Pence’s Jobs Claim: Fact Checked

I watched Vice President Prence’s convention speech last night. He made several claims, including one about the Trump administration creating 9.3 million jobs over the last three months.

That’s a lot of jobs. So I wanted to check and see if that was true.

It is. over the last three months, the administration has created 9.3 million jobs.

But in the two months before that?

They lost 22.2 million jobs.

They have another 12.9 million jobs to create just to get back to where we were, and that doesn’t include the jobs required for the natural increase in population and immigration.

Credit for the piece is mine.

The UK’s Most Popular Baby Names

Today’s piece comes from a BBC piece that visualises the most popular baby names in the UK along with the largest winners and losers in name popularity. The article leads with the doubling of babies with the name Dua, from a singer named Dua Lipa, and more than doubling those with the name Kylo, from a character in Star Wars. Of course, those are not the most popular names in the United Kingdom. For boys it’s presently Oliver and for girls, Olivia.

Naturally the piece has a bar chart for each sex and their ten most popular names. But later on in the piece we see two set of graphics that look at those names with the fastest rises or declines in popularity. I chose to screenshot the winners.

It makes use of essentially sparklines, a concept that features small line charts that really focus on direction instead of levels. Note the lack of axis labelling to inform the reader the line’s minimum and maximum. Instead the minimum and maximum are the absolute vertical range of the line.

What this chart attempts to do, however, is hint at those ranges through colour. By using a thicker weight, the line encodes the number of names in the colour. Compare Arthur, whose line ends in a dark bluish colour, to that of Arlo or Grayson, whose names also end in their peak, but in a light bluish colour. All three names have risen, but in terms of absolute levels, we see far more Arthurs than Graysons. Holy popularity, Batman.

When it comes to communicating the size of the names’ popularity, I am not entirely convinced about the idea’s efficacy. But, it lands more often than not. Can I compare Ada to Hallie? No, not really. But Ada vs. Theo is fairly clear.

Could the same effect be accomplished by a sorting order? Say the names were grouped by those who have numbers in 2019 that fall between 3,000 and 4,000, then another range of 1,000–2,000, and so on.

I also wonder if the colours in the bar charts could have been linked to those of the rising and falling names? Keep dark green for the boys’ names and purple for the girls’. It could have made a more solid thematic link between the graphics. As it is now, there seems no rhyme nor reason for the colour choices.

Finally the article has two tables that list the most popular names for each sex for each region. There’s nothing really to improve in the table’s design. The rules dividing rows and columns are fairly light so we don’t have to highlight that usual fault.

Overall, it’s a strong article with some nice visualisations.

Credit for the piece goes to the BBC graphics department.

A Foot by Any Other Name

Measurement systems are important. They allow us to compare objects, buy and sell goods, and get from Chicago to Philadelphia. The latter, according to Google, is 759.6 miles. Or 4,010,688 feet.

But what feet?

In this piece from the New York Times we get a look at the two different foot measurements used in the United States. The article provides insight into the history of why we have a standard system of measurement.

Accompanying the wonderful article is an illustration showing how those two feet differ. It’s a simple, scaled illustration. But it does the job.

Of course we would all be better off if the United States joined the rest of the world in using the metric system. Like that time we lost a space probe because we failed to convert from English imperial to metric.

Credit for the piece goes to Eleanor Lutz.

Double Your Hurricanes, Double Your Fun

In a first, the Gulf of Mexico basin has two active hurricanes simultaneously. Unfortunately, they are both likely to strikes somewhere along the Louisiana coastline within approximately 36 hours of each other. Fortunately, neither is strong as a storm named Katrina that caused a mess of things several years ago now.

Over the last few weeks I have been trying to start the week with my Covid datagraphics, but I figured we could skip those today and instead run with this piece from the Washington Post. It tracks the forecast path and forecast impact of tropical storm force winds for both storms.

The forecast path above is straight forward. The dotted line represents the forecast path. The coloured area represents the probability of that area receiving tropical storm force winds. Unsurprisingly the present locations of both storms have the greatest possibilities.

Now compare that to the standard National Weather Service graphic, below. They produce one per storm and I cannot find one of the combined threat. So I chose Laura, the one likely to strike mid-week and not the one likely to strike later today.

The first and most notable difference here is the use of colour. The ocean here is represented in blue compared to the colourless water of the Post version. The colour draws attention to the bodies of water, when the attention should be more focused on the forecast path of the storm. But, since there needs to be a clear delineation between land and water, the Post uses a light grey to ground the user in the map (pun intended).

