maps – Page 10 – Coffee Spoons

Parties in Pennsylvania

This is from a social media post I made a few days ago, but think it may be of some relevance/interest to my Coffeespoons followers. I was curious to see at 30+ days from the general election, how has the landscape changed for the two parties since 2016?

Well, this project has driven me to a related, but slightly different project that has been consuming my non-work time. Hopefully I will have more on that in the coming days. Without further ado, the post:

Pennsylvania will likely be one of the more critical battleground swing states in this year’s election. In 2016, then candidate Trump won the state by less than one percentage point. But four years is a long time and I was curious to see how things have changed.

In the first chart on the right we see counties won by Trump and on the left, Clinton. The further from the centre, the greater the candidate’s margin of victory over the other. The top half plots registered Republicans’ margin over Democrats as a percentage of all registered voters in the county (including independents and third party) and the bottom half does the same for Democrats. Closer to the centre, the more competitive, further away, less so.

Trump’s key to victory was the white, working class voter clustered in the west and the northeast of the state–old mining and steel towns. There Democrats normally counted on organised labour support as registered Democrats. That all but collapsed in 2016. The bottom right shows a number of nominally Democratic counties Trump won, whereas Clinton only picked up one Republican county, Chester.

But what are PA’s battlegrounds?

In the second chart we ignore places like Philly and Fulton County and zoom in on more competitive counties within 20 point margins. Polls presently point to a Biden lead of about 5 points in PA. If every dot moved left by 5 points (it doesn’t really work like that), we only see Erie and Northampton with potential to flip.

But Trump’s realignment of politics is accelerating (more on this another day) a realignment of PA’s political geography.

In the fourth chart, neither Erie nor Northampton show any real movement via party registration back to Democrats. Erie may flip, but Northampton’s likely a stretch. Places like Cumberland and Lancaster counties are too solidly Republican to flip this year. Instead Trump is more likely to flip counties like Monroe and Lehigh red, even if he loses the state.

Because, not shown, the key to a Biden victory will be running up the margins in Philly & Pittsburgh, and to a lesser extent Philly’s four collar counties, including Chester, which appears to be rapidly shifting in Democrats’ favour.

Credit for the piece is mine.

The Size of the California Wildfires Compared to Philly

The West Coast is a different scale than the East Coast. After all, California alone is almost the size of New England and parts of the Mid-Atlantic combined. So when we take that enormous size into consideration, how big are these fires on an East Coast scale? It can be difficult to imagine.

Thankfully the Philadelphia Inquirer addressed the issue.

It’s a simple concept, but I love these kind of graphics. The East Coast is dense and cities and towns are clustered closer together, being they were founded before personal automobiles were things. And so the August Complex fire in California would cover a significant portion of the Philadelphia metropolitan area, almost wiping it all off the map.

Credit for the piece goes to John Duchneskie.

It’ll Get Cooler Eventually

President Trump, on climate change.

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

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

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

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

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

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

Credit for the piece goes to Laris Karklis.

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.

A Map of Unequal Comparisons

I’ve largely been busy creating and posting content on the Covid pandemic and its impact on the Pennsylvania, New Jersey, and Delaware tristate area along with, by request, both Virginia, and Illinois, my former home. It leaves me very little time for blogging, and I really do not want this site to become a blog of my personal work. That’s why I have a portfolio or my data project sites, after all.

But in posting my Covid datagraphics, I’ve come across variations of this map with all sorts of meme-y, witty captions saying why Canada is doing so much better than the US, why Americans shouldn’t be allowed to travel to Canada, and now why the Blue Jays shouldn’t be allowed to host Major League Baseball games.

Well, that map isn’t necessarily wrong, but it’s incredibly misleading.

First, the map comes from the fantastic Johns Hopkins work on Covid-19. (Full disclosure, that’s the data source I use at work to create my work work datagraphics: https://philadelphiafed.org/covid-19/covid-19-research/covid-19-cases-and-deaths#.) And their site has a larger and more comprehensive dashboard (still hate that term but it does have sticking power) of which the map is the focal point.

