Coffee Spoons – Examining How We Measure Our Lives

Modelling the Impact of Not Sheltering in Place or Staying at Home

The administration botched the early stages of the COVID-19 pandemic. Only within the last two weeks have states acted to begin enacting dramatic policies aimed at slowing the spread of the virus through their communities. But what policies the federal government has enacted are now threatened by an administration that prioritises the economy and market over the lives of the citizens it leads. The White House is discussing loosening all the policies of social distancing that health officials and scientists say are necessary to slow the spread of the virus.

This website from CovidActNow.org uses a model to predict the impact state by state of various policies on hospital overcrowding and ultimately deaths. The site opens with a map of the United States showing, broadly, what kind of response each state has followed (understanding things change rapidly these days).

The state of reactions in the United States

That also serves as the navigation for a deep dive into those models for that state. Here I have selected my home state of Pennsylvania. It borders New Jersey and New York, two states that revolve, at least in part, around New York City, rapidly becoming the epicentre of the US outbreak, supplanting Seattle and the Pacific Northwest. What would the state face if we allowed things to keep going blithely on? What would happen if we merely socially distance for three months? What if we shelter in place for three months? (Emphasis added by me to show this is a long-term problem.)

Turns out that things don’t work out that well if we don’t stay at home, stop travelling, stop socialising. A table below the line charts shows the user how bad things go for the state in a table.

As you can see, for Pennsylvania, if we were to continue going on like normal, that would result in the deaths of almost the size of the entire city of Pittsburgh. Imagine if the city of Pittsburgh were suddenly wiped off the state map. That’s the level we are talking about.

Just three months of just social distancing? Well now you’re talking about wiping out just the cities of Allentown and Scranton.

Sheltering in place for three months, statewide? Well, thankfully Pennsylvania has lots of towns around the size of 5000 to choose from. Imagine no more Paoli, or Tyrone. Or maybe a Collegeville or Kutztown. An Oxford or a Media. Pick one of those and wipe it from the map.

Fundamentally the choice comes down to, do you want to restart your economy or do you want to save lives? Saving lives will undoubtedly mean unemployment, shattered 401k plans, bankruptcies, mental health problems, and cities, towns, and industries devastated without a tax base to provide for the necessary services. But, saving those jobs and dollars will means tens if not hundreds of thousands of deaths.

I don’t envy the state executive branches making these decisions.

Pennsylvania has chosen a middle road, if you will. It enacted a stay-at-home policy for seven counties: Allegheny (Pittsburgh); Philadelphia and its suburban counties of Bucks, Chester, Delaware, and Montgomery; and Monroe County. The rest of the state, primarily where the virus has yet to make any real significant appearance or appears to be spreading in the community, is not under the strictest of measures. This site’s model doesn’t account for a partial, statewide stay-at-home, but Pennsylvania’s choice is clearly a far superior one for people who prioritise lives over dollars.

Finally, to the people I have seen from my apartment gathering in parks, partying in outdoor spaces, that I can hear throwing house parties, please stop. If not for you, for the rest of us.

Credit for the piece goes to CovidActNow.org.

The Spread of COVID-19 in Select States

By now we have probably all seen the maps of state coverage of the COVID-19 outbreak. But state level maps only tell part of the story. Not all outbreaks are widespread within states. And so after some requests from family, friends, and colleagues, I’ve been attempting to compile county-level data from the state health departments where those family, friends, and colleagues live. Not surprisingly, most of these states are the Philadelphia and Chicago metro areas, but also Virginia.

These are all images I have posted to Instagram. But the content tells a familiar story. The outbreaks in this early stage are all concentrated in and around the larger, interconnected cities. In Pennsylvania, that means clusters around the large cities of Philadelphia, Pittsburgh, and Harrisburg. In New Jersey they stretch along the Northeast Corridor between New York and Trenton (and along into Philadelphia) and then down into Delaware’s New Castle County, home to the city of Wilmington. And then in Virginia, we see small clusters in Northern Virginia in the DC metro area and also around Richmond and the Williamsburg area. Finally in Illinois we have a big cluster in and around Chicago, but also Springfield and the St. Louis area, whose eastern suburbs include Illinois communities like East St. Louis.

I have also been taking a more detailed look at the spread in Pennsylvania, because I live there. And I want to see the rapidity with which the outbreak is growing in each county. And for that I moved from a choropleth to a small multiple matrix of line charts, all with the same fixed scale. And, well, it doesn’t look good for southeastern Pennsylvania.

