I am a graphic designer who focuses on information design. My day job? Well, they asked me not to say. But to be clear, this blog is my something I do on my own time and does not represent the views of…my employers. I think what I can say is that given my interest in information design—be it in the shape of clear charts, maps, diagrams, or wayfinding systems—I am fortunate that my day job focuses on data visualisation. Outside of work, I try to stay busy with personal design work. Away from the world of design, I have become an amateur genealogist and family historian. You will sometimes see that area of work bleed into my posts.
If all goes according to plan, your author today will receive his first dose of the Covid-19 vaccine, the Pfizer variety for the curious. As such, it feels appropriate to share this recent piece from xkcd.
All joking aside, it should be said that, and as this graphic illustrates, just because you receive your first dose, doesn’t mean you should be out socialising and seeing people later that night.
You are not fully vaccinated until two weeks after your second dose, or the first if you received Johnson & Johnson. And so while I may be receiving my first dose this afternoon, it is going to be close to a month and a half before I’m able to leave my household unit and socialise with others. Probably three weeks for my second dose and then another two weeks for the vaccine to fully take effect.
Doesn’t mean I won’t be counting the days, though.
Admittedly, I was trying to find a data set for a piece, but couldn’t find one. So instead for today’s post I’ll turn to something that’s been sitting in my bookmarks for a little while now. It’s a choropleth map from the US Census Bureau looking at population change between the censuses.
The reason I have it bookmarked is for the apportionment map, but I will save apportionment for another post because, well, it’s complicated. But map colours are a thing we’ve been discussing of late and we can extend that conversation here.
What I find interesting about this map is how they used a very dark blue-grey colour for their positive growth and an orange that is a fair bit brighter for negative growth, or population loss. And because of that difference in brightness, the orange really jumps out at you.
To be fair, that’s ideal if you’re trying to talk about where state populations are shrinking, because it focuses attention on declines. But, if you’re trying to present a more neutral position, like this seems to be, that colour choice might not be ideal.
Another issue is that if you look at the legend it simply says loss for that orange. But, look above and you’ll see four bins clearly delimited by ranges of percents for the positive growth. If we are trying to present a more neutral story, the use of the orange places it visually somewhere near the top of that blue-grey spectrum.
If you look at the percentages, however, Michigan’s population decline was 0.6% and Puerto Rico’s 2.2%. If this map used a legend that treated positive and negative growth equally, you would place that one state and one should-be state in a presumably light orange. The scale of their negative growth is equal to something like Ohio, which is in the lightest blue-grey available.
Consequently, this map is a little bit misleading when it comes to negative growth.
Credit for the piece goes to the Census Bureau graphics team.
The alliteration failed at that last word, but it gets the point across. No mater how you may want to define infrastructure, the term always includes transit. In the Boston Globe, an opinion piece proposed how the city and region of Boston could improve upon the city’s mass transit options.
And they made a map.
The map is an interesting one. It uses thick purple lines to indicate the commuter rail branches—not the metro/subway lines. The problem is that the outside of those lines then encodes the suggested improvements. An orange outline indicates where tracks should be electrified—Boston still uses diesel engines for some of its commuter rail transit. But the problem is that the dark purple dominates the graphic. If, however, the purple were entirely replaced by an orange line, it would be clearer that the Providence needs electrification. (It’s actually already electrified, as that’s the same line Amtrak uses, but Boston’s transit service still uses diesel engines on the line.)
Similarly, the key to indicate upgraded tracks and signals is a blue line of similar “colour” to the purple. That makes it hard to distinguish between the two, especially when next to the green inline option, representing increased speeds.
The key flaw? A long-time wish for Boston transit lovers (or haters). Note how the system is divided into two, the two main hubs, South Station and North Station, do not connect. Connecting the two will require billions of dollars. But the benefits can be tremendous.
Philadelphia, for example, for decades had two rail hubs: Broad Street Station across from City Hall and Reading Terminal several blocks east along Market Street. Reading Terminal was the terminus for the Reading Railroad and Broad Street Station for the Pennsy, or Pennsylvania Railroad. In 1930, Broad Street Station was replaced by an underground station, today’s Suburban Station. But it would not be until 1984 when rail tunnels would finally be opened linking the western/southern Pennsylvania Railroad lines to the northern lines of Reading. But today you can take a train from a southwest suburb to the far northern suburbs without changing trains because of that connection.
