Biden’s English Ancestry

We all know Joe Biden as the Irish American president. And that’s no malarkey. But, go back far enough in your family tree and you may find some interesting ancestry and ethnic origins and that’s no different with Joe Biden. Keep in mind that our number of ancestors doubles every generation. You have four grandparents, and many of us met most of them. But you had eight great-grandparents. How many of those did you know? And you had 16 great-great-grandparents, you likely didn’t know any of them personally. It becomes pretty easy for an ethnic line to sneak into your ancestry.

And in Biden’s case it may well be English. Although sneaking in is probably a stretch, as this BBC article points out, because his patrilineal line, i.e. his father’s father’s father’s, &c., is likely English. Of course back in the day the Irish and the English mixing would have been unconscionable, at least as my grandmother would have described it. And so it’s easy to see how the exact origins of family lines are quietly forgotten. But that’s why we have genealogists.

The article eschews the traditional family tree graphic and instead uses only two charts. The first is a simple timeline of Biden’s direct ancestors.

Biden’s patrilineal timeline

No, it’s no family tree, but timelines are a critical tool used by genealogists because at its core, genealogy is all about time and place. And a timeline has got one of those two facets covered.

Timelines help visualise stories in chronological order. I cannot tell you the number of family trees I have seen where people who create trees casually simply copy and paste data without scrutiny. Children born well after the deaths of parents are common. Or children born to parents in their 50s or 60s—perhaps not strictly impossible, but certainly highly irregular. And so to see Biden’s ancestors plotted out chronologically is a common graphic for those who do any work in genealogy, which my regular readers know is my hobby.

That alone would make the article worth sharing. Because, I enjoyed that graphic. I probably would have created a separate line for the birthplace of each individual, but I quibble.

However, we have another graphic that’s not so great. And once again with the BBC I’m talking about axis lines.

American ethnic origins

Here we have a chart looking at US ancestry as claimed in the US censuses of 1980 and 2000. But we do not have any vertical lines making it easy for readers to accurately compare the lengths of the various bars. Twice lately I’ve posted about axis lines and the BBC. Third time’s the charm?

We can also look at using these not as bars, but as line charts as I did in this re-imagining to the right.

First, we no longer need two distinct colours, though you could argue the English line should be a highlight or call out colour given its role in the article. Instead each line receives a label at the right and only the English line crosses any other, but given their point-to-point slope, it’s not confusing like a line chart with all years between 1980 and 2000 could be.

Secondly, the slope here of the line reinforces the idea of falling population numbers. The bar chart also shows this, but through a leftward movement in bars. The bar option certainly works and there’s nothing wrong with it, but these lines offer a more intuitive concept of falling numbers.

I also added some clarification to the data definition. These lines represent the number of people who reported at least one ethnic ancestry—at the time US census respondents could enter upwards of two. For myself, as an example, I could have entered Irish and Carpatho-Rusyn. But my own small sliver of English ancestry would have been left off the list.

Ultimately, the declining numbers of responses along with some reporting on self-identification points to the disappearing concepts of “Irish American” or “English American” as many increasingly see themselves as simply White Americans. But that’s a story for another day.

In the meantime, we have Joe Biden, the Irish American president, with a small bit of English ancestry. Those interested in the genealogy, the article also includes some nice photos of baptismal records and marriage records. It’s an interesting read, though I’m hungry for more as it’s a very light duty pass.

Credit for the BBC pieces goes to the BBC graphics department.

Credit for my reimagination is mine.

The Times Wore It Better

Two weeks ago I posted about the death toll in the latest conflict between Israel and Hamas. As it happened, later that morning when I opened the door, there was this graphic sitting above the fold on the front page of the New York Times.

They added a map.

The piece sits prominently on the front page, but tones down the colour and detail on the map to let the graphical elements, the coloured boxes, shine and take their prominent position.

Here’s a detail photo I took in case the above is too small.

Maps make everything cooler.

Ultimately, the piece isn’t too complex and isn’t more than what I made. However, the map adds some important geographical context, showing just where the deaths were occurring.

The piece also highlights the deaths in the West Bank and those in Israel from civil unrest. That was data I didn’t have at the time.

redit for the piece goes to the New York Times graphics department..

It’s Warming Up

As many of my readers know, I prefer my weather cooler and summer is probably my least favourite season—weather wise at least. Appropriately, my vaccination will be kicking in just in time for a small, early season heatwave. Felt like an appropriate time to share this piece from Brian Brettschenider.