Then last night I also compared the total number of cases in Pennsylvania, New Jersey, Delaware, and Virginia. Most interestingly, Pennsylvania and New Jersey’s outbreaks began just a day apart (at least so far as we know given the limited amount of testing in early March). And those two states have taken dramatically different directions. New Jersey has seen a steep curve doubling less than every two days whereas Pennsylvania has been a bit more gradual, doubling a little less than every three.

For those of you who want to continue following along, I will be looking at potential options this coming weekend whilst still recording the data for future graphics.

Credit for the pieces is mine.

The Results from St. Paddy’s Primaries

On Tuesday the United States had another round of primaries, this time in Arizona, Florida, and Illinois. Ohio was scheduled to be the fourth, but its governor postponed the vote to some point in the future due to the ongoing coronavirus COVID-19 epidemic.

As my regular readers all know, I thoroughly enjoy election season because we get all the data to look at and try to explain what is going on. And more data means more graphics, so I doubly enjoy it. And so the last few weeks have been exciting. But while there were twists and turns and excitement the last several weeks, we walked into Tuesday night with a common hypothesis or story line. Biden would probably win all three states, though Latinos could help Sanders in Arizona in a long shot, or an anticipated depressed turnout in Illinois due to COVID-19 could allow Sanders to pick up a victory there.

The results did not surprise most: Biden beat Sanders decisively in all three states. And over the last two weeks, that put eight of the nine states in play into Biden’s camp. Furthermore, Biden has won the majority of states now since primary season kicked off with that disastrous caucus in Iowa. You can see that in this graphic that I made Tuesday night and posted on Instagram. (All the images that follow I posted on Instagram that night and the night after Michigan voted.)

But beyond the top line figure of who won which state, I wanted to look at some of the underlying data on Sanders’ support. Specifically, the margins in the head-to-head contests seemed, as far as I could recall, vastly different than the close run races between Sanders and Clinton in 2016. For this, I had to discount everything up to and including Super Tuesday. All those contests featured multiple centrists/moderates and multiple leftist/progressives and that muddied up the data.

Instead I focused on the states over the last two weeks. (I noted that Washington and Idaho had switched from caucus to primary in an effort to draw out more Democratic voters, in an attempt to generate more support for Sanders’ campaign.) And I discovered that in every state, without exception, Sanders performed worse in 2020 than he had in 2016.

Sanders underperformed in every head-to-head match up this year relative to his numbers against Clinton in 2016

I hypothesise that one factor in this underperformance relative to 2016 is that he has Joe Biden as a foil instead of Hillary Clinton. Before the campaign began, Biden was one of the most favourably regarded Democratic politicians. There’s a reason people have given him the nickname Uncle Joe (though his gaffes certainly are another factor in that sobriquet). Conversely, Hillary Clinton was, well, suffice it to say, not popular. She was eminently qualified, but had low favourability numbers.

First, we can clearly establish that Sanders enjoys a strong and loyal core base of support. With squinting eyes and a little bit of spit, we can say the floor is probably in the 20–25% range of the Democratic voting bloc. Clearly in some states it is far higher, in others far lower. But was the 2016 vote, where, relative to 2020, he over-performed perhaps due to that low favourability of Clinton?

One possible hypothesis is that Sanders suffers from lower turnout than 2016. After all, his own campaign makes the point that he will bring out a larger, more diverse, and crucially younger electorate to propel him to victory.

On the youth front, one of the drawbacks to the coronavirus outbreak is that this week we had no exit poll data to understand the demographic breakdown of the voters in Arizona, Illinois, and Florida. (And I highly doubt we will see exit polling through the next several sets of primaries.) But we could take a look at it last week, and I did with the crucial state of Michigan. After all, that had been Sanders’ surprise upset that allowed him to carry on almost into the convention. But, what I found was that youth turnout was actually lower, proportionally, in 2020 than it was in 2016.

In Michigan the youth vote was largely down, though a tarnished silver lining would be 25–29 year olds held steady

So, if the idea was that Sanders would be turning out the youth vote, well, it should have gone up and not down. Again, a more robust check on this hypothesis would be to look at exit poll data from more states, but right now we have few states to do that. Washington, which voted the same night as Michigan, would be a fascinating study, except it switched from caucus to primary and so the numbers cannot be directly compared.