Last Thursday I wrote about the use of colour in a choropleth map from the Philadelphia Inquirer. Then on Sunday morning, I opened the door to collect the paper and saw a choropleth above the fold for the New York Times. I’ll admit my post was a bit lengthy—I’ve never been one described as short of words—but the key point was how in the Inquirer piece the designer opted to use a blue-to-red palette for what appeared to be a data set whose numbers ran in one direction. The bins described the number of weeks a house remained on the market, in other words, it could only go up as there are no negative weeks.
Compare that to this graphic from the Times.
Here we are not looking at the Philadelphia housing market, but rather the spread of the UK/Kent variant of SARS-CoV-2, the virus that causes COVID-19. (In the states we call it the UK variant, but obviously in the UK they don’t call it the UK variant, they call it the Kent variant from the county in the UK where it first emerged.)
Specifically, the map looks at the share (percent) of the variant, technically named B.1.1.7, in the tests reported for each country. The Inquirer map had six bins, this Times map has five. The Inquirer, as I noted above, went from less than one week to over five weeks. This map divides 100% into five 20-percent bins.
Unlike the Inquirer map, however, this one keeps to one “colour”. Last week I explained why you’ll see one colour mean yellow to red like we see here.
This map makes better use of colour. It intuitively depicts increasing…virus share, if that’s a phrase, by a deepening red. The equivalent from last week’s map would have, say, 0–40% in different shades of blue. That doesn’t make any sense by default. You could create some kind of benchmark—though off the top of my head none come to mind—where you might want to split the legend into two directions, but in this default setting, one colour headed in one direction makes significant sense.
Separately, the map makes a lot of sense here, because it shows a geographic spread of the variant, rippling outward from the UK. The first significant impacts registering in the countries across the Channel and the North Sea. But within four months, the variant can be found in significant percentages across the continent.
Credit for the piece goes to Josh Holder, Allison McCann, Benjamin Mueller, and Bill Marsh.
This time last week I wrote about how we should not be surprised at rising levels of coronavirus in the states of Pennsylvania, New Jersey, Delaware, Virginia, and Illinois. After all, our elected officials reopened economies despite data saying they should do otherwise. On top of that, people have been engaging in reckless behaviour and seemingly abandoning the very behaviours that had been leading to declining rates. With those two failures, our last hope is that vaccines will come quickly and be widely taken by the public.
A week hence.
Well, we are beginning to see some divergent patterns, especially with new cases.
Last week there was some evidence that New Jersey might be bucking the trend and headed downwards after weeks of rising new cases. And now that appears to be a more sustained trend as the line for the Garden State’s seven-day average clearly began headed the right direction this past week.
That’s the good news. The bad news is that we continue to see rising numbers of new cases in Pennsylvania, Delaware, and Illinois. Although if we want to try and find the positives in the bad, we can see that Delaware’s upward trend remains fairly shallow. Illinois, while steeper, is rising from a lower base as the Land of Lincoln managed to reach low, summer levels of new case spread earlier this year. And in Pennsylvania, there is a bend in the curve, an inflection point, that could indicate growth in the number of new cases is slowing. We still need to see it turn negative, but slowing growth is better than increasing growth.
Virginia splits the difference between those sets. It remains at an elevated level of new case transmission, but the upward tick we saw—unlike the other states—was not followed by a general surge in new cases. The little rise we did see, in fact seems to have perhaps shifted back downward.
One of the big questions in this current wave of new cases is will deaths rise? We are seeing increasing numbers of new cases and hospitalisations, but will deaths follow? The hope is that we have vaccinated enough of the most vulnerable populations to prevent them from suffering the most serious of results.
So far so good. While death rates remain slightly elevated over summer levels, we do not yet see any signs of rising numbers of deaths. The only possible exception is Virginia, where cases bottomed out after the state added delayed death certificates from the holidays, but have risen in recent days.
Finally we have vaccinations. Here is the best news at which we can look. We can now say that at least 20% of the populations of Pennsylvania, Virginia, and Illinois are fully vaccinated. To be clear, that is still a long way from herd immunity levels, but that’s 20 percentage points more than we had four months ago.
One big outstanding question is how much, if at all, can vaccinated people spread coronavirus? This is why we need to continue to wear masks and socially distance even those who have been vaccinated. But at some point—I don’t know when—these increasing levels of full vaccination should begin to flatten the new case curves. Could that be what’s flattening the curves in New Jersey, Virginia, and Pennsylvania? It’s too early to say, but one can hope.
