than that, as is Delaware. Across the board, numbers are falling and falling quickly.

When we move to deaths, we’re beginning to see an improvement. As the lagging indicator, we would expect these to begin to drop a few weeks after new cases begin to drop. We have begun to see what might be the peaks of deaths in a few states.

Full vaccination curves for PA, NJ, DE, VA, & IL.

Over this coming week, I’ll be closely watching these numbers to see if we can finally begin to say authoritatively that deaths are in decline.

Vaccinations drive all of this. And we continue to see the total number of fully vaccinated people climbing in Pennsylvania, Virginia, and Illinois. But, that rate is slowing down. Most likely we are entering a phase where those eager for their shots have largely received them. Now begins the challenge of vaccinating those who might lack easy access or have reservations.

But to be clear, we need those people to become fully vaccinated before we can truly begin to return to normal. Whatever normal is. It’s hard to remember anymore.

Credit for the piece is mine.