But it also wasn’t just the youth vote was down proportionally. In fact, a look at that Michigan data shows how Sanders had a lower share in most demographics, only seeing a rise in the 40–49 cohort.

Sanders' support in Michigan was down in most demographic cohorts — Sanders’ support in Michigan was down in most demographic cohorts

If the youth (and potentially others) vote is down, maybe overall lower turnout is what hampers Sanders’ performance. Here, I looked at the total number of ballots cast in the states over the last two weeks. I noted that Illinois in particular would be lower given the outbreak as both Florida and Arizona do a significant number of mail-in ballots. And what I found is that, no, turnout is actually up almost across the board.

Turnout is generally up in all the head-to-head states.

And so we can see clear evidence that despite Sanders’ strong and fervent support with his core supporters, he has been unable to grow that support and build a coalition. His share of the vote fell in every state where we had a head-to-head race and yet turnout is up, which runs counter to the argument of his campaign.

Sanders is clearly underperforming in 2020. 2016 was the year that people felt the Bern. Maybe in 2020 a significant number of his 2016 supporters are Berned out.

Credit for the pieces goes to me.

The Spread of COVID-19 in Pennsylvania

Over the last several days, along with most of the country, I’ve taken an interest in the spread of the novel coronavirus named COVID-19. Though, to be fair, it’s actually been in the news since early January, though early news reports like this from the Times, simply called it a mysterious new virus. At the time I thought little of it, because the news out of China was that it did not appear it could spread amongst humans. How did that idea…wait for it…pan out?

Anyway, over the last couple of days I’ve been making some maps for Instagram because people tend to look at a national map and see every nearly state infected when, in reality, there are pockets and clusters within those states. So I started looking at Pennsylvania. And initially, the cluster was along the Delaware River, namely Pennsylvania as well as its upper reaches near the Lehigh Valley and in the far northeast of the state.

But the spread has grown, and fairly quickly, with Montgomery County, a Philadelphia suburb, a hotspot. Consequently, the Pennsylvania governor has shut down all schools across the state and ordered non-essential shops, restaurants, and bars in the counties surrounding Philadelphia—as well as the county containing Pittsburgh—closed.

So 11 days in, here’s where we stand. (To be fair, I looked at including the early numbers out of today, but nothing has really changed, so I’ll wait until the evening figures are released before I update this again.)

Credit is mine. Data is the Pennsylvania Department of Health.

The Mookie Betts Trade

Apologies for the lack of posting the last few months. There are several things going on in my life right now that have prevented me from focusing on Coffee Spoons as much as I would like. I will endeavour to resume posting, but it might not be the daily schedule it had been for at least a little while longer.

Onwards and downwards to the title—one of the dumbest, stupidest, worstest things the Boston Red Sox have done. At least in my lifetime. But also probably in all time. Except Babe Ruth to the Yankees. Also dumb. I’m so upset by this trade I’m using my words good.

The Red Sox had some financial difficulties, or so they claimed. Their payroll was one of the highest in baseball and was over an arbitrary line called the luxury tax, above which teams incur penalties. Repeat offenders pay increased fines, lose draft picks, &c. Boston was a repeat offender and was set to be again with several large contracts on the books.

Instead of sucking it up for a year and fielding a competitive team, the Red Sox dumped a huge chunk of their salary by trading away their star player, maybe baseball’s second best, and their second best pitcher. For a good, not great, outfielder, a fringe-y second baseman, and an even fringier catcher. But mostly they got salary relief. And the 2020 Sox are going to be painful to watch.

Anyway, I made a graphic about this complete suckfest. Because it sucks.

Credit for this awfulness goes to Chaim Bloom, the new president of Red Sox baseball operations. But the graphic is mine.

Axis Lines in Charts

The British election campaign is wrapping up as it heads towards the general election on Thursday. I haven’t covered it much here, but this piece from the BBC has been at the back of my mind. And not so much for the content, but strictly the design.

In terms of content, the article stems from a question asked in a debate about income levels and where they fall relative to the rest of the population. A man rejected a Labour party proposal for an increase in taxes on those earning more than £80,000 per annum, saying that as someone who earned more than that amount he was “not even in the top 5%, not even the top 50”.

The BBC looked at the data and found that actually the man was certainly within the top 50% and likely in the top 5%, as they earn more than £75,300 per annum. Here in the States, many Americans cannot place their incomes within the actual spreads of income. The income gap here is severe and growing. But, I want to look at the charts the BBC made to illustrate its points.

