As I said yesterday, since people are finding these updates helpful on the social media, I am going to repost the previous evening’s graphics I make on the Coronavirus Covid-19 outbreak here on Coffeespoons as well. So while today is Thursday, these are the numbers states provided yesterday, so it’s more of a Wednesday update.
But here I can start with the flatter curves graphic. The New Jersey numbers in particular look good—I mean they’re still bad. Of course we are just a few big breaches of quarantine and lapses in social distancing from reversing that progress.
Maybe some curve flattening?
State-wise, Pennsylvania continues to worsen. However, a close look at the slope of the line in the previous chart indicates that the steepness of the growth may be lessening. Deaths passed 300 and cases are now firmly entrenched on both sides of the state with the rural, less densely populated areas in the Ridge and Valley portion of the state seemingly hit not as hard.
The situation in Pennsylvania
Despite the potential flattening, New Jersey is just in a rough spot. The final bastions of low case numbers in South Jersey are slowly filling up as Cape May County passed the 100-case threshold.
The situation in New Jersey
Delaware continues to accelerate and is now past 1000 cases.
The situation in Delaware
Virginia continues to see cases spreading in the eastern, more populous portions of the state. And at 75 deaths, it’s nearing the 100-death threshold.
The situation in Virginia
Illinois is seeing deaths occur away from Chicago, in the St. Louis suburban counties and in and around Springfield and Champaign and Bloomington areas.
Here are the Tuesday figures for Pennsylvania, New Jersey, Delaware, Virginia, and Illinois. At the end is an updated version of the flattening curves chart as well. Given the value of these graphics that people have been texting, emailing, and DMing me on social media, I might consider making these a regular staple here on my blog as well. I would probably slowly write about other graphics covering the outbreak as well.
Any feedback is welcome on how to make the graphics more useful to you, the public.
Pennsylvania has finally reached the point where the virus has infected at least one person in every county. Now, if we shift our attention a wee bit to the deaths, we can see those are still largely confined to the eastern third of the state.
The condition in Pennsylvania
New Jersey continues to suffer greatly. But a sharp increase in new cases could be a blip, or it could mean the curve isn’t flattening. We need more data to see a longer trend. Regardless, over 3000 more people were reported infected and over 200 more died.
The condition in New Jersey
Delaware worsened significantly. As a small state, it has a lower captive population. But it is rapidly approaching 1000 cases. In fact, I would not be surprised if that is the headline from Wednesday.
The condition in Delaware
Virginia also saw a significant uptick in cases. And most counties and independent cities in eastern Virginia now report cases. But the rural, mountainous counties in the west and southwest are not uniformly infected. At least not yet.
The condition in Virginia
Illinois saw some geographic spread, but again, compared to a state like Pennsylvania, the worst in Illinois is disproportionately concentrated in the Chicago metropolitan area.
The condition in Illinois
Lastly, the curves are not flattening in all the states but maybe New Jersey. But as I noted above, the higher daily cases there might be a blip.
This past weekend I continued looking at the spread of COVID-19 across the United States. But in addition to my usual maps of Pennsylvania, New Jersey, Delaware, Virginia, and Illinois, I also looked at the number of cases across the United States adjusted for population. I then looked at the five aforementioned states in terms of new cases to see if the curve is flattening. Finally, I looked at the number of hospital beds per 1000 people vs the number of cases per 1000 people.
The latter in particular I wanted to be an examination of hospitalisation rates vs ICU beds, which are a small fraction of total hospital beds. But as I could not find that data, I made do with overall cases and overall beds.
So first let’s look at the cases across the U.S. What you can see is that whilst New York and New Jersey do have some of the worst of the impact, Washington is still not great and Louisiana and Michigan are also suffering.
The situation across the United States
And then when we look at the states by their cases per 1000 people and their hospital beds per 1000 people, we see that the states often claimed to be overwhelmed, New York, New Jersey, and Washington are all well over the blue line, which indicates an equal number of beds and cases per 1000 people, or near it. Because it is important to remember that not all beds are the type needed for COVID-19 victims, who often require the more fully kitted out ICU beds. Additionally, not all cases are severe enough to warrant hospitalisation.