In many cities through the United States, real estate represents a hot commodity. It’s not difficult to understand why, as have covered before, Americans are saving a bit more. Coupled with stay-at-home orders in a pandemic, spending that cash on a home down payment makes a lot of sense for a lot of people. But with little new construction, it’s a seller’s market.
The Philadelphia Inquirer covers that angle for the Philadelphia region and in the article, it includes a map looking at time to sell a house. And it’s that interactive map I want to look at briefly this morning.
Primarily I want to discuss the colours, as you can gather from this post’s title. We have six bins here, each indicating an amount of time in one-week intervals. So far so good. Now to the colours, we have red for homes that sell in one week or less and blue for homes that sell in five weeks or more.
Blue to red is a pretty standard choice. You will often see it in maps where you have positive growth to negative growth or something similar, I’ve used it myself on Coffeespoons a number of times, like in this map of population growth at the county level here in Pennsylvania.
In those scenarios, however, note how you have positive values and negative values. The change in colour (hue) encodes the change in numerical value, i.e. positive vs. negative. We then encode the values within that positive or negative range with lighter/darker blues and reds. Most often the darker the blue or red, the greater the value toward the end of the spectrum. For example, in Pennsylvania, the dark blue meant population growth greater than 8% and red meant population declines in excess of 8%.
As an aside you’ll note that there are no dark blue counties in that map and that’s by design. By keeping the legend symmetrical in terms of its minimum and maximum values, we can show how no counties experienced rapid population growth whilst several declined rapidly. If dark blue had meant greater than 4% growth, that angle of the story would have been absent from the map.
Back to our choropleth discussion, however. How does that fit with this map of selling times for homes in the Philadelphia region?
Note first that five weeks is a positive value. But so is one week or less. The use of the red-blue split here is not immediately intuitive. If this map were about the change or growth in how long homes sell, certainly you could see positive and negative rates and those would make sense in red and blue.
The second part to understand about a traditional red-blue choropleth is that at some point you have to switch from red to blue, a mid-point if you will. If you are talking positive/negative like in my Pennsylvania map, zero makes a whole lot of sense. Anything above zero, blue, anything below zero red.
Sometimes, you will see a third colour, maybe a grey or a purple, between that red and blue. That encodes a fuzzier split between positive and negative. Say you want to give a margin of 1%, i.e. any geographic area that has growth between +1% and -1%. That intrinsically means the bin is both positive and negative at the same time, so a neutral colour like grey or a blend of the two colours, a purple in the case of red and blue, makes a whole lot of sense.
Here we have nothing like that. Instead we jump from a light yellow two-to-three weeks to a light blue three-to-four weeks.
What about that yellow? In a spectrum of dark blue to light blue, you will see lighter blues than darker blues. But in a red spectrum, that light red becomes pinkish or salmonish depending on that exact type of red you use. (Conversation for another day.) Personal preferences will often push clients to asking a designer to “use less pink” in their maps. I can’t tell you the number of times I’ve heard that.
If that comes up, designers will often keep their blue side of the legend from the dark to light—no complaints there, or at least I’ve never heard any. But for the red side, they’ll switch to using hue or type of colour instead of dark to light red.
Not all colours are as dark as others. Blue and red can be pretty dark. Yellow, however, is a fairly light colour. Imagine if you converted the colours to greyscale, you’ll have very dark greys for blue and red, but yellow will be consistently far lighter than the other two.
The designer can use the light yellow as the light red. But to link the yellow to red, they need to move through the hues or colours between the two. There’s a whole conversation here about colour theory and pigment and light absorption vs. pixels and light emission, but let’s go back to your colours you learned in primary school (pigment and light absorption). Take your colour wheel and what sits between red and yellow? Orange.
And so if a client objects to a light pink, you’ll see a pseudo dark-to-light red spectrum that uses a dark red, a medium orange, and a light yellow. Just like we see here in this Inquirer map.
Back to the two-to-three week and three-to-four week switch, though. What’s the deal? This is my sticking point with the graphic. I am looking for the explanation of why the sudden break in colour here, but I don’t see any obvious one.
Why would you use this colour scheme where blue and red diverge around a non-zero value? Let’s say the average home in the region sells in three weeks, any of the zip codes in red are selling faster than average, hot markets, and those taking longer than average are in blue, cold markets. Maybe it’s the current average, however. What if it were the average last year? Or the national average? These all serve as benchmarks for the presented data and provide valuable context to understand the market.