You can see the map there in the centre and some tables to the left, some tables to the right, and even a micro table beneath thundering away at the map’s position. I could get into the overall design—maybe I will one of these days—but again, let’s look at that map.

The crux of the argument is that there are a lot of red dots in the United States and very few in Canada. But look at the table in the dashboard on the left. At the very bottom you see three small tabs, Admin 0, Admin 1, and Admin 2. Admin 0 contains all entities at the sovereign state level, e.g. US, Canada, Sweden, Brazil, &c. Admin 1 is the provincial/state level, e.g. Pennsylvania, Illinois, Ontario, Quebec, &c. Admin 2 is the sub-provincial/sub-state level, e.g. Philadelphia County, Cook County, Chester County, Lake County, &c.

Notice anything about my examples? Not all countries have provinces/states, but Canada certainly does. And then at Admin 2, the examples and indeed the data only have US counties and US data. Everything in Canada has been aggregated up to Admin 1. And that is the problem.

The second part to point out is the dot-ness of the map. And to be fair, this is part of a broader problem I have been seeing in data visualisation the last few months. Dots, circles, or markers imply specificity in location. The centre of that object, after all, has to fall on a specific geographic place, a latitude and longitude coordinate. It utterly fails to capture the dimensions and physical size of the geographic unit, which can be critical.

Because not all geographic units are of the same size. We all know Rhode Island as one of the smallest US states. Let’s compare that to Nunavut or Yukon in Canada, massive provinces that spread across the Canadian Arctic. Rhode Island, according to Google, 1212 square kilometres. Nunavut? 808,200.

So now show both states/provinces on a map with one dot and Rhode Island’s will practically cover the state. And it will also be surrounded by and in close proximity to the states or Massachusetts and Connecticut. Nunavut, on the other hand will be a small dot in a massive empty space on a map. But those dots are equal.

Now, combine that with the fact that the Hopkins map is showing data on the US county level. Every single county in the United States gets a red dot. By default, that means the US is covered with red dots. But there is no county-level equivalent data for Canada. Or for Mexico (also seen in the above graphic). And so given we’re only using dots to relate the data, we see wide swaths of empty space, untouched by red dots. And that’s just not true.

Yes, large parts of the Canadian Arctic are devoid of people, but not southern Ontario and Quebec, not the southwestern coast of British Columbia, not the Maritimes.

The Hopkins map should be showing geographic units at the same admin level. By that I mean that when on Admin 0, the map should reflect geographic units of sovereign state level, allowing us to compare the US to Canada directly. But, and for this argument I’m assuming we’re keeping the dots despite their flaws, we only see Admin 0 level data.

Admin 1 shows only provincial level data. Some countries will begin to disappear, because Hopkins does not have the data at that level. But in North America, we still can compare Pennsylvania and Illinois to Ontario and Quebec.

But then at Admin 2, we only see the numerous dots of the United States counties. It’s neither an accurate nor a helpful comparison to contrast Chester County or Will County to the entire province of Ontario and so the map should not allow it. Instead, as the above graphic shows, it creates misconceptions of the true state of the pandemic in the US and Canada.

Credit for the Hopkins dashboard goes to, well, Hopkins.

Wednesday’s Covid-19 Data

Here we have the data from Wednesday for Covid-19.

Pennsylvania saw continued spread of the virus. Notably, Monroe County in eastern Pennsylvania passed 1000 cases. It was one of the state’s earliest hotspots. That appears to have been because it was advertised as a corona respite for people from New York, not too far to the east and by then in the grips of their own outbreak.

New Jersey grimly passed 5000 deaths Wednesday. And it is on track to pass 100,000 total cases likely Friday or Saturday. Almost 2/3 of these cases are located in North Jersey, with some South Jersey counties still reporting just a few hundred cases and a handful of deaths.

Delaware passed 3000 cases and Kent Co. passed 500. While those don’t read like large numbers, keep in mind the relatively small population of the state.