It’s just an animated map showing where in the United States and Canada the daily average high temperature is 70ºF for each day of the year. Here’s where you can expect a daily high of 70ºF for the date of 20 May. Not Philadelphia.

I’m sure going to miss those reds.

Make sure to click through to watch the video on the Twitter.

Credit for the piece goes to Brian Brettschneider.

Delco vs Chesco

One of the things in the pop culture these days is an HBO show called Mare of Easttown. For those that haven’t heard of it, probably my more international audience, it’s a crime drama set in the near suburbs of Philadelphia, a placed called Delaware County that locals simply call Delco.

Last Saturday, the show got its limelight on Saturday Night Live, which spoofed the show in a trailer for a fictional show called Murdur Durdur, from the producers of Mare of Easttown as well as those of It’s Always Sunny in Philadelphia.

The SNL skit included a crime map of which I took a screenshot.

I can see my house from here. Dur.

This caught my attention because one of the characters mentioned Downingtown, which is where your author grew up until he was 16. SNL‘s map really just served as a vehicle to showcase Googling all the town names—and the Philadelphia region has a wealth of them—because the map is all over the place, pun intended.

Conshohocken is actually a real place in neighbouring Montgomery County, on the Schuylkill River (real place). Royersford is also real and also in Montgomery County. Hockessin is also real, but is in Delaware, the state, not Delaware County, which is in Pennsylvania. (Both border the Delaware River.)

The map also makes reference to Lionville, a real place near Downingtown. Your humble author worked in a restaurant in Lionville, located in Uwchlan Township. (They don’t mention that, but I can see people enjoying that name as well.)

The keen observers will also note the placement of a label for Altor, which is only about 2.5 miles from my aforementioned childhood home. Clearly some SNL writer is from or is incredibly familiar with the western suburbs of Philadelphia.

As for the map itself? Well, it’s fictional. One, there is no Jagoff Bridge. Two, it’s actually a map of Bethlehem, to the north in the Lehigh Valley. Route 30 is a real place and does run through Downingtown and Chester County. But nowhere does it cross any town or city like the one the map depicts. Instead that road is Route 378 crossing the Lehigh River. (Fun fact, Route 30 runs west and eventually through Indiana and Illinois, south of Chicago.)

In fact the funny thing is, the map spoofing the show set in Delaware County does not contain a single place in the real Delaware County. Easttown is, for fans of the show, not actually located in Delaware County. Instead, it’s in Chester County. And your author, not surprisingly perhaps, has connections there because it’s where you can find Devon and Berwyn. (My Chicago readers may recognise those names, as several streets were named for Main Line towns.) And where I attended middle- and high-school is across the street from Easttown Township. The real one.

Now I want to actually watch the show. The real one. Not the SNL one. But first I’ll need to grab a Yuengling and a Wawa hoagie.

Credit for the piece goes to probably the writers and props department of Saturday Night Live.

Choropleths…Again

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.

Choropleths and Colours

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.

Credit for the piece goes to John Duchneskie.

But What About New Zealand?

It’s time for another Friday just for fun posting. I once worked with a guy who could draw a map of the United States or the world on a whiteboard incredibly accurately. He then left it in the break room for the office to try and label correctly.

This is kind of that, but in reverse, from xkcd. Good luck.

Which states are missing?

Credit for the piece goes to Randall Munroe.

Warmer, Wetter Winters in the UK

I remember hearing and reading stories as a child about the Thames in London freezing over and hosting winter festivals. Of course most of that happened during what we call the Little Ice Age, a period of below average temperatures during the 15th through the early 19th century.

But those days are over.

The UK’s Meteorological Office, or the Met for short, released some analysis of the impacts of climate change to winter temperatures in the United Kingdom. And if, like me, you’re more partial to winter than summer, the news is…not great.

Winter warming

Broadly speaking, winters will become warmer and wetter, i.e. less snowy and more rainy. Meanwhile summers will become hotter and drier. Farewell, frost festivals.

But let’s talk about the graphic. Broadly, it works. We see two maps with a unidirectional stepped gradient of six bins. And most importantly those bins are consistent between the maps, allowing for the user to compare regions for the same temperatures: like for like.

But there are a couple of things I would probably do a bit differently. Let’s start with colour. And for once we’re not dealing with the colour of the BBC weather map. Instead, we have shades of blue for the data, but all sitting atop an even lighter blue that represents the waters around the UK and Ireland. I don’t think that blue is really necessary. A white background would allow for the warmest shade of blue, +4ºC, to be even lighter. That would allow greater contrast throughout the spectrum.