The biggest difference is what the coloured forecast areas mean. In the Post’s versions, it is the probability of tropical force winds. But, in the National Weather Service version, the white area actually is the “cone”, or the envelope or range of potential forecast paths. The Post shows one forecast path, but the NWS shows the full range and so for Laura that means really anywhere from central Louisiana to eastern Texas. A storm that impacts eastern Texas, for example, could have tropical storm force winds far from the centre and into the Galveston area.

Of course every year the discussion is about how people misinterpret the NWS version as the cone of impact, when that is so clearly not the case. But then we see the Post version and it might reinforce that misconception. Though, it’s also not the Post’s responsibility to make the NWS graphic clearer. The Post clearly prioritised displaying a single forecast track instead of a range along with the areas of probabilities for tropical storm force winds.

I would personally prefer a hybrid sort of approach.

But I also wanted to touch briefly on a separate graphic in the Post version, the forecast arrival times.

This projects when tropical storm force winds will begin to impact particular areas. Notably, the areas of probability of tropical storm force winds does not change. Instead the dotted line projections for the paths of the storms are replaced by lines relatively perpendicular to those paths. These lines show when the tropical storm winds are forecast to begin. It’s also another updated design of the National Weather Service offering below.

Again, we only see one storm per graphic here and this is only for Laura, not Marco. But this also probably most analogous to what we see in the Post version. Here, the black outline represents the light pink area on the Post map, the area with at least a 5% forecast to receive tropical storm force winds. The NWS version, however, does not provide any further forecast probabilities.

The Post’s version is also design improved, as the blue, while not as dark the heavy black lines, still draws unnecessary attention to itself. Would even a very pale blue be an improvement? Almost certainly.

In one sense, I prefer the Post’s version. It’s more direct, and the information presented is more clearly presented. But, I find it severely lack in one key detail: the forecast cone. Even yesterday, the forecast cone had Laura moving in a range both north and south of the island of Cuba from its position west of Puerto Rico. 24 hours later, we now know it’s on the southern track and that has massive impact on future forecast tracks.

Being east of west of landfall can mean dramatically different impacts in terms of winds, storm surge, and rainfall. And the Post’s version, while clear about one forecast track, obscures the very real possibilities the range of impacts can shift dramatically in just the course of one day.

I think the Post does a better job of the tropical storm force wind forecast probabilities. In an ideal world, they would take that approach to the forecast paths. Maybe not showing the full spaghetti-like approach of all the storm models, but a percentage likelihood of the storm taking one particular track over another.

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

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

Reducing Postal Sort Capacity

For my non-American audience, the United States uses a federal system under which its constituent states retain the responsibility for organising and executing elections. And so we have 50 different electoral systems. A select few use the United States Post Office (USPS) to distribute blank ballots to voters and collect them when completed. Five states have used this system without issue for years (and infinitesimally small issues of fraud): Colorado, Hawaii, Oregon, Utah, and Washington.

But with the United States having failed to adequately deal with its Covid-19 outbreak, see yesterday’s post, most US states will be expanding their mail-in ballots to help protect voters and keep them safe. But this all depends upon the USPS. The Trump administration fears losing the election and in a press conference Trump admitted aloud that he wants to withhold funding from the USPS to prevent people from voting.

What does that look like? Well, Trump appointed a new postmaster general to carry out his wishes and the Washington Post created this graphic to show where the USPS has reduced the sorting capacity, a critical part of the delivery of postal ballots.

Is this why my mail is taking longer than usual?

Often I will write about how I don’t like the use of circles and their measurement by area.

First, the advantage of the circles here is that they are tied to specific geographic sites, and they do not refer to geographic areas like counties, states, or regions. So in this case, this is a plus.

Second, the circles appear to not be sized by area, but maybe by diameter. I would need more time to investigate this, but the areas look off. But I should add I do like how the largest postal facility impacts are called out by labels, and those in heavily clustered areas are numbered and placed off the southeast seaboard.

Third, I’m not really sure why the colours are necessary, or rather, what changing the colours adds given that the sizes of the circles is already changing.

So while I have some issues with what’s going on here, the content itself is critically important for people to see. Note that a number of the largest postal facilities by impact are located in Ohio, Pennsylvania, Michigan, and Texas. And Florida has a lot of medium-sized circles. And while Texas is likely still a Republican state in the electoral college, Biden is currently polling within a good night’s results’ reach of Trump there. The other states are all solidly swing states up for grabs or with Biden leading by some degree in the current polling.

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

Is Covid-19 Surging in New Zealand?

Yesterday, President Trump claimed that Covid-19 was “surging” in New Zealand, a country widely lauded as having successfully contained and suppressed their outbreak. That has allowed Wellington to reopen large swathes of their economy without incident.

Until this surge.

