Covid Migration

Yep, Covid-19 remains a thing. About a month or so ago, an article in City Lab (now owned by Bloomburg), looked at the data to see if there was any truth in the notion that people are fleeing urban areas. Spoiler: they’re not, except in a few places. The entire article is well worth a read, as it looks at what is actually happening in migration and why some cities like New York and San Francisco are outliers.

But I want to look at some of the graphics going on inside the article, because those are what struck me more than the content itself. Let’s start with this map titled “Change in Moves”, which examines “the percentage drop in moves between March 11 and June 30 compared to last year”.

Conventionally, what would we expect from this kind of choropleth map. We have a sequential stepped gradient headed in one direction, from dark to light. Presumably we are looking at one metric, change in movement, in one direction, the drop or negative.

But look at that legend. Note the presence of the positive 4—there is an entire positive range within this stepped gradient. Conventionally we would expect to see some kind of red equals drop, blue equals gain split at the zero point. Others might create a grey bin to cover a negative one to positive one slight-to-no change set of states. Here, though, we don’t have that. Nor do we even get a natural split, instead the dark bin goes to a slightly less dark bin at positive four, so everything less than four through -16 is in the darker bin.

Look at the language, too, because that’s where it becomes potentially more confusing. If the choropleth largely focuses on the “percentage drop” and has negative numbers, a negative of a negative would be…a positive. A -25% drop in Texas could easily be mistaken with its use of double negatives. Compare Texas to Nebraska, which had a 2% drop. Does that mean Nebraska actually declined by 2%, or does it mean it rose by 2%?

A clean up in the data definition to, say, “Percentage change in moves from…” could clear up a lot of this ambiguity. Changing the colour scheme from a single gradient to a divergent one, with a split around zero (perhaps with a bin for little-to-no change), would make it clearer which states were in the positive and which were in the negative.

The article continues with another peculiar choice in its bar charts when it explores the data on specific cities.

Here we see the destinations of people moving out of San Francisco, using, as a note explains, requests for quotes as a proxy for the numbers of actual moves. What interests me here is the minimalist take on the bar charts. Note the absence of an axis, which leaves the bars almost groundless for comparison, except that the designer attached data labels to the ends of the bars.

Normally data labels are redundant. The point of a visualisation is to visualise the comparison of data sets. If hyper precise differences to the decimal point are required, tables often are a better choice. But here, there are no axis labels to inform the user as to what the length of a bar means.

It’s a peculiar design decision. If we think of labelling as data ink, is this a more efficient use with data labels than just axis labels? I would venture to say no. You would probably have five axis labels (0–4) and then a line to connect them. That’s probably less ink/pixels than the data labels here. I prefer axis lines to help guide the user from labels up (in this case) through the bars. Maybe the axis lines make for more data ink than the labels? It’s hard to say.

Regardless, this is a peculiar decision. Though, I should note it’s eminently more defensible than the choropleth map, which needs a rethink in both design and language.

Credit for the piece goes to Marie Patino.

Covid-19 Update: 19 October

I took a holiday yesterday. To be honest, I’ll be taking a lot of short holidays as the year winds down on account of not taking any the first three quarters of the year. So expect quite a few quiet Mondays and Fridays in the next few months.

But back to Covid-19. I won’t have a lot to say in this weekly update, because I didn’t write anything last night when I made these. Suffice it to say that things are bad and getting worse. Although, things could also be much worse. And by that I mean, while we are seeing dramatic rises in new cases, we are not yet seeing the rises in deaths that accompanied similar rises in March and April.

Although it should be said that while still low, deaths have been rising. The easiest seen instance of that is in Illinois, below. You can see deaths are rising slowly upwards and the state is approaching 50 deaths per day. While that is still off from the peaks of 100+ earlier this year, that’s still too many people.

New case curves for PA, NJ, DE, VA, and IL.
Death curves for PA, NJ, DE, VA, and IL.

Credit for the pieces is mine.

Mask Up

Well, we made it to Friday. But, if you’ve been following me on the social, you’ll know that Covid is beginning to spread once again in Pennsylvania, New Jersey, Delaware, Virginia, and Illinois. I live in a tower block and I can say that many of my neighbours are no longer wearing masks indoors. Yet mask-wearing is the easiest defence we have against the spread of the coronavirus. So let’s take a look at the most effective types of masks, thankfully charted by xkcd.

Credit for the piece goes to Randall Munroe.

Cheesesteaks and Politics

For those unaware, Pennsylvania matters in the 2020 election. And it has mattered for years as a perennial swing state. There are of course the visits to steel mill cities like Pittsburgh, deindustrialised places like Johnstown, and unions love visits to places in Lackawanna and Luzerne. (You can read more about Pennsylvania as a swing state in my latest analysis here.)

