Today we have an interesting little post, a choropleth map in a BBC article examining the changes occurring in the voting systems throughout the United States. Broadly speaking, we see two trends in the American political system when it comes to voting: make it easier because democracy; make it more restrictive because voter fraud/illegitimacy. The underlying issue, however, is that we have not seen any evidence of widespread or concerted efforts of voter fraud or problems with elections.
Think mail-in ballots are problematic? They’ve been used for decades without issues in many states. That doesn’t mean a new state could screw up the implementation of mail-in voting, but it’s a proven safe and valid system for elections.
Think that were issues of fraudulent voters? We had something like sixty cases brought before the courts and I believe in only one or two instances were the issues even remotely proven. The article cites some Associated Press (AP) reporting that identified only 500 cases of fraudulent votes. Out of over 14 million votes cast.
500 out of 14,000,000.
Anyway, the map in the article colours states by whether they have passed expansive or restrictive changes to voting. Naturally there are categories for no changes as well as when some expansive changes and some restrictive changes were both passed.
Normally I would expect to see a third colour for the overlap. Imagine we had red and blue, a blend of those colours like purple would often be a designer’s choice. Here, however, we have a hatched pattern with alternating stripes of orange and blue. You don’t see this done very often, and so I just wanted to highlight it.
I don’t know if this marks a new stylistic design direction by the BBC graphics department. Here I don’t necessarily love the pattern itself, the colours make it difficult to read the text—though the designers outlined said text, so points for that.
But I’ll be curious to see if I, well, see more of this in coming weeks and months.
Credit for the piece goes to the BBC graphics department.
When I was in the Berkshires, one thing I noticed was signs about bears. Bear crossing. Don’t feed the bears. Be beary careful. Okay, not so much the latter. But it was nonetheless odd to a city dweller like myself where I just need to be wary of giant rats.
Less than a month later, I read an article in the Boston Globe about how the black bear population in Massachusetts is expanding from the western and central portions of the state to those in the east.
The graphic in the article actually comes from the Massachusetts Division of Fisheries and Wildlife, so credit goes to them, but it shows the existing range and the black bears’ new range.
I understand the inclusion of the highways in red, green, and black, but I wish they had some even simple labelling. In the article they mention a few highways, but my familiarity with the highway system in Massachusetts is not great. Also, because the designer used thin black lines to demarcate the towns, one could think that the black lines, especially out west, represent counties or other larger political geography units.
Credit for the piece goes to the Massachusetts Division of Fisheries and Wildlife.
After twenty years out of power, the Taliban in Afghanistan are back in power as the Afghan government collapsed spectacularly this past weekend. In most provinces and districts, government forces surrendered without firing a shot. And if you’re going to beat an army in the field, you generally need to, you know, fight if you expect to beat them.
I held off on posting anything about the Taliban takeover of Afghanistan simply because it happened so quick. It was not even two months ago when they began their offensive. But whenever I started to prepare a post, things would be drastically different by the next morning.
And so this timeline graphic from the BBC does a good job of capturing the rapid collapse of the Afghan state. It starts in early July with a mixture of blue, orange, and red—we’ll come back to the colours a bit later. Blue represents the Afghan government, red the Taliban, and orange contested areas.
The start of the summer offensive
The graphic includes some controls at the bottom, a play/pause and forward/backward skip buttons. The geographic units are districts, sub-provincial level units that I would imagine are roughly analogous to US counties, but that’s supposition on my part. Additionally the map includes little markers for some of the country’s key cities. Finally in the lower right we have a little scorecard of sorts, showing how many of the nearly 400 districts were in the control of which group.
Skip forward five weeks and the situation could not be more different.
So much for 20 years.
Almost all of Afghanistan is under the control of the Taliban. There’s not a whole lot else to say about that fact. The army largely surrendered without firing a shot. Though some special forces and commando units held out under siege, notably in Kandahar where a commando unit held the airport until after the government fell only to be evacuated to the still-US-held Hamid Karzai International Airport in Kabul.