And by surge we mean something like 30 cases in 3 days. So, let’s compare that surge to the numbers of new cases in the United States.

Now, to be fair, New Zealand has a population of nearly 5 million, the United States has nearly 335 million. So a direct number-to-number comparison of the number of new cases per day isn’t fair.

So let’s look at the number of new cases per million people, which equalises the data for population.

So yeah, New Zealand is not “surging”. The data shows that even with the more limited testing per capita conducted in the United States, we are nowhere near the point of bending the curve anywhere close to zero.

Credit for the piece is mine.

Covid-19 Update: 16 August

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 .

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

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.

Deaths in PA, NJ, DE, VA, and IL
Death curve in PA, NJ, DE, VA, and IL

Credit for the piece is mine.

When Is an Opener Game in Baseball Really Just a Bullpen Game?

Whenever someone not named Eovaldi or Perez starts a Red Sox game in 2020, that’s when.

We all know the Red Sox are the worst team in the American League. They have only two starters, maybe sometimes a third. And then the last two days of the regular five-day rotation cycle, manager Ron Roenicke throws some relievers at the wall and sees which ones stick that night. Spoiler: Few do.

But as much as I enjoy listening to the three-man broadcasting booth (Remy and Eck make the games fun to at least listen to) the games are unwatchable. And then to hear them try and dress a game up as having an opener? Well, what is the opener?

For the non-baseball fans, most are probably aware enough that some guy goes out to a small hill and throws (pitches) a ball at a batter for most of the night. Then towards the end, when the guy’s energy wanes, he is replaced by some guy who throws really fast. That’s over simplified, but that’s a normal ballgame. A starting pitcher records five, but ideally at least six, innings of work before handing the ball over to an eighth-inning setup man and then a ninth-inning closer. Sometimes a really good seventh-inning reliever sets up the setup man.

A bullpen game, by contrast, is when a bunch of those relief pitchers handle the entire game. Usually this would be after a game went into extra innings (since baseball cannot end in a tie, unless you’re in an All Star game), and the next day’s starting pitcher had to finish the long game by pitching several innings. With nobody available to throw six innings, a bunch of relievers come in and try to cover that by pitching one, two, or three innings each.

The opener game is relatively new. The idea is in addition to the really good closer, a really good opener records the first inning or two (3–6 outs) to deal with the opposing team’s best hitters. He then hands the ball over to a mediocre starting pitcher who throws the next four or five innings, who then hands the ball over to the late-inning relief specialists. Doing it this way, the starter avoids one set of at-bats or plate appearances by the opposition’s best hitters.

But when is an opener just a bullpen game? Well, it’s when that mediocre starting pitcher isn’t really a starting pitcher. And when he doesn’t even throw four or five innings. Basically all the Red Sox games this year.

I made a graphic this morning to contrast those different types of games and compare them to a game I watched two nights ago between the Red Sox and the Tampa Bay Rays. The game was teed up as an opener with a good relief pitcher by the name of Ryan Brasier starting the first inning. But then instead of a mediocre starter pitching four or five innings, we got a mediocre reliever pitching three innings. He handed it over to a guy who was supposed to go maybe two, but couldn’t get through his second inning. He handed it over to another guy, who handed it over to another guy, who handed it over to a final guy. And none of those last guys were the good relievers you would typically expect to see. (Though, to be fair, the Sox weren’t winning, so why use your best relievers?)

What is an opener and how does it compare to a bullpen game?
Different game types compared

Credit for the piece is mine.

Credit for the Red Sox dumpster fire of a season goes to John Henry and ownership.

Sweet Summer Air of Subway Cars

For those of my readers who live in a city where the subway or underground is a great means of getting around the city, you know you really miss that late Saturday night/early Sunday morning bouquet in the air. Though as this New York Times piece explains, sure it smells bad, but that air is probably safer than you dining indoors at a restaurant or even a child attending class in person.

The piece focuses on New York City subway cars, but they are very similar to the rest of the stock used in the United States. It uses a scrolling reveal to show how the air circulation and filtration systems work. Then it concludes with a model of how a person sneezing appears, both with and without a mask. (Spoiler, wear a mask.)

It’s a really nicely done and informative piece. It compares the rate of air recycled in a subway car to that of several other locations, and the results were a bit surprising to me. Of course, early on in the pandemic before we began to fully understand it, the threat was thought to be from contaminated surfaces—and let’s be honest, there are a lot of contaminated surfaces in a New York City subway car—but we now know the real risk is particles breathed/coughed/sneezed out from one’s mouth and nose. And we can now see just how efficient subways are at cycling and filtering that air.

Credit for the piece goes to Mika Gröndahl, Christina Goldbaum, and Jeremy White.