Cases per 1k people vs hospital beds per 1k people
Then from the broader national view, we can look at the states of interest. Here, those of you who have been following my social media posts, you can see fewer dark purples in these maps. That’s because I have adopted a new palette that has sacrificed granularity at the lower end of the scale and added it at the top, a particular need in New Jersey and the Philadelphia and Chicago metro areas. And finally we look at the daily new cases to see if that curve is flattening.
Pennsylvania now has almost every county infected. But unlike Illinois, which has a similar infection rate but more unaffected counties, Pennsylvania has fewer cases in its big city, Philadelphia, and has more cases in the smaller cities and towns.
The situation in Pennsylvania
New Jersey is just a disaster. Deaths are now reported in every county—so I can probably remove those orange outlines. The only potential good news is that new cases for the second day in a row were fewer than the day before. It could be a blip. But it could also be a signal that the peak of infection has or is nearing. That said, hospitalisations and deaths are lagging indicators and could take two weeks to follow the positive test results. So in the best case scenario that this is a peak, New Jersey is far from out of the woods.
The situation in New Jersey
Delaware is the smallest state I look at—and one of the smallest in the union overall—but its cases are worryingly increasing rapidly, although like every state I examine in detail it had fewer new cases Sunday than Saturday.
The situation in Delaware
Virginia is in a better spot overall than the other four states. You can see that in the national map above. And most of Virginia’s cases are concentrated in the DC and Richmond areas as well as the cities along the peninsulas jutting into the Chesapeake.
The situation in Virginia
Illinois is, as noted above, similar to Pennsylvania in terms of infections. In terms of deaths, however, it is doubling Pennsylvania’s numbers. And most of its cases are located in and around Chicago. Big chunks of downstate Illinois are unaffected or lightly affected compared to the Commonwealth.
The situation in Illinois
Finally, as I noted in New Jersey, could these lower numbers Sunday than Saturday be meaningful? Possibly. But in all five states? Highly unlikely. Regardless, we can look at the number of daily new cases and see if that curve of infection is flattening. We should wait several days before beginning to make that assessment. But one can hope.
The case for flattening curves
All of this is to say that things are bad and likely will continue to get worse. But I will keep looking at the data daily and presenting it to the public to keep them informed.
Today is yet another Friday in the pandemic. And so I wanted to just upload a few of the graphics I have been making for family, friends, and coworkers and posting on the Instagram and the Facebook. I did this two weeks ago as well, and if you compare those maps to these, you will see quite a stark difference. But on to today’s maps.
As a brief reminder, I am specifically looking at Pennsylvania, New Jersey, and Delaware—the tri-state region for my non-Philly followers—as well as Virginia and Illinois by the request of friends and former colleagues who live in those states. And then at the end I’ve been putting the tri-state region together to provide a fuller regional context.
Lastly, for today only, the Bureau of Labour Statistics published its jobs report about the number of job losses in March across the US. And…it wasn’t pretty.
Conditions in PennsylvaniaConditions in New JerseyConditions in DelawareConditions in VirginiaConditions in IllinoisConditions in the tri-state region
Plus, the added bonus of the Bureau of Labour Statistics’ monthly jobs report. And spoiler, things aren’t so great out there.
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.)
Potential outcomes for Pennsylvania
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.
A table of potential outcomes
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.
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.
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.
Oh look at that Mississippi River trail
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.
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.
Pennsylvania is among the states…
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.
Just before Halloween, NBC News published an article by political analyst David Wasserman that examined what airports could portend about the 2020 American presidential election. For those interested in politics and the forthcoming election, the article is well worth the read.
The tldr; Democrats have been great at winning over cosmopolitan types in global metropolitan areas in the big blue states, e.g. New York and California. But the election will be won in the states where the metropolitan areas that sport regional airports dominate, i.e. Pennsylvania, Michigan, Wisconsin, and North Carolina. And in those districts, support for Democrats is waning.
The closing line of the piece sums it up nicely:
…to beat Trump, Democrats will need to ask themselves which candidates’ proposals will fly in Erie, Saginaw and Green Bay.
But what about the graphics?
We have a line chart that shows how support for Democrats has been increasing amongst those in the global and international airport metros.
Democrats aren’t performing well with the non-global and international types of metros
It uses four colours and I don’t necessarily love that. However, it smartly ties into an earlier graphic that did require each series to be visualised in a different colour. And so here the consistency wins out and carries on through the piece. (Though as a minor quibble I would have outlined the MSA being labelled instead of placing a dot atop the MSA.)