My personal thoughts, well you can blame Biden and the US for a rushed US exodus that looks bad optically, but the American withdrawal plan, initiated by Trump let’s not forget, counted on the Afghan army actually fighting the Taliban and the government negotiating some kind of settlement with the Taliban. Neither happened. And so the end came far quicker than anyone thought possible.
But we’re here to talk graphics.
In general I like this. I prefer this district-level map to some of the similar province-level maps I have seen, because this gives a more granular view of the situation on the ground. Ideally I would have included a thicker line weight to denote the provinces, but again if it’s one or the other I’d opt for district-level data.
That said, I’d probably have used white lines instead of black. If you look in the east, especially south and east of Kabul, the geographically small areas begin to clump up into a mass of shapes made dark by the black outlines. That black is, of course, darker than the reds, blues, and yellows. If the designers had opted for white or even a light shade of grey, we would enhance the user’s ability to see the district-level data by dropping the borders to the back of the visual hierarchy.
Finally with colours, I’m not sure I understand the rationale behind the red, blue, yellow here. Let’s compare the BBC’s colour choice to that of the Economist. (Initially I was going to focus on the Economist’s graphics, but last minute change of plans.)
Another day, more losses for the government
Here we see a similar scheme: red for the Taliban, blue for the government. But notably the designers coloured the contested areas grey, not yellow. We also have more desaturated colours here, not the bright and vibrant reds, blues, and yellows of the BBC maps above.
First the grey vs. yellow. It depends on what the designers wanted to show. The grey moves the contested districts into the background, focusing the reader’s attention on the (dwindling) number of districts under government control. If the goal is to show where the fighting is occurring, i.e. the contest, the yellow works well as it draws the reader’s attention. But if the goal is to show which parts of the country the Taliban control and which parts the government, the grey works better. It’s a subtle difference, I know, but that’s why it would be important to know the designer’s goal.
I’ll also add that the Economist map here shows the provincial capitals and uses a darker, more saturated red dot to indicate if they’d fallen to the Taliban. Contrast that with the BBC’s simple black dots. We had a subtler story than “Taliban overruns country” in Afghanistan where the Taliban largely did hold the rural, lower populated districts outside the major cities, but that the cities like the aforementioned Kandahar, Herat, Mazar-i-Sharif held out a little bit longer, usually behind commando units or local militia. Personally I would have added a darker, more saturated blue dot for cities like Kabul, which at the time of the Economist’s map, was not under threat.
Then we have the saturation element of the red and blue.
Should the reds be brighter, vibrant and attention grabbing or ought they be lighter and restrained, more muted? It’s actually a fairly complex answer and the answer is ultimately “it depends”. I know that’s the cheap way out, but let me explain in the context of these maps.
Choropleth maps like this, i.e. maps where a geographical unit is coloured based on some kind of data point, in this instance political/military control, are, broadly speaking, comprised of large shapes or blocks of colour. In other words, they are not dot plots or line charts where we have small or thin instances of colour.
Now, I’m certain that in the past you’ve seen a wall or a painting or an advert for something where the artist or designer used a large, vast area of a bright colour, so bright that it hurt your eyes to look at the area. I mean imagine if the walls in your room were painted that bright yellow colour of warning signs or taxis.
That same concept also applies to maps, data visualisation, and design. We use bright colours to draw attention, but ideally do so sparingly. Larger areas or fields of colours often warrant more muted colours, leaving any bright uses to highlight particular areas of attention or concern.
Imagine that the designers wanted to highlight a particular district in the maps above. The Economist’s map is better designed to handle that need, a district could have its red turned to 11, so to speak, to visually separate it from the other red districts. But with the BBC map, that option is largely off the table because the colours are already at 11.
Why do we have bright colours? Well over the years I’ve heard a number of reasons. Clients ask for graphics to be “exciting”, “flashy”, “make it sizzle” because colours like the Economist’s are “boring”, “not sexy”.
The point of good data visualisation, however, is not to make things sexy, exciting, or flashy. Rather the goal is clear communication. And a more restrained palette leaves more options for further clarification. The architect Mies van der Rohe famously said “less is more”. Just as there are different styles of architecture we have different styles of design. And personally my style is of the more restrained variety. Using less leaves room for more.