Virginia has restarted reporting deaths, this time at the county level and not the health district level. What we see is deaths being reported all over the eastern third of the state from DC through Richmond down to Virginia Beach. In the interior counties we are beginning to see the first deaths appear. And in western counties, we still see that the virus has yet to reach some locations, but counties are beginning to report their first cases.

Illinois continues to suffer greatly in the Chicago area, and at levels that dwarf the remainder of the state. However, the downstate counties are beginning to see spikes of their own. Macon and Jefferson Counties each saw increases of 30–40 cases in just 24 hours.

Preview(opens in a new tab)

A longer-term look at the states shows how the states diverge in their outbreaks. Pennsylvania looks like it might be forcing the curve downward whereas New Jersey appears to have more plateaued. Earlier I expressed concern about Virginia, which does now appear to have not peaked and continues to see an increasing rate of spread. Then we have Illinois, which may have plateaued, but we need to see if yesterday’s record amount of new cases was a blip or an inflection point. And in Delaware a missing day of records makes it tricker to see what exactly the trend is.

Credit for the piece is mine.

Comparing Covid-19 to Influenza

I want to share a small graphic I made yesterday evening. And I am being charitable with the term graphic. Really it is nothing more than a collection of organised factettes. But I have seen the footage of those protesting the lockdowns in various states, including Pennsylvania.

To be clear, people can have different policy prescriptions to solve the pandemic. For example, the governor of Pennsylvania is considering lifting the lockdown piecemeal once the state overall has sufficient testing and tracing capabilities. Look at the state.

He rightly said that Cameron County, one of the little light purple shapes in the upper left, with its one case for the last 25 days is in a different situation than Philadelphia where cases continue to grow, albeit at a slowing rate. And in the future it is possible that Cameron County could open before Philadelphia. That is a different policy prescription than, say, opening the state all at once.

I don’t think most people enjoy lockdown—I haven’t left my building in 38 days and I cannot wait to leave and go do something. But I recognise that spreading outside these walls we have a deadly pandemic for which we have no vaccine. But then I see people protesting—protesting in a manner that contradicts the guidelines put out by the health officials—and claiming that we should open up because this is nothing worse than the flu.

Well, Covid-19 is not the flu. It is much worse.

This isn't your grandmother's flu. Or anyone else's flu. Because this isn't the flu. — This isn’t your grandmother’s flu. Or anyone else’s flu. Because this isn’t the flu.

Now, those numbers will change because the pandemic is ongoing. But, let’s spitball. Let’s assume those numbers hold. The idea of the shutdowns, lockdowns, and quarantines is to prevent the spread of the virus. For the sake of this thought experiment, let’s just assume, however, that it infects 56 million people, the upper end of the range for this most recent influenza season.

Influenza this year killed as many as 62,000 people after infecting 56 million. Hypothetically, with a mortality rate of 5%, Covid-19 would kill 2,800,000 people.

With a 4% rate that drops to 2,240,000

With a 3% rate that drops to 1,680,000

With a 2% rate that drops to 1,120,000

With a a 1% rate that drops to 560,000

With a 0.5% rate that drops to 280,000

And even at 0.5% that is still far greater than the flu. And so that is why it is so important to keep the number of people infected as low as possible. (And I won’t even get into the surge problems overwhelming hospitals that acts as a force multiplier and is the proximate reason for the lockdowns.)

This is not the flu.

Credit for the piece is mine.

Covid-19 Data from Monday

Monday’s Covid-19 data for Pennsylvania, New Jersey, Delaware, Virginia, and Illinois provided a glimmer of good news, most notably in Pennsylvania. That, however, occurred on the same day as a protest in Harrisburg that could set the state back days if not weeks. More on that below.

Pennsylvania saw fewer than 1000 new cases for the first time since 1 April. The curve here may be doing more than flattening, but it might actually be falling. That is to say the infection rate is decreasing rather than stabilising and holding steady, as it appears to be doing in New Jersey. That said, new cases are appearing sporadically in the rural and less dense areas of the state. Problematically, protestors arrived in Harrisburg to let it be known they are unhappy with the quarantine. Because the rest of us are.