Secondly, note the use of think black lines to delineate the sub-national regions of the UK whilst the border of the Republic of Ireland is done in a light grey. What if that were reversed? If the political border between the UK and Ireland were black and the sub-national region borders were light grey—or white—we would see a greater contrast with less visual disruption. The use of lines lighter in intensity would allow the eye to better focus on the colours of the map.

Then we reach an interesting discussion about how to display the data. If the purpose of the map is to show “coldness”, this map does it just fine. For my American audience unfamiliar with Celsius, 4ºC is about 39ºF, many of you would definitely say that’s cold. (I wouldn’t, because like many of my readers, I spent eight winters in Chicago.)

The article touches upon the loss of snowy winters. And by and large, winters require temperatures below the freezing point, 0ºC. So what if the map used a bidirectional, divergent stepped gradient? Say temperatures above freezing were represented in shades of a different colour like red whilst below freezing remained in blue, what would happen? You could easily see which regions of the UK would have their lowest temperatures fail to fall below freezing.

Or another way of considering looking at the data is through the lens of absolute vs. change. This graphic compares the lowest annual temperature. But what if we instead had only one map? What if it coloured the UK by the change in temperature? Then you could see which regions are being the most (or least) impacted.

If the data were isolated to specific and discrete geographic units, you could take it a step further and then compare temperature change to the baseline temperatures and create a simple scatterplot for the various regions. You could create a plot showing cold areas getting warmer, and those remaining stable.

That said, this is still a really nice piece. Just a couple little tweaks could really improve it.

Credit for the piece goes to the UK Met Office.

Biden’s Biggest Pyramids

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.

Credit for the piece goes to John Duchneskie.

Covid Migration

Yep, Covid-19 remains a thing. About a month or so ago, an article in City Lab (now owned by Bloomburg), looked at the data to see if there was any truth in the notion that people are fleeing urban areas. Spoiler: they’re not, except in a few places. The entire article is well worth a read, as it looks at what is actually happening in migration and why some cities like New York and San Francisco are outliers.

But I want to look at some of the graphics going on inside the article, because those are what struck me more than the content itself. Let’s start with this map titled “Change in Moves”, which examines “the percentage drop in moves between March 11 and June 30 compared to last year”.

Conventionally, what would we expect from this kind of choropleth map. We have a sequential stepped gradient headed in one direction, from dark to light. Presumably we are looking at one metric, change in movement, in one direction, the drop or negative.

But look at that legend. Note the presence of the positive 4—there is an entire positive range within this stepped gradient. Conventionally we would expect to see some kind of red equals drop, blue equals gain split at the zero point. Others might create a grey bin to cover a negative one to positive one slight-to-no change set of states. Here, though, we don’t have that. Nor do we even get a natural split, instead the dark bin goes to a slightly less dark bin at positive four, so everything less than four through -16 is in the darker bin.

Look at the language, too, because that’s where it becomes potentially more confusing. If the choropleth largely focuses on the “percentage drop” and has negative numbers, a negative of a negative would be…a positive. A -25% drop in Texas could easily be mistaken with its use of double negatives. Compare Texas to Nebraska, which had a 2% drop. Does that mean Nebraska actually declined by 2%, or does it mean it rose by 2%?

A clean up in the data definition to, say, “Percentage change in moves from…” could clear up a lot of this ambiguity. Changing the colour scheme from a single gradient to a divergent one, with a split around zero (perhaps with a bin for little-to-no change), would make it clearer which states were in the positive and which were in the negative.

The article continues with another peculiar choice in its bar charts when it explores the data on specific cities.

Here we see the destinations of people moving out of San Francisco, using, as a note explains, requests for quotes as a proxy for the numbers of actual moves. What interests me here is the minimalist take on the bar charts. Note the absence of an axis, which leaves the bars almost groundless for comparison, except that the designer attached data labels to the ends of the bars.

Normally data labels are redundant. The point of a visualisation is to visualise the comparison of data sets. If hyper precise differences to the decimal point are required, tables often are a better choice. But here, there are no axis labels to inform the user as to what the length of a bar means.

It’s a peculiar design decision. If we think of labelling as data ink, is this a more efficient use with data labels than just axis labels? I would venture to say no. You would probably have five axis labels (0–4) and then a line to connect them. That’s probably less ink/pixels than the data labels here. I prefer axis lines to help guide the user from labels up (in this case) through the bars. Maybe the axis lines make for more data ink than the labels? It’s hard to say.

Regardless, this is a peculiar decision. Though, I should note it’s eminently more defensible than the choropleth map, which needs a rethink in both design and language.

Credit for the piece goes to Marie Patino.