Note how the Economist’s map is able to layer labels and annotations atop the map. The more muted and desaturated reds, blues, and greys also allow for text and other artwork to layer atop the map but, crucially, still be legible. Imagine trying to read the same sorts of labels on the BBC map. It’s difficult to do, and you know that it is because the BBC designers needed to move the city labels off the map itself in order to make them legible.
Both sets of maps are strong in their own right. But the ultimate loser here is going to be the Afghan people. Though it is pretty clear that this was the ultimate result. There just wasn’t enough support in the broader country to prop up a Western style liberal democracy. Or else somebody would have fought for it.
Credit for the BBC piece goes to the BBC graphics department.
Credit for the Economist piece goes to the Economist graphics department.
Today I want to highlight a print article from the New York Times I received about two weeks ago. It’s been sitting in a pile of print pieces I want to sit down, photograph, and then write up. But as we begin to return to normal, I need my second dining room chair back because at some point I’ll have guests over.
The article in question examined the rates of Covid-19 vaccination across the United States. And on the front page, above the fold no less, we can compare the vaccination rates for Covid-19 to those of the 2019–2020 flu and if you unfold it to its full-length glory we can add in the 2009–2010 H1N1 swine flu outbreak.
Front page graphics
First thing I want to address is the obvious. Look at those colours. Who loves a green-to-red scale on a choropleth? Not this guy. They are a pretty bad choice because of green-to-red colour blindness. (There’s two different types as well as other types of colour blindness, but I’m simplifying here.) But here’s what happen when I pull the photo into Photoshop and test for it. (This is a screenshot, because I’m not aware of a means of exporting a proof image.)
Reds and greens become yellows and greys.
You can still see the difference between the reds and greens. That’s good. And it’s because colour is complicated. In red-green colour blindness, the issue is sensitivity to picking up reds and greens. (Again, oversimplifying for the sake of a blog post.) Between those two colours in the spectrum we have yellow. To the other side of green we have blue.
So if a designer needs to use a red-green colour scheme—and any designer who has worked in data visualisation will have undoubtedly have had a client asking for the map/chart/whatever to be in red and green—there’s a trick to making it work.
I don’t know if this is true, but growing up, I learned that green was the one colour the human eye evolved to distinguish the most. Now for a print piece like this, you are working in what we call CMYK space (cyan, magenta, yellow, and black). Red is a mixture of magenta and yellow. Green a mixture of cyan and yellow. If you remember your school days, it’s similar to—but not the same as—mixing your primary colours. So if you need to make red and green work, what can you do? First, you can subtract a bit of yellow from your green, because that exists between red and green. But then, and this is why CMYK is different from your primary school primary colours, we can adjust the amount of magenta. Magenta is not a “pure” red, instead it’s kind of purplish and that means has some blue in it. Adding a little bit of magenta, while it does add “red” into the green, it’s also adding more blue to the blue present in the cyan. Now you can spend quite a bit of time tweaking these colours, but very quickly I can get these two options.
Reds and greens.
Great, you can still see them as both red and green. Your client is probably happy and probably accepts this greenish-blue as green, because we have that ability to distinguish so many types of green. But what about those with red-green colour blindness? Again, I can’t quite do a straight export, so the best is a screenshot, but we can compare those two options like so.
I can see the differences significantly more clearly here.
You can probably still tweak the green, but by going for that simple tweak, you can make the client happy—even though it’s still just better to avoid the red and green altogether—and still make the graphic work.
There’s a bit more to say about the rest of the article, which has some additional graphics inside. But that’ll have to wait for another day. As will clearing down the pile of print pieces to share, because that keeps on growing.
Credit for the piece goes to Lazaro Gamio and Amy Schoenfield Walker.
I’ve been trying to limit the amount of Covid-19 visualisations I’ve been covering. But on Sunday this image landed at my front door, above the fold on page 1 of the New York Times. And it dovetails nicely with our story about the pandemic’s impact on Pennsylvania, New Jersey, Delaware, Virginia, and Illinois.
