The Teamsters Union decided to officially endorse neither candidate in the 2024 US presidential election. Prior to their non-announcement announcement, however, the union surveyed its members and then released the polling data ahead of the announcement.
Of course, the teamsters represent but a single union in a large and diverse country. More importantly, the survey results reported only the share of responses for either candidate—and “Other”—so we have no idea how many of what number opted for whom. But hey, it’s another talking point in the final six weeks of the campaign.
Naturally, I decided to visualise the data.
The trend is pretty, pretty clear. The union’s rank-and-file clearly support Trump for president, with the exception of the teamsters in the District of Columbia. (Note, no survey was taken in Wyoming.) In fact, in only eight states plus DC did Harris’ support top 40%.
I thought three-dimensional charts died back in the 2010s. Alas, here we are in 2024 and I have to discuss one once again. have been following the Titan Inquiry this week and the opening presentation included this gem of data visualisation.
To be fair, I do not know how many designers, let alone specialist information designers, the US Coast Guard had or made available to create a clear and compelling chart and presentation, but…this is not it. First I will go through a number of points and then, when I had written about half of the post this morning, I decided it would simply be easier to put a white box over the main chart area and just recreate the graphic myself.
Unfortunately, after digging around, I could not find the actual dive depth data the Coast Guard used and so I essentially traced out the chart by hand. Not ideal, but for proof of concept as to how this chart could have been improved…I think my reinterpretation ssuffices.
To start, the chart sits on the slide with a drop shadow. Drop shadows are not all bad. They create perceived depth between an object and it’s background. The interwebs love them. I have used them. But I do not understand why here the chart needs a drop shadow to sit on the slide. Especially since the shadow pushes the chart “above” the deck, only for the three-dimensional bar chart to push the data “below” the chart’s surface, which means the chart data is being represented on the slide surface.
Deep breath.
The chart background features some kind of coloured gradient that became pixellated upon export and import into the PowerPoint deck.
The type was too small that it too became pixellated and grainy to the point that the dive labels are illegible. I would argue labelling each dive beyond its number is unnecessary in the context of Titan’s final dive, but without having listened to the presentation I cannot say for certain.
Next we have the third-dimension. It adds nothing and creates more coloured areas—because the dimension is fake, this a two-dimensional representation of three dimensions—that distract the eye from the important dimension, the length of the bar.
After that, we can look at the axis labels. First, there are far too many. Second, the maximum depth labelling makes no sense. Sometimes, if a line or a bar exceeds the chart maximum once or twice by a small amount, you can let it poke above the top—in this case bottom—line. If you know the rules, you know when you can break the rules. Here, however, the maximum label is 3800 metres.
But Titanic rests at 3840. Ergo 13 different measurements will need to sit below the chart’s maximum—minimum, technically—axis line.
Deep breath.
If she rests at 3840 metres, just add 60 to the chart minimum and you will ahve a final axis label of 3900 metres. Look carefully, however, and you will see in the bottom left how after the final white line, the chart keeps going. Clearly, the designers knew the chart needed more space. This unlabelled minimum is probably 3900 metres given the 100-metre increments used throughout.
But, however, if you add 160 metres to the chart you have a nice, round, divisible number of 4000, which means you do not need to mark the depths in 100-metre increments. It means all the bars sit within the chart. It means fewer pixels on the slide to distract the eyes. (Especially if you drop the background colour.)
Furthermore, if you look carefully at the green boxes, which represent successful dives to Titanic, you can see how the bars break the dimensional rules and are actually flat two-dimensional bars. Perhaps this was only noticeable to me as I worked off the downloaded file at a high-level of zoom to try and figure out the depths as precisely as possible. Or perhaps it is an artifact of the pixellated export of the graphic. If the latter, more of a reason not to make the thing a three-dimensional bar chart.
Then we can get to the colours.
Deep breath.
To start, red-green colour blindness is a thing. I harp on this often and so I will not rehash everything here. No, it does not mean all green and red combinations will not work, one just needs to be careful with them. This one comes pretty close to not working so I would have avoided it.