The most important is this line chart, which shows the income level and how it fits among the percentages of the population.

Are things lining up? It's tough to say. — Are things lining up? It’s tough to say.

I am often in favour of minimal axis lines and labelling. Too many labels and explicit data points begin to subtract from the visual representation or comparison of the data. If you need to be able to reference a specific data point for a specific point on the curve, you need a table, not a chart.

However, there is utility in having some guideposts as to what income levels fit into what ranges. And so I am left to wonder, why not add some axis lines. Here I took the original graphic file and drew some grey lines.

Of course, I prefer the dotted or dashed line approach. The difference in line style provides some additional contrast to the plotted series. And in this case, where the series is a thin but coloured line, the interruptions in the solidity of the axis lines makes it easier to distinguish them from the data.

But the article also has another chart, a bar chart, that looks at average weekly incomes across different regions of the United Kingdom. (Not surprisingly, London has the highest average.) Like the line chart, this bar chart does not use any axis labels. But what makes this one even more difficult is that the solid black line that we can use in the line charts above to plot out the maximum for 180,000 is not there. Instead we simply have a string of numbers at the bottom for which we need to guess where they fall.

Here we don't even a solid line to take us out to 700. — Here we don’t even a solid line to take us out to 700.

If we assume that the 700 value is at the centre of the text, we can draw some dotted grey lines atop the existing graphic. And now quite clearly we can get a better sense of which regions fall in which ranges of income.

We could have also tried the solid line approach.

But we still have this mess of black digits at the bottom of the graphic. And after 50, the numbers begin to run into each other. It is implied that we are looking at increments of 50, but a little more spacing would have helped. Or, we could simply keep the values at the hundreds and, if necessary, not label the lines at the 50s. Like so.

The last bit I would redo in the bar chart is the order of the regions. Unless there is some particular reason for ordering these regions as they are—you could partly argue they are from north to south, but then Scotland would be at the top of the list—they appear an arbitrary lot. I would have sorted them maybe from greatest to least or vice versa. But that bit was outside my ability to do this morning.

So in short, while you don’t want to overcrowd a chart with axis lines and labelling, you still need a few to make it easier for the user to make those visual comparisons.

Credit for the original pieces goes to the BBC graphics department.

Thanksgiving Side Dishes

American Thanksgiving meals often feature elaborate spreads of side dishes. And everyone has a favourite. A common theme around the holiday is for media outlets to conduct surveys to see which ones are most popular where. In today’s piece we have one such survey from pollster YouGov. In particular, I wanted to focus on a series of small multiples maps they used to illustrate the preferences.

Big splashes of colour do not necessarily make for a great map

I used to see this approach taken more often and by this I hope I do not see a foreshadow of its comeback. Here we have US states aggregated into distinct regions, e.g. the Northeast. One could get into an argument about how one defines what region. The Midwest is one often contested such region—I have one post on it dating back to at least 2014.

Instead, however, I want to focus on the distinction between states and regions. This small multiples graphic is a set of choropleth maps that use side dish preferences to colour the map. Simple enough. However, the white lines delineating states imply different fields to be coloured within the graphic. Consequently, it appears that each state within the region has the same preference at the same percentage.

The underlying data behind the maps, at least that which was released, indicates the data is not at the state level but instead at the regional level. In other words, there are no differences to be seen between, say, Pennsylvania and New Jersey. Consequently, a more appropriate map choice would have been one that omitted the state boundaries in favour of the larger outlines of the regions.

More radically, a set of bar charts would have done a better job. Consider that with the exception of fruit salad, in every map, only one region is different than the others. A bar chart would have shown the nuance separating the three regions that in almost all of these maps is lost when they all appear as one colour.

I appreciate what the designers were attempting to do, but here I would ask for seconds, as in chances.

Credit for the piece goes to the YouGov graphics team.

Food Flows Connect Counties

For my American audience, this week is Thanksgiving. That day when we give thanks for Native Americans giving European settlers their land for small pox ridden blankets. And trinkets. Don’t forget the trinkets. But we largely forget about the history and focus on three things: family, food, and American football. Not necessarily in that order.

But this week I am largely going to want to focus on the food.

Today we can look at a graphic coming from a team of researchers at the University of Illinois who examined the flows of food across the United States, down to the county level. It helped produce this map that shows the linkages between counties.

