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.

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.

Covid-19 Update: 28 September

Apologies for the lack of posting, work is pretty busy as we wrap some projects up. But here’s a look at the latest Covid data for Pennsylvania, New Jersey, Delaware, and Illinois. Normally we look at Virginia as well, but their site was down for maintenance and so there was no data to report.

When it comes to new cases, we have on the one hand places like New Jersey and Illinois, where new cases continue to rise. The rate is nowhere near as fast as it was in March and April, but the inclines are clearly there. Delaware has been up and down, but largely hovering around just shy of 100 new cases per day. Pennsylvania is a bit harder to tell because of some dramatic swings that have knocked the average around, but it does appear to be trending upward, though I’m not quite as confident in that as I am with New Jersey and Illinois.

New cases curve in PA, NJ, DE, and IL.

And then when we look at deaths, we generally have good news. Last week we were looking at Virginia and its working through a backlog of unreported deaths. That artificially inflated recent days, but also depressed deaths earlier in the pandemic. Beyond the old Dominion, however, deaths have remained fairly low. Only in Pennsylvania and Illinois do they hover around 20 deaths per day from the virus.

Death curves in PA, NJ, DE, AND IL.

Credit for the piece is mine.

Covid-19 Update: 21 September

Apologies for the lack of posting yesterday, but I wasn’t feeling well. I had some other things planned for today, but then some other things happened this weekend and then I took ill. But it’s still important to look at what’s going on with the pandemic, especially in the United States where it’s been disastrously handled by the White House.

As we approach 200,000 dead Americans, we still look at what’s going on in the tristate region alongside Virginia and Illinois. Specifically we compare last week’s post to this week’s post. Note that normally we look at Sunday data on Monday morning and today we’ll be looking at Monday data on a Tuesday. Both Sunday and Monday are reports from their preceding days, and so we are still looking at weekend reporting of figures. So we can expect them to be lower than workweek data.

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

If we compare the above chart to last week’s, we can see that Pennsylvania has decidedly reversed course. Whereas things had been headed down in terms of averages, I was worried about the days of daily new cases exceeding the average. Sure enough the average has caught up to the new cases and we’re seeing a rise in the average to levels not really seen since the summer.

New Jersey remains on the path of slowly increasing its numbers of new cases. Delaware looks to be heading back down after a small bump. We might be seeing the beginning of a decline in cases in Virginia, down from its long-running plateau of nearly 1000 new cases per day. And finally in Illinois, it’s not quite clear where things are headed at present. But for the one-day spike that raised the average, it seemed as if new cases had been in decline, but the end of that otherwise decline might have been an inflection point as the average may be trending back upwards again.

Death curves in PA, NJ, DE, VA, & IL.

Then when we look at deaths, well we see no real significant change in four of the states. But last week, we were saying Virginia was at a good spot with its latest surge cycle coming to an end. Well now look at that spike and deaths that are higher now than they were in the spring. If you follow my daily posts on social media, you’ll know that there’s a reason for this.

For the last week Virginia has been working through a backlog of deaths that were not entered into its electronic database. And so these deaths happened over the last several months. Consequently the rise, if there even is one, is not nearly as high as shown. But it also means that the earlier peaks may have been far higher than reported at the time.

Credit for the pieces is mine.

Positioning Is Important

Yesterday Pew Research released the results of a survey of how the rest of the world views select countries throughout the world. The Washington Post covered it in an article and created some graphics to support the text. The text, of course, was no big surprise in that the rest of the world views the United States poorly compared to just several years ago and that, in particular, President Trump is a leader in whom the world has no confidence.

But that’s not what I want to talk about. Instead, I want to address a design element in the one of their graphics. (But you should go ahead and read about the survey results.)

The issue here is the positioning of the labels for each bar, representing a world leader. At the very top of the graphic, things are in a good way. We have Merkel with a small space beneath that text then another label, “No confidence, 19 percent”, and then a connecting line to a dot to the blue bar. We then have a small space and the label Macron, meaning we have moved on and are on the next world leader.

But what if the reader sees the title and starts towards the bottom? They want to see the leaders in whom the world has no confidence. Now look at the bottom of the chart and the positioning of the labels for Trump, and above him, Xi, Putin, and maybe even Johnson. Because the “No confidence, x percent” labels have moved further to the right, there is an enormous space between the leader’s name and their coloured bar. Visually, this creates a link between the leader’s name and the preceding bar. For example, Trump appears to have a no confidence value of 78 with an unlabelled bar chart beneath him.

I suggest that there are two easy fixes to better link the labels to the data. The first is to move the leaders’ labels down, once the “No confidence” label has moved sufficiently far to the right. Like so.

The leader is now very clearly attached to his or her data with little confusion.

My second option is to fix the “No confidence” labels permanently to the left of the chart so as not to create that visual space in the first place, like so.

Here, after seeing the first option, I wonder if there is enough visual space at all between the leaders. But, this is only a quick Photoshop exercise. If I wanted to really tweak this, I would consider putting the data point or number in bold to the right of the label.That would eliminate an entire line of type that could be repurposed as a visual buffer between leaders.

I think either option would be preferable because of increased clarity for the reader.

Credit for the piece goes to the Washington Post graphics department.

It’ll Get Cooler Eventually

President Trump, on climate change.

I mean, technically he’s correct. Eventually the universe will likely end with heat death as all the energy dissipates and stars die out and space becomes a truly empty, cold void. So it’ll get cooler, eventually.

But what about right now? In one to three generations’ time? 30–90 years? Not looking so great.

So what sparked this ludicrous comment? This year’s wildfire season on the West Coast, usually relegated to California, this year’s season has burned up forests in both Washington and Oregon as well, states whose usually wetter climate inhibits these kind of rapidly spreading fires.

A few days ago the Washington Post published a piece looking at the fires out west. It started with a map showing ultimate fire perimeters and currently active fires.

In a normal year, those fires in Oregon and Washington wouldn’t be there. Welcome to the new normal.

Frequent readers will know I’m not a fan of the dark background for graphics, but I’m betting it was chosen because as you scroll through the article, it makes the photo journalism really pop off the page. Contrast the bright yellows, oranges, and reds with a dark black background and c’est magnifique, at least from a design standpoint. And given this piece is really about the photography depicting the horrors on the West Coast, it’s an understandable design decision.

Credit for the piece goes to Laris Karklis.

Covid-19 Update: 13 September

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.

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

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.

Death curves in PA, NJ, DE, VA, & IL.

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.

Credit for the pieces is mine.

Covid-19 Update: 7 September

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.

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

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.

Death curves in PA, NJ, DE, VA, & IL.

Credit for the pieces is mine.