Secondly, just look at the red. I mean, how can you not. It is very bright and draws your eye almost immediately to all those red bars, particularly the one nearly a fifth of the way in from the right edge. That one is next to one of the successful Titanic dives. My first thought? Oh, that was the final dive. Wrong.
Red means non-Titanic dives. Again, I have not listened to the presentation, but these would presumably be dives of relatively less importance than the Titanic dives. I would not have made the less important dives the one colour that stands out the most.
If you want to go green represents successful Titanic dives and red represents unsuccessful Titanic dives, that makes sense. I can understand the design decision. (Though you would still need to ensure the shades work with each other.) In that case maybe the blue bars represent non-Titanic dives.
Instead, here blue represents unsuccessful dives to Titanic, which of course means the final dive, which of course includes the inquiry’s raison d’être. Not only that, the chart’s background is also blue, which makes visually separating the bars from the background more difficult. This is particularly true at the sides of the chart where the gradient leaves the darker blue.
Finally we have a little orange box with some tiny type pointing out the final dive’s depth. That bit, more visible than the green and orange bars, was still lost to me behind the red bars.
And breathe.
All in all, a mess.
As I noted at the top, halfway through I decided this was such a mess I would prefer just to show how the chart could have been designed. It took a little over an hour to make the chart. Clearly I do not have the chart style guidelines for the Coast Guard, so I just chose a typeface I think worked and then picked some reasonable colours from the deck.
Call me biased, but my design substantially improves the chart. First, you can read the text. Second, the colours fit the brand, do not distract from and in fact highlight the final dive. If I started from scratch, I would prefer to use what looks like the full content area of the PowerPoint slide, but I simply traced over the existing chart. I.e., ideally the chart would have been a little bit taller. I did have to cut out the labels for each dive, but as I stated earlier, they were illegible.
Credit for the original piece goes to the US Coast Guard.
For the last few weeks I have been working on my portfolio site as I update things. (Note to self, do not wait another 15 years before embarking upon such an update.)
At the University of the Arts (requiescat in pace), I took an information design class wherein I spent a semester learning about the electricity generation market in the Philadelphia region. This became a key part of my portfolio when I applied for 99 jobs at the beginning of the Great Recession, had 3 interviews, and only 1 job offer.
That job offer lead me to Chicago and Euromonitor International where one of the first projects I worked on was a datagraphic about throat share, i.e. what drinks products/brands people in different countries drank. Essentially, I took what I learned about visualising the share of electricity generation in Pennsylvania to the share of drinks consumption across the world. Thus a career was born. Fast forward 15 years and I wanted to see how that electricity generation had changed. And I can do that because I used a public source in the US Energy Information Administration.
Anecdotally, Pennsylvanians know fracking for natural gas has been a boon to the former coal and steel parts of the Commonwealth, which really is a lifeline. But overall, Pennsylvania has long been known as a nuclear power state. More on that from a personal standpoint in a later post. Back in the uphill both ways to university day, I did not look at the United States overall. But now I can.
Largely this fits with the narratives I know. Coal has plummeted both in the Commonwealth and more broadly as natural gas has largely taken its place. No, that’s not great from a climate change perspective, but natural gas is definitely better than coal.
Renewables, nationally speaking, are now about 20% or 1/5th our net electricity generation. But in Pennsylvania, whilst this Monday morning might be a bright and blue sky day great for solar power, the nights are getting longer and we get a lot of clouds. We do have some hydroelectric dams—it helps to be a partially mountainous state. And, yes, we do have the wind farms along the Allegheny Ridge, one of the windiest spots along the East Coast, but for context one of the two nuclear reactors near to which I grew up is equal to almost the entire wind power electricity generation in the entire Commonwealth.
But for all the supposed growth in renewables, we just are not seeing it in Pennsylvania, at least not at a scale to supplant fossil fuels. And unfortunately, it is not as if demand is falling. And that might be why we are seeing quiet talks about reactivating some of Pennsylvania’s shuttered nuclear reactors. If you could bump that nuclear share of electric throat back up to 40% or even 50%, you could cut down that natural gas usage significantly.