A lot of these global metros are in already blue states
The kicker, however is one of those maps with trend arrows. It shows the increasing Republican support by an arrow anchored over the metropolitan area.
Lot of Trump support in the battleground states
The problem here is many-fold. First, the map is actually quite small in the overall piece. Whereas the earlier maps sit centred, but outside the main text block, this fits neatly within the narrow column of text (on a laptop display at least). That means that these labels are all crowded and actually make it more difficult to realise which arrow is which city. For example, which line is Canton, Ohio? Additionally with the labels, because they are set in black text and a relatively bolder face, they standout more than the red lines they seek to label. Consequently, the users’ focus falls not on the lines, but actually on the labels—the reverse of what a good graphic should do.
Second, length vs. angle. If all lines moved away from their anchor at the same angle, we could simply measure length and compare the trending support that way. However, it is clear from Duluth and Green Bay that the angles are different in addition to their sizes. So how does one interpret both variables together?
Third, I wonder if the map would not have been made more useful with some outlines or shading. I may know what the forthcoming battleground states are. And I might know where they are on a map. But Americans are notorious for being, well, not great when it comes to geography. A simple black outline of the states could have been useful, though it in this design would have conflicted with the heavy black labelling of the arrows. Or maybe a purple shading could have been used to show those states.
Overall, the piece is well worth a read and the graphics generally help tell the narrative visually. But that final graphic could have used a revision or two.
Credit for the piece goes to Jiachuan Wu and Jeremia Kimelman.
The last two days we looked at densification in cities and how the physical size of cities grew in response to the development of transport technologies, most notably the automobile. Today we look at a New York Times article showing the growth of automobile emissions and the problem they pose for combating the greenhouse gas side of climate change.
The article is well worth a read. It shows just how problematic the auto-centric American culture is to the goal of combating climate change. The key paragraph for me occurs towards the end of the article:
Meaningfully lowering emissions from driving requires both technological and behavioral change, said Deb Niemeier, a professor of civil and environmental engineering at the University of Maryland. Fundamentally, you need to make vehicles pollute less, make people drive less, or both, she said.
Of course the key to that is probably in the range of both.
The star of the piece is the map showing the carbon dioxide emissions on the roads from passenger and freight traffic. Spoiler: not good.
From this I blame the Schuylkill, Rte 202, the Blue Route, I-95, and just all the highways
Each MSA is outlined in black and is selectable. The designers chose well by setting the state borders in a light grey to differentiate them from when the MSA crosses state lines, as the Philadelphia one does, encompassing parts of Pennsylvania, New Jersey, Delaware, and Maryland. A slight opacity appears when the user mouses over the MSA. Additionally a little box remains up once the MSA is selected to show the region’s key datapoints: the aggregate increase and the per capita increase. Again, for Philly, not good. But it could be worse. Phoenix, which surpassed Philadelphia proper in population, has seen its total emissions grow 291%, its per capita growth at 86%. My only gripe is that I wish I could see the entire US map in one view.
The piece also includes some nice charts showing how automobile emissions compare to other sources. Yet another spoiler: not good.
I’ve got it: wind-powered cars with solar panels on the bonnet.
Since 1990, automobile emissions have surpassed both industry emissions and more recently electrical generation emissions (think shuttered coal plants). Here what I would have really enjoyed is for the share of auto emissions to be treated like that share of total emissions. That is, the line chart does a great job showing how auto emissions have surpassed all other sources. But the stacked chart does not do as great a job. The user can sort of see how passenger vehicles have plateaued, but have yet to decline whereas lorries have increased in recent years. (I would suspect due to increased deliveries of online-ordered goods, but that is pure speculation.) But a line chart would show that a little bit more clearly.
Finally, we have a larger line chart that plots each city’s emissions. As with the map, the key thing here is the aggregate vs. per capita numbers. When one continues to scroll through, the lines all change.
Lots of people means lots of emissions.There’s driving in the Philadelphia area, but it’s not as bad as it could be.
Very quickly one can see how large cities like New York have large aggregate emissions because millions of people live there. But then at a per capita level, the less dense, more sprawl-y cities tend to shoot up the list as they are generally more car dependent.
Credit for the piece goes to Nadja Popovich and Denise Lu.