Some not so great looking numbers across the globe.
For most of 2020, the United States was one of the worst hit countries as the pandemic raged out of control. Since January 2021, however, the United States has slowly been coming to grips with the virus and the pandemic. Its rate is now solidly middle of the pack—no longer is America first.
And if you compare the chart at the bottom to those that I’ve been producing, you can clearly see how our five states have really gotten this most recent wave under control to the point of declining rates of new cases.
However, you’ve probably heard the horror stories from India and Brazil where things are not so great. It’s countries like those that account for the continual increase in new cases at a global level.
Credit for the piece goes to Lazaro Gamio, Bill Marsh, and Alexandria Symonds.
Yesterday I wrote my usual weekly piece about the progress of the Covid-19 pandemic in the five states I cover. At the end I discussed the progress of vaccinations and how Pennsylvania, Virginia, and Illinois all sit around 25% fully vaccinated. Of course, I leave my write-up at that. But not everyone does.
This past weekend, the New York Times published an article looking at the correlation between Biden–Trump support and rates of vaccination. Perhaps I should not be surprised this kind of piece exists, let alone the premise.
From a design standpoint, the piece makes use of a number of different formats: bars, lines, choropleth maps, and scatter plots. I want to talk about the latter in this piece. The article begins with two side by side scatter plots, this being the first.
Hesitancy rates compared to the election results
The header ends in an ellipsis, but that makes sense because the next graphic, which I’ll get to shortly, continues the sentence. But let’s look at the rest of the plot.
Starting with the x-axis, we have a fairly simple plot here: votes for the candidates. But note that there is no scale. The header provides the necessary definition of being a share of the vote, but the lack of minimum and maximum makes an accurate assessment a bit tricky. We can’t even be certain that the scales are consistent. If you recall our choropleth maps from the other day, the scale of the orange was inconsistent with the scale of the blue-greys. Though, given this is produced by the Times, I would give them the benefit of the doubt.
Furthermore, we have five different colours. I presume that the darkest blues and reds represent the greatest share. But without a scale let alone a legend, it’s difficult to say for certain. The grey is presumably in the mixed/nearly even bin, again similar to what I described in the first post about choropleths from my recent string.
Finally, if we look at the y-axis, we see a few interesting decisions. The first? The placement of the axis labels. Typically we would see the labelling on the outside of the plot, but here, it’s all aligned on the inside of the plot. Intriguingly, the designers took care for the placement—or have their paragraph/character styles well set—as the text interrupts the axis and grid lines, i.e. the text does not interfere with the grey lines.
The second? Wyoming. I don’t always think that every single chart needs to have all the outliers within the bounds of the plot. I’ve definitely taken the same approach and so I won’t criticise it, but I wonder what the chart would have looked like if the maximum had been 35% and the grid lines were set at intervals of 5%. The tradeoff is likely increased difficulty in labelling the dots. And that too is a decision I’ve made.
Third, the lack of a zero. I feel fairly comfortable assuming the bottom of the y-axis is zero. But I would have gone ahead and labelled it all the same, especially because of how the minimum value for the axis is handled in the next graphic.
Speaking of, moving on to the second graphic we can see the ellipsis completes the sentence.
Vaccination rates compared to the election results
We otherwise run into similar issues. Again, there is a lack of labelling on the x-axis. This makes it difficult to assess whether we are looking at the same scale. I am fairly certain we are, because when I overlap the graphics I can see that the two extremes, Wyoming and Vermont, look to exist on the same places on the axis.
We also still see the same issues for the y-axis. This time the axis represents vaccination rates. I wish this graphic made a little clearer the distinction between partial and full vaccination rates. Partial is good, but full vaccination is what really matters. And while this chart shows Pennsylvania, for example, at over 40% vaccinated, that’s misleading. Full vaccination is 15 points lower, at about 25%. And that’s the number that needs to be up in the 75% range for herd immunity.
But back to the labelling, here the minimum value, 20%, is labelled. I can’t really understand the rationale for labelling the one chart but not the other. It’s clearly not a spacing issue.