But I want to focus on visits to Philadelphia. Because they inevitably involve the candidate consuming a cheesesteak. The Economist’s sister magazine, 1843, recently published an article on this very subject. And the whole thing is worth a read.

How have I managed to find this relevant to a blog about data visualisation? Well, they included a recipe to help people understand just what goes into the traditional Philadelphia dish.

Personally, I always have to confess, I’ve never been a huge fan. But, I’ll take provolone over whiz any day.

Credit for the piece goes to Jake Read.

Trumpsylvania

After working pretty much non-stop all spring and summer, your humble author finally took a few days off and throw in a bank holiday and you are looking at a five-day weekend. But, because this is 2020 travelling was out of the question and so instead I hunkered down to finish writing/designing an article I have been working on for the last several weeks/few months.

The main write-up—it is a lengthy-ish read so you may want to brew a cup of tea—is over at my data projects site. This is the first project I have really written about for that since spring/summer 2016. Some of my longer-listening readers may recall that the penultimate piece there I wrote about Pennsyltucky was inspired by work I did here at Coffeespoons.

To an extent, so is this piece. I wrote about Trumpsylvania, the political realignment of the state of Pennsylvania. 2016 and the state’s vote for Donald Trump was less an aberration than many think. It was the near-end result of a decades-long transformation of the state’s political geography. And so I looked at the data underlying the shift and how and where it occurred.

And originally, I had a slightly different conclusion as to how this related to Pennsylvania in the upcoming 2020 election. But, the whole 2020 thing made me shift my thinking slightly. But you’ll have to read the whole thing to understand what I’m talking about. I will leave you with one of the graphics I made for the piece. It looks at who won each county in the state, but also whether or not the candidate was able to flip the county. In other words, was Clinton able to flip a Republican county? Was Trump able to flip a Democratic county?

Who won what? Who flipped what?

Let me know what you think.

And of course, many, many thanks to all the people who suffered my ideas, thoughts, and early drafts over the last several weeks. And even more thanks to those who edited it. Any and all mistakes or errors in the piece are all mine and not theirs.

Credit for the piece is mine.

Covid-19 Is Not the Flu Part Augh!

Yesterday, President Trump once again lied to the American public on his social media platforms. He falsely claimed that Covid-19 was nothing worse than the flu, which he falsely claimed sometimes kills more than 100,000 people. Once again we are going to look at the data comparing influenza to the novel coronavirus and the disease it causes, Covid-19. We are going to look at the president’s claim that Covid isn’t much worse than the flu, which sometimes kills more than 100,000 people.

I mean, I don’t know where else to begin. Over the last decade, not in any flu season has the flu killed 100,000 people. In the 2017/18 season, the CDC estimates the flu killed 61,000 Americans. But they also give a range where they feel with 95% confidence that the flu killed between 46,000 and 95,000 Americans. And that is the closest it’s come.

In fact, as of yesterday, Covid-19 has killed 207,000 Americans. That averages out to about 30,000 Americans per month. In other words, Covid-19 has killed each month the same number of people the flu kills in an entire (average) fly season.

And the worst part is that we still haven’t exited the first wave of the coronavirus, because we never got it under control in the first place.

I just don’t know how many more times we have to say this, but because the president keeps lying about it, I feel like I need to say, once again…

Covid-19. Is. Not. The. Flu.

Credit for the piece is mine.

Super Spreading Garden Parties

If you were unaware, in the wee hours of Friday, President Trump announced that he had tested positive for the coronavirus that causes Covid-19. It should be stated in the just three days hence, there is an enormous amount of confusion about the timeline as the White House is not commenting. From the prepared statement initially released it seems Trump first tested positive Wednesday. But that statement was then changed to fit the diagnosis in the wee hours of Friday morning. But just last night I saw reporting saying that test was actually a second, confirmatory test and the president first tested positive earlier Thursday.

The timeline is also important because it would allow us to more definitively determine when the president was infected. The reporting indicates that he caught the virus at a Rose Garden ceremony at the White House to introduce his Supreme Court nominee, Amy Coney Barrett. This BBC graphic does a great job showing who from that ceremony has tested positive with the virus.

The photo also does a great job showing how the seven people there were situated. Six of the seven did not wear masks, only North Carolina Senator Thom Tillis did. There is no social distancing whatsoever. And not shown in this photo are the indoor pre- and post-ceremony festivities where people are in close quarters, mingling, talking, hugging, shaking hands, all also without masks.

It should be noted others not in the photograph, e.g. campaign manager Bill Stepien, communications advisor Hope Hicks, and body man Nicholas Luna, have also now been confirmed positive.

The final point is that this goes to show how much the administration does not take the pandemic seriously. Right now the Covid data for some states indicates that the virus is beginning to spread once again. And so maybe this serves as a good reminder to the general public.