The problem is that it appears a significant percentage of those infected with the virus are asymptomatic carrier, i.e. they are sick, but do not show any symptoms like fever, coughing, difficulty breathing. Critically, they may not appear sick, but they can spread the sickness. And so a gathering of several hundred people in close quarters? Not ideal.

Compare that to a Christian cultish church in Daegu, South Korea. There, an infected parishioner did not heed government calls to isolate and instead attended a church service. The average infected person spreads this virus to two or three people. This congregant? They infected 43 people who then went on to infect other people.

It is quite possible that someone in that Harrisburg protest was an asymptomatic carrier. And given the lack of social distancing, the lack of masks, and the general reckless behaviour, it is quite possible that the rally could be a super-spreading event. But we won’t know for 5–10 days, the apparent incubation period of the virus. Hopefully we dodge the proverbial bullet. But it is quite easy to see how these kinds of protests could lead to surges in infections. And those surges would then force the government to extend its quarantine by weeks thereby defeating the entire point of the protestors.

We get it. Quarantine sucks. But we all have to suck it up.

Moving on to New Jersey, where we see continuing evidence of the plateauing of cases. The bulk of the cases remain in the north in the New York suburban counties with the fewest numbers in the counties in South Jersey. However, averages of nearly 3500 new cases daily remains quite high and the death toll of 4377 is likely to continue to climb higher, even if Monday’s 175 new deaths was lower than most days in recent weeks.

Delaware is back to reporting its figures. And in that release, we had Sussex County in the south climb above 1000 total cases. The levels or curves chart at the end will also show how the state might be flattening and stabilising its infection rate, but we will need several days of uninterrupted reporting to make that determination.

Virginia might be worrying. Or it might not be. Cases continue to increase in the big metropolitan counties like Fairfax and Henrico. But, there are still several counties out in the west that remain unaffected. And the curves chart at the end shows how there has not yet been any sort of even a near-exponential growth curve. Instead we just see a steady, slow increase in the number of cases. That in its own way makes it more difficult to see when the curve flattens, because it was already a relatively flat curve.

Illinois continues to be the tale of two states: Chicago vs. everywhere else. The combined Chicago and Cook County have over 20,000 total cases and the surrounding counties add a few thousand more, which gets you over 2/3 of the state’s 31,000 cases. That said, new cases and new fatalities are beginning to pop up in downstate counties.

Lastly a look at the curves. As I noted above when talking about Pennsylvania, you can clearly see the downward slope of the state’s new cases curve. Compare that to the plateau-like shape of New Jersey. Delaware and Illinois might be approaching a New Jersey-like curves. But I would want to see more data and in Delaware less volatility. But like I said, Virginia is a tricky one to read.

Credit for the pieces is mine.

Sunday’s Covid-19 Data

Here is a look at the data from Sunday’s releases on the COVID-19 outbreak in Pennsylvania, New Jersey, Virginia, and Illinois. I’ve omitted Delaware because they paused reporting on Sunday to move to a noontime release instead of their previous end-of-day.

I’m not exactly certain what that means for the data on Delaware and reporting time series. But, my guess is that will be more like a hole in the time series. I need to spend some time looking at that. But, anyways, on to the states for which we did have data on Sunday.

Pennsylvania continues to see growth in cases, but as we’ll get to with the levels, that appears to at least be stabilising. But in the spread of the outbreak, we are beginning to see the T of the state, that more rural and less densely populated area, beginning to fill in with cases. These are of course the areas of concern, the areas with shuttered rural hospitals, lack of comparatively developed infrastructure, where the impacts could be proportionately more severe than in the bigger cities. In terms of deaths, they have now spread almost across the state from east to west. I am still waiting until two adjacent counties connect before I make that final pronouncement.

For New Jersey, I have removed the orange outlines around each county. The initial idea was to show where deaths had occurred. But now that they have been reported in every county, they don’t seem to be as helpful as the small number I provide in the graphic. Regardless, 4200 deaths is a lot. But the approximately 200 new deaths is the lowest number reported in several days.