I have some concerns about the numbers chosen for the minimum and maximum values of the y-axis. However, towards the middle of the article, this basic construct is used to build a small multiples matrix looking at all 50 states and their rates of vaccination. More on that in a moment.
My last point about this graphic is on the super picky side. Look at the letter g in “of residents given”. It gets clipped. You can still largely read it as a g, but I noticed it. Not sure why it’s happening, though.
So that small multiples graphic I mentioned, well, see below.
All 50 states compared
Note how these use an expanded version of the larger chart. The y-minimum appears to be 0%, but again, it would be very helpful if that were labelled.
Also for the x-axis in all the charts, I’m not sure every one needs the Biden–Trump label. After all, not every chart has the 0–60% range labelled, but the beginning of each row makes that clear.
In the super picky, I wish that final row were aligned with the four above it. I find it super distracting, but that’s probably just me.
Overall, this is a strong piece that makes good use of a number of the standard data visualisation forms. But I wish the graphics were a bit tighter to make the graphics just a little clearer.
Credit for the piece goes to Danielle Ivory, Lauren Leatherby and Robert Gebeloff.
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.
Unequal growth
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.
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.
More choropleth colours…
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.
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.
Red vs. blue
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.
Yesterday we looked at an article from the Inquirer about the 2020 election and how Biden won because of increased margins in the suburbs. Specifically we looked at an interactive scatter plot.
Today I want to talk a bit about another interactive graphic from the same article. This one is a map, but instead of the usual choropleth—a form the article uses in a few other graphics—here we’re looking at three-dimensional pyramids.
All the pyramids, built by aliens?
Yesterday we talked about the explorative vs. narrative concept. Here we can see something a bit more narrative in the annotations included in the graphic. These, however, are only a partial win, though. They call out the greatest shifts, which are indeed mentioned in the text. But then in another paragraph the author writes about Bensalem and its rightward swing. But there’s no callout of Bensalem on the map.
But the biggest things here, pun intended, are those pyramids. Unlike the choropleth maps used elsewhere in the article, the first thing this map fails to communicate is scale. We know the colour means a county’s net shift was either Democratic or Republican. But what about the magnitude? A big pyramid likely means a big shift, but is that big shift hundreds of votes? Thousands of votes? How many thousands? There’s no way to tell.
Secondly, when we are looking at rural parts of Bucks, Chester, and Montgomery Counties, the pyramids are fine. They remain small and contained within their municipality boundaries. Intuitively this makes sense. Broadly speaking, population decreases the further you move from the urban core. (Unless there’s a secondary city, e.g. Minneapolis has St. Paul.) But nearer the city, we have more population, and we have geographically smaller municipalities. Compare Colwyn, Delaware County to Springfield, Bucks County. Tiny vs. huge.
In choropleth maps we face this problem all the time. Look at a classic election map at the county level from 2016.
Wayb ack when…
You can see that there is a lot more red on that map. But Hillary Clinton won the popular vote by more then 3,000,000 votes. (No, I won’t rehash the Electoral College here and now.) More people are crowded into smaller counties than there are in those big, expansive red counties with far, far fewer people.
And that pattern holds true in the Philadelphia region. But instead of using the colour fill of an area as above, this map from the Inquirer uses pyramids. But we face the same problem, we see lots of pyramids in a small space. And the problem with the pyramids is that they overlap each other.
At a glance, you cannot see one pyramid beind another. At least in the choropleth, we see a tiny field of colour, but that colour is not hidden behind another.
Additionally, the way this is constructed, what happens if in a municipality there was a small net shift? The pyramid’s height will be minimal. But to determine the direction of the shift we need to see the colour, and if the area under the line creating the pyramid is small, we may be unable to see the colour. Again, compare that to a choropleth where there would at least be a difference between, say, a light blue and light red. (Though you could also bin the small differences into a single neutral bin collecting all small shifts be them one way or the other.)
I really think that a more straight forward choropleth would more clearly show the net shifts here. And even then, we would still need a legend.
The article overall, though, is quite strong and a great read on the electoral dynamics of the Philadelphia region a month ago.