Just because you are socialising outdoors does not make you safe. Outdoors is better than indoors. No gatherings is better than small gatherings is better than large, well attended garden parties. Masks are better than no masks. Rapid result test screening is better than no test screening. Temperature checks are better than no temperature checks.

But the White House only did that last one, temperature checks, in order to protect the president before admitting people to the Rose Garden. Compare that to how they protect the president from other physical threats. He has Secret Service agents standing near him (or riding with him in hermetically sealed SUVs for joyrides whilst he is infected and contagious); he has checkpoints and armed fences further out to secure the perimeter. Scouts and snipers are on the White House roof for longer range threats. And there is a command centre coordinating this with I presume CCTV and aerial surveillance to monitor things even further out. In short, a multi-layered defence keeps the president safe.

If you just take temperatures; if you just hang out outside; if you just wear masks; if you just do one of those things without doing the others I mentioned above, you are putting yourself—and through both pre-diagnostic/pre-symptomatic and asymptomatic spreading, others—at risk.

But on Sunday night, Trump campaign strategist went on television said that now that President Trump has been infected, been hospitalised, he is ready to lead the fight on coronavirus. Great. We need leadership.

But where was that leadership seven months ago when your advisors told you in January about the impact this pandemic would likely have on the United States? Where was the leadership in February saying the coverage was a hoax? Where was it in March when he said the virus would go away in April with the warmer weather? Where was it in April when it didn’t go away, when things continued to get worse? Where was it in May when thousands of Americans were dying? Where was it in June when states began to reopen even though the virus was still out-of-control and testing and contact tracing was less available than necessary to contain outbreaks? Where was it in July? And August? And September? Where was the leadership at a Rose Garden party celebrating the nomination of a Supreme Court justice, a party where at least seven people have been infected and one of them, the president of the United States, has been hospitalised with moderate to severe symptoms?

Credit for the piece goes to the BBC.

Text Me

Well that was a week. Thankfully we all made it to Friday.

Now, your humble author is an old millennial, which means I remember a time before the Facebook and the Whatsapp and the internets. I also remember the first carphones and mobile bricks and flip phones. But what’s stayed consistent amongst all these platforms? It’s the humble old text message. Yep. Those still work. (And your humble author prefers it for his personal communications.) The technical term for a text is SMS. And despite approaching 30 years old, it’s still a widely used channel. If someone has a phone, but they don’t have Facebook? You can text them. No Whatsapp? Text. No Slack? Text. No Teams? Text. No Skype? Text. No whatever? Text.

xkcd recently looked at the ye olde SMS and its perseverance.

I don’t even know what half of these are. Get off my lawn.

And after all that, if you still want to come at me, bro, just text me first.

Credit for the piece goes to Randall Munroe.

Parties in Pennsylvania

This is from a social media post I made a few days ago, but think it may be of some relevance/interest to my Coffeespoons followers. I was curious to see at 30+ days from the general election, how has the landscape changed for the two parties since 2016?

Well, this project has driven me to a related, but slightly different project that has been consuming my non-work time. Hopefully I will have more on that in the coming days. Without further ado, the post:

Pennsylvania will likely be one of the more critical battleground swing states in this year’s election. In 2016, then candidate Trump won the state by less than one percentage point. But four years is a long time and I was curious to see how things have changed.

In the first chart on the right we see counties won by Trump and on the left, Clinton. The further from the centre, the greater the candidate’s margin of victory over the other. The top half plots registered Republicans’ margin over Democrats as a percentage of all registered voters in the county (including independents and third party) and the bottom half does the same for Democrats. Closer to the centre, the more competitive, further away, less so.

Trump’s key to victory was the white, working class voter clustered in the west and the northeast of the state–old mining and steel towns. There Democrats normally counted on organised labour support as registered Democrats. That all but collapsed in 2016. The bottom right shows a number of nominally Democratic counties Trump won, whereas Clinton only picked up one Republican county, Chester.

But what are PA’s battlegrounds?

In the second chart we ignore places like Philly and Fulton County and zoom in on more competitive counties within 20 point margins. Polls presently point to a Biden lead of about 5 points in PA. If every dot moved left by 5 points (it doesn’t really work like that), we only see Erie and Northampton with potential to flip.

But Trump’s realignment of politics is accelerating (more on this another day) a realignment of PA’s political geography.

In the fourth chart, neither Erie nor Northampton show any real movement via party registration back to Democrats. Erie may flip, but Northampton’s likely a stretch. Places like Cumberland and Lancaster counties are too solidly Republican to flip this year. Instead Trump is more likely to flip counties like Monroe and Lehigh red, even if he loses the state.

Because, not shown, the key to a Biden victory will be running up the margins in Philly & Pittsburgh, and to a lesser extent Philly’s four collar counties, including Chester, which appears to be rapidly shifting in Democrats’ favour.

Credit for the piece is mine.