To be sure, the piece uses some line charts and other maps to showcase the links, but the star is really this map. But aside from its lack of Alaska and Hawaii, I think it suffers from one key design choice: leaving the county borders black.

The black lines, while thin, compete with the faint blue lines that show the numerically small links between counties. Larger trade flows, such as those within California, are clearly depicted with thicker strokes that contrast with the background political boundaries of the counties. But the light blue lines recede into the background beneath the borders.

I wonder if a map of solid, light grey fills and white county borders would have helped showcase the blue lines and thus trade flows a little bit better. After all, the problem is especially noticeable in the eastern half of the United States where we have much geographically smaller counties.

Hat tip to friend and former colleague Michael Schaefer for sharing the article in question.

Credit for the piece goes to Megan Konar et al.

From Order to Chaos?

A few weeks ago we said farewell to John Bercow as Speaker of the House (UK). Whilst I covered the election for the new speaker, I missed the opportunity to post this piece from the BBC. It looked at Bercow’s time in office from a data perspective.

The piece did not look at him per se, but that era for the House of Commons. The graphic below was a look at what constituted debates in the chamber using words in speeches as a proxy. Shockingly, Brexit has consumed the House over the last few years.

At least climate change has also ticked upwards?

I love the graphic, as it uses small multiples and fixes the axes for each row and column. It is clean, clear, and concise—just what a graphic should be.

And the rest of the piece makes smart use of graphical forms. Mostly. Smart line charts with background shading, some bar charts, and the only questionable one is where it uses emoji handclaps to represent instances of people clapping the chamber—not traditionally a thing that happens.

Content wise it also nailed a few important things, chiefly Bercow’s penchant for big words. The piece did not, however, cover his amazing sense of sartorial style vis-a-vis neckties.

Overall a solid piece with which to begin the weekend.

Credit for the piece goes to Ed Lowther & Will Dahlgreen.

Casual Fails?

In a recent Washington Post piece, I came across a graphic style that I am not sure I can embrace. The article looked at the political trifecta at state levels, i.e. single political party control over the government (executive, lower legislative chamber, and upper legislative chamber). As a side note, I do like how they excluded Nebraska because of its unicameral legislature. It’s also theoretically non-partisan (though everybody knows who belongs to which party, so you could argue it’s as partisan as any other legislature).

At the outset, the piece uses a really nice stacked bar chart. It shows how control over the levers of state government have ebbed and flowed.

You can pretty easily spot the recent political eras by the big shifts in power.

It also uses little black lines with almost cartoonish arrowheads to point to particular years. The annotations are themselves important to the context—pointing out the various swing years. But from an aesthetic standpoint, I have to wonder if the casualness of the marks detracts from the seriousness of the content.

Sometimes the whimsical works. Pie charts about pizza pies or pie toppings can be whimsical. A graphic about political control over government is a different subject matter. Bloomberg used to tackle annotations with a subtler and more serious, but still rounded curve type of approach. Notably, however, Bloomberg at that time went for an against the grain, design forward, stoic business serious second approach.

Then we get to a choropleth map. It shows the current state of control for each state.

X marks the spot?However, here the indicator for recent party switches is a set of x’s. These have the same casual approach as the arrows above. But in this case, a careful examination of the x’s indicates they are not unique, like a person drawing a curve with a pen tool. Instead these come from a pre-determined set as the x’s share the exact same shape, stroke lengths and directions.

In years past we probably would have seen the indicator represented by an outline of the state border or a pattern cross-hatching. After all, with the purple being lighter than the blue, the x’s appear more clearly against purple states than blue. I have to admit I did not see New Jersey at first.

Of course, in an ideal world, a box map would probably be clearer still. But the curious part is that the very next map does a great job of focusing the user’s attention on the datapoint that matters: states set for potential changes next November.

Here the states of little interest are greyed out. The designers use colour to display the current status of the potential trifecta states. And so I am left curious why the designers did not choose to take a similar approach with the remaining graphics in the piece.

Overall, I should say the piece is strong. The graphics generally work very well. My quibbles are with the aesthetic stylings, which seem out of place for a straight news article. Something like this could work for an opinion piece or for a different subject matter. But for politics it just struck a loud dissonant chord when I first read the piece.

Credit for the piece goes to Kate Rabinowitz and Ashlyn Still.