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.
Yesterday I mentioned that there was some data to suggest that at least in New Jersey the curve was flattening. Monday we received additional data and so I wanted to share what that data showed.
I used a set of bar charts to show the new daily cases yesterday for Pennsylvania, New Jersey, Delaware, Virginia, and Illinois. But as I mentioned, a single day can be a blip. Noise. We want to find the pattern or the signal within that data set. Consequently I applied a simple 7-day rolling average to the new daily cases data set.
I chose seven days for two reasons. The first was that the onset of the symptoms is 5–10 days after infection. Picking a mid-point in that range assures us that people who are infected are beginning to appear in the data. Secondly, a cursory check of the data suggests that reported numbers dip lower on weekends. And so making a week-long average covers any possibility of lower values at week’s end.
That preface out of the way, what do we see? Well, there is some evidence that the curve is flattening in New Jersey. The lines below represent that rolling average. And if you look at the very top of the New Jersey curve, you can see it beginning to flatten.
Flattening curves
Unfortunately that does not mean New Jersey is out of the woods. Not by a long shot. Instead, that means tens of thousands of people will still be infected. And hundreds more will die. But, the rate at which those two things happen will be lower. Hopefully hospitals will not be as overwhelmed as they presently are. And that might make for a lower total death count.
The data does not support, however, the notion that the curve is flattening in the other states. Consider that the United States spans a continent and contains over 330 million people. The outbreak will look different in different states. Compare Pennsylvania and Illinois, which have similar case numbers. But in Pennsylvania we have more cases in smaller cities and rural areas and fewer in the largest cities. Plus, of course, we have the different measures taken by different states to contain and mitigate the pandemic within their borders.
But, we do have some data to suggest that at least in New Jersey the curve is flattening. I’ll take good news where I can find it. (Even if it comes from Jersey.)
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.
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.
19 March county wide spread of COVID-1919 March county wide spread of COVID-1919 March county wide spread of COVID-1919 March county wide spread of COVID-1919 March county wide spread of COVID-19
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.
County levels compared
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.
State levels since early March
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.
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 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.
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.
Better…
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.
Better still.
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.
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.
Much easier to read
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.
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.