Unfortunately it’s not clear what, if any, benchmarks the divergence point in this map reflects. And if there is no reason to change colours mid-legend, with only six bins, a designer could find a single colour, a blue or purple for example, and then provide five additional lighter/darker shades of that to indicate increasing/decreasing levels of speed at which homes sell.
Overall, I left this piece a wee bit confused. The general trend of regional differences in how quickly homes are selling? I get that. But because there’s a non-logical break between red and blue here—or at least one I fail to see in the graphic—this map would work almost as well if each bin were a separate colour entirely, using ROYGBIV as a base for example.
This morning I read a piece in Politico Playbook that broke down President Biden’s $2.25 trillion proposal for infrastructure spending. A thing generally regarded as the United States sorely needs. $2.25 trillion is a lot of money and it’s a fair question to ask whether all that money is really money for infrastructure.
Because, it turns out, it’s not.
That isn’t to say money spent on job retraining or home care services wouldn’t be money well spent. Rather, it’s just not infrastructure.
Last week the Guardian published an article about drinking water pollution across the United States. Overall, it was a nicely done piece and the graphics within segmented the longer text into discrete sections. Each unit looks similar:
The left focuses on a definition and provides contextual information. It includes small illustrations of the mechanisms by which the pollutant enters the water system. To the right is a chart showing the levels of the contamination detected in the 120 tests the Guardian (and its partner Consumer Reports) conducted.
In almost all of the charts, we see the maximum depicted on the y-axis. And the bars are coloured if that observation station exceeds the health and safety limits. (The limit is represented by the dotted line.)
But towards the end of the piece we get to lead, a particularly problematic pollutant. There is no safe level of lead contamination. But how the piece handles the lead chart leaves a bit to be desired.
The first thing is colour, but that’s okay. Everything is red, but again, there is no safe level of lead so everything is over the limit. But look at the y-axis. That little black line at the top indicates a discontinuity in the lines, in other words the values for those three observations are literally off the chart.
But does that work?
First, this kind of thing happens all the time. If you ever have to work with data on either China or India, you’ll often find those two nations, due to their sheer demographic size, skew datasets that involve people. But in these kind of situations, how do we handle off the charts data points?
There is a value to including those points. It can show how extreme of an outlier those observations truly are. In other words, it can help with data transparency, i.e. you’re not trying to hide data points that don’t fit the narrative with which you’re working.
In this piece, it’s never explicitly stated what the largest value in the data set is, but I interpret it as being 5.8. So what happens if we make a quick chart showing a value of 6 (because it’s easier than 5.8)? I added a blue bar to distinguish it from the the rest of the chart.
You can see that including the data point drastically changes how the chart looks. The number falls well outside the graphic, but it also shows just how dangerously high that one observation truly is.
But if you say, well yeah, but that falls outside the box allowed by the webpage, you’re correct. There are ways it could be handled to sit outside the “box”, but that would require some extra clever bits. And this isn’t a print layout where it’s much easier to play with placement. So what happens when we resize that graphic to fit within its container?
You can see that All the other bars become quite small. And this is probably why the designers chose to break the chart in the first place. But as we’ve established, in doing so they’ve minimised the danger of those few off-the-charts sites as well as left off context that shows how for the vast majority of sites, the situation is not nearly as dire—though, again, no lead is good lead.
What else could have been done? If maintaining the height of the less affected bars was paramount, the designers had a few other options they could have used. First, you could exclude those observations and perhaps put a line below the 118 text that says “for three sites, the data was off the charts and we’ve excluded them from the set below.”
I have used that approach in the past, but I use it with great reluctance. You are removing important outliers from the data set and the set is not complete without them. After all, if you are looking to use this data set to inform a policy choice such as, which communities should receive emergency funding to reduce lead levels, I’d want to start with the city in blue. Sure, I would like everyone to get money, but we’d have to prioritise resources.
I think the best compromise here would have actually been a small tweak to the original. Above the three bars that are broken (or perhaps to the right with some labelling), label the discontinuous data points to provide clearer context to the vast majority of the sites, which are below 0.5 ppb.
This preserves the ability to easily compare the lower level observations, but provides important context of where they sit within the overall data set by maintaining the upper limits of the worst offenders.
Credit for the piece goes to the Guardian’s graphics department.