Virginia is a weird state. When we see the levels chart below, you will see how its uptick has been far more gradual, and to this point it does not yet appear to have peaked or begun to stabilise. Most of the reported cases continue to be in and around the state’s big cities, notably the DC metro area, but also Richmond.

Illinois has now seen cases from north at the Wisconsin border all the way south to Cairo. Most cases remain, however, concentrated in the Chicago metropolitan area, with lesser scale outbreaks occurring in the Quad Cities area and the suburban counties of Illinois this side of the Mississippi. Deaths continue to rise, and while most area again in the Chicago area, they are appearing increasingly at low levels in downstate counties.

But what about those curves? Excepting Delaware, which hasn’t reported new data, we can see that some states like Virginia continue to see increasing rates of infection. Others like New Jersey and Pennsylvania clearly have flattened and have entered a new phase. In New Jersey’s case it appears to be more of a stabilised plateau. In Pennsylvania, there was some evidence it was entering a declining rate phase, but that may now have begun to become more of a steady rate of infection like in New Jersey. Illinois is tough to read because of the variability of its data. It might be more of a pause in the rate of increase, or it may have begun to stabilise. We need more data.

Credit for the pieces is mine.

Sunday Covid-19 Data

Another day, more cases of coronavirus and Covid-19. So let’s take a look at Sunday’s data as there were some interesting things going on.

First, let’s dispense with Virginia. The state is enhancing its reporting structure, and so they admit the data is likely an underestimate of the present situation in Virginia. So here’s Virginia, nothing really changed.

Moving on, we have Pennsylvania. Here we are beginning to truly see the disparity between the cities in the southeast and southwest, namely Philadelphia and Pittsburgh, and the T that describes what sometimes is used to describe Pennsyltucky. (Though it also includes cities like Harrisburg, the state capital.) The point is that the T of Pennsylvania has yet to suffer greatly from the outbreak. Of course, it’s also the part of the state least equipped to deal with a pandemic.

New Jersey is just bad. One can make the argument that South Jersey is hanging on. (Though I will touch on that later with an idea for today’s afterwork work.) Bergen County in the northeast is likely to surpass 10,000 cases on its own today. And that will put it above most states.

Delaware is tough because it sits as a small state next to several much larger ones. But, the numbers seem to indicate the outbreak is still worsening. Though in terms of geographic spread, there’s little to say other than that New Castle County, home to Wilmington, in the north is the heart of the state’s outbreak.

Illinois is a fascinating state, because of how dissimilar it is compared to Pennsylvania, a state which has a similar number of people.

The map shows that geographic spread still has a little way to go before reaching every county in the state. But the outbreak has been there longer than in Pennsylvania. And most of the darker purples are concentrated in the northeast, in Chicago and its collar counties. Compare that to Pennsylvania above where you will see dark purple scattered across the cities of its eastern third, e.g. Allentown and Scranton, and in the western parts near Pittsburgh. This too could be worth exploring in depth in the future.

Lastly I want to get to the cases curves charts. Here we look at the daily new cases in each state.

The curves, flattening or otherwise, of the five states.

And unfortunately Sunday’s numbers will impact the Virginia curve, but it overall looks as if the state is worsening. I would argue that Illinois, which appears to be bending towards a steadying condition is likely in a weird weekly pattern where it appears to stabilise on weekends and then resumes reported infections come Monday. Pennsylvania might well be flattening its curve. I would want to see a few more days’ worth of data before stating that more definitively. Let’s give it to Wednesday or Thursday.

And then in New Jersey we have a fascinating trend. The curve of increasing number of cases has clearly broken. But it also is not shrinking. Instead, it seems to be more of a plateau. And in that case, the outbreak in New Jersey is not getting worse, but it’s also not getting any better. At least not numerically. However, the goal of flattening the curve is to create a slower, more steady increase in case numbers to help hospitals cope with surge volumes. So good news?

Credit for the pieces is mine.