We’re about to go into the sportsball realm, readers. Baseball, specifically.
Tuesday night, Atlanta Braves batter Whit Merrifield was hit in the back of the head by a 95 mph fastball. Luckily, modern ballplayers wear helmets. But at that velocity, one does not have the most reaction time in the world a number of other batters have been hit in the face. And generally, that’s not good. Merrifield went off in post-game interviews about the lack of accountability on the pitchers’ side. From my perspective as an armchair ballplayer, back in my day, when I walked up hill through the snow both ways to get to my one-room schoolhouse, if you hit a batter, our pitcher was hitting one of yours.
I have noticed in ballgames, however, I see hit-by-pitch (HBP) more often—and I score most ballgames I attend, so I have records. But I also know a handful attended per year makes for a very small sample size. Nonetheless, I know I have talked to other baseball friends and brought up that I think pitchers throw with less command, i.e. throwing strikes, than they used to, because I see more HBP in the box scores. And when I go to minor league ballgames, which I do fairly often, HBP seems on the rise there, which means in future years those same pitchers will likely be in the majors.
So yesterday morning, I finally took a look at the data and, lo and behold, indeed, since my childhood, the numbers of HBPs has increased.
There is one noticeable sharp dip and that is the 2020 COVID-shortened season. Ignore that one. And then a smaller dip in the mid-90s represents the 114-game and 144-game seasons, compared to the standard 162 per year. Nonetheless, the increase is undeniable.
There is a general dip in the curve, which occurs in the late 200s and early 2010s, with its nadir in 2012. Without doing more research, that was probably the peak of pitchers, who could command—throw strikes—and control—put their strikes where they want in the strike zone—their pitches at the sacrifice of velocity.
2014 saw the rise of the dominant Royals bullpen, which changed the course of modern baseball. Stack your bullpen with a number of power arms who throw 100 mph and just challenge batters to hit the speedball. Problem is, not everyone who can throw 100 knows where that speedball is going. And that leads to more batters being hit.
Merrifield is correct in his assessment that until pitchers and teams face consequences for hitting batters, we are not likely to see a decrease in HBPs. Or at least not until velocity is de-emphasised for some other reason. What if there were a rule a pitcher who hits a batter from the shoulder up is immediately ejected? What if a long-term injury for a batter is tied to a long-term roster removal for the pitcher? If, say, the batter hit in the head is out for a month with a concussion, the same pitcher is on the restricted list for a month?
Have I worked through any of these ideas in depth? Nope. Just spitballing here on ye olde blog. But as my chart shows, it does not look like this potentially life-changing problem in the game is going away anytime soon.
Back in autumn 2023 I shared a map with which I keep track of where I’ve visited (and driven/ridden through). In the months since I’ve visited a few new places and decided to update the map.
Most importantly, last autumn I visited Keane, New Hampshire for a day and so crossed the state off the list—not that visiting all 50 states has been or is today a goal of mine. Additionally, I came upon a photograph of me as a young lad in Wilmington, North Carolina. Can I recall being there? No. But I definitely was. So I added that county to the map.
Finally, in terms of new counties visited, I travelled out to Erie, Pennsylvania this past spring to witness the solar eclipse. I had never been to the far opposite corner of the Commonwealth and so coloured that eponymous county purple.
Of course on the day of the eclipse, the sky opened up and rain fell throughout breakfast. Consequently I got into my car and drove west like any proper young man until I found blue skies overhead and Ohio underneath. The eclipse was fantastic and those long-term readers should know that I have a card waiting to go to press, but am waiting for the funding of employment before going into production.
Finally, on my return from Erie, I purchased tickets to enjoy some Red Sox minor league baseball in Reading, Pennsylvania. I opted to enjoy a scenic drive instead of taking the interstates with which I am very familiar. And with that I coloured a number of western Pennsylvania counties in light purple.