Last week I wrote about how the inevitable rise in new Covid-19 cases was occurring in Pennsylvania, New Jersey, Delaware, Virginia, and Illinois. Now, one, in the last week, we saw no evidence of states preparing to reinforce their public health and safety restrictions. And two, whilst we have no data on people not following guidelines, anecdotally a large group of people threw a party in my building’s common amenities space so it does seem like people are feeling less inclined to wear masks, socially distance, and isolate to their own households.
Those two conditions, of course, do not help reduce the case count. Instead they add to it. So it should come as no surprise that Covid-19 continues to rapidly spread in our five states, though some are doing worse than others.
New Jersey and Pennsylvania arguably performed the worst. If we look at the peak to trough decline from early winter’s surge to late winter’s nadir, we can see that New Jersey has reached 40% of that peak. Pennsylvania enjoyed a better decline and so has a large gap, but is still nearing 20% its previous peak.
Illinois is also remarkable—again not in a good way—as its peak to trough fall was even greater than Pennsylvania’s, however it’s also now clearly rising. The Land of Lincoln, however, did manager to reach late summer levels of new cases—good. But those are now rising—bad. Delaware too is seeing a rise, albeit at a slower rate than its two tristate neighbours.
Only Virginia’s rise remains slight, barely discernible in the chart.
Deaths, while not exactly good news, aren’t exactly good news either. Last week I mentioned how they had stalled out and stopped declining. That is better than rising death rates, but the levels of deaths per day is still higher than we saw last summer. In other words, things could be significantly better even in pandemic terms.
Last week? Deaths continued to stubbornly persist at those elevated levels. We remain vigilant, looking for any indication that deaths will follow the rates of new cases and hospitalisations and begin to climb.
The hope, of course, is that we have vaccinated enough of the most at risk populations to prevent a surge in deaths. But, we just don’t know yet. The only good news is that vaccinations continue to progress.
Illinois has surpassed 18% of its population being fully vaccinated. Virginia is not far behind at 17.75%. Pennsylvania, because of the bifurcated nature of its data reporting, remains unclear. It sits at 17.8% fully vaccinated, but Philadelphia has not posted updated data since late Thursday. It’s likely that the Commonwealth has joined Illinois in surpassing 18%, but it’s not fully certain.
Also this past week, the CDC updated its guidance for the fully vaccinated, saying that it was safe for them to travel. I take some issue with this, primarily on the messaging front.
First, we need to be clear about what fully vaccinated means. It means two weeks after your final dose. For Johnson & Johnson’s vaccine, that means two weeks after your shot as you only receive one. For both Pfizer and Moderna, you are only fully vaccinated two weeks after your second shot—not before. And keep in mind with Pfizer you need to wait three weeks between first and second dose. With Moderna it’s four weeks. In other words, with J&J you need to wait two weeks after your first (and only) shot before you can begin to follow the loosened guidelines. If you receive Pfizer’s, you need to wait five weeks from your first shot, assuming you do receive your second three weeks later, and with Moderna it’s six weeks, again assuming the recommended four week gap.
The problem is that only about 20% of the US population is fully vaccinated. And with the virus spreading at high rates and at high levels, it poses a significant risk as the newer, more lethal, and more infectious variants could take root in the United States and overwhelm the healthcare systems of the 50 states. We do not yet know if fully vaccinated people can spread the virus if they do become infected.
I think the advice should have remained to refrain from all but essential travel until we reached a high percentage of fully vaccinated folks. I ballparked earlier this week something like 2/3 the estimated amount of full vaccinations required for herd immunity (est. at 75%). In other words, keeping restrictions on travel until at least 50% of the US becomes fully vaccinated.
We remain several weeks away from that milestone, unfortunately. I understand the desire/urge people have to get out and do things and enjoy spring after a year of isolation. Sadly, if winter was the darkest/hardest part of the pandemic, I think that makes spring and early summer the most challenging. Because we see progress, we see the light at the end of the tunnel, and it coincides with warmer weather and we want nothing more to get out and do things and see people. But that is the last thing we need to be doing at this point.
I’ve often described the vaccination as the marshmallow test. In a study, scientists presented kids with a marshmallow. They could eat the marshmallow immediately, but if they waited 15 minutes, unsupervised, they could then have an additional marshmallow. We are all just grabbing that first marshmallow whilst the promise of a more normal summer is ours if we can wait just 15 minutes.