I initially made this datagraphic over the weekend, after watching the last few weeks of Boston Red Sox baseball wherein they continued to win a game, lose a game, resulting in an even .500 record.
When I started, the graphic I sketched looked very different as I had included timelines and highlighted key moments where key players went down for the year or the year-to-date. But after I added some context of the sport’s leading clubs’ games above or below .500, I realised most of those clubs were all those that my good friends and family followed.
Consequently I ditched my initial concept and opted to instead show how middling my Red Sox have been to the rest of them. And whilst this graphic may have a few more spaghetti lines than I’d typically prefer, it does show that squiggle of consistency in the middle that is the Red Sox 2024 season to date.
Of course, when I posted it, the Red Sox had just lost to the Yankees and I said I expected them to win one and lose one the rest of the weekend to stay at .500. So what happened? The Red Sox won both and are now two games over .500.
Baseball superstition thus requires I post more graphics about the .500 Red Sox to get them more games over .500.
Yesterday was Saint Patrick’s Day and those who have followed me at Coffeespoons—or more generally know me—are well aware that my background is predominantly Irish. Those same people probably also know of my keen interest in genealogy. And that’s what today’s post is all about.
Irish genealogy is difficult because of the lack of records and lack of record access. My struggle is often in connecting an ancestor to a specific place in Ireland, necessary for any work to identify baptism, marriage, or death records. Starting with my maternal lines, it’s easy to see how ancestors were from “Ireland”, but I’ve been able to place precious few into a specific geographic context.
Thomas Doyle is the only ancestor I can place into a specific parish, and he wasn’t the key person who allowed it. For those interested in genealogy, it’s always worthwhile to investigate siblings, cousins, aunts, uncles, and sometimes even friends and neighbours because they often can provide clues, as it did in the case of the Doyles.
Sometimes you also need to step outside and get lost in a cemetery. I took a drive one weekend before the pandemic to find the graves of John Hickey and his family. Until that point, I knew nothing about the origins of him or his wife. Luckily his gravestone went one step beyond Ireland and stated he was born in Queen’s County, now County Laois. But I’ve still found no evidence of where in Laois he was born and so tracking the rest of his family is difficult, perhaps impossible.
Furthermore, you can also see that I have little specific information about when these ancestors all arrived. None were present in the 1850 US Census, so we can reasonably work from a starting hypothesis that they arrived after 1850 and then when each had children documented born in the US—or the rarer occasion of a US marriage record—we can reasonably assume they arrived between 1850 and the child’s birth.
On my maternal side there is a lot of work to do, which belies all the effort put into just getting this far over the last decade plus. Contrast that to my paternal side.
Here I have more Irish ancestors to investigate and I’m fortunate that I have more of an American paper trail, which when stitched together allowed me to get snippets of counties of birth or marriage, which, with some helpfully uncommon names, allowed me to dial in on specific parishes and towns. In other cases, my Irish ancestors first settled in Canada or the United Kingdom, which have much better preserved records. And finally a few have had family histories written and documented elsewhere, which allowed me to check the paper trail and validate the work.
And obviously when dealing with people in the mid-19th century, we don’t have a lot of photography and I’m lucky to have found a website—no longer extant, rest in peace Geocities—that had photos of my ancestors and a cousin over in Ireland who had a few photos sent my ancestors to their relations—though we’re not sure how they’re related, another story for another day—that I can put two faces to 18 names of direct Irish immigrant ancestors.
And of course the thing of note for all these people is that grey bar in the middle of the timeline: the Great Famine. In a roughly seven year period, over one million Irish died in Ireland and another over one million people left Ireland for places like the UK, Canada, the United States, Australia, New Zealand, among other places. It’s partly the reason for the massive Irish diaspora and why Saint Patrick’s Day is celebrated globally.
You can see some of my Irish ancestry is clearly unrelated, at least directly, to the Great Famine. But when you dig a bit deeper, you see the indirect connection. That John Barry who was an Irish stablekeeper who left Edinburgh for Philadelphia via Liverpool and New York, he was born to Irish parents in Cumberland, England—now Cumbria—who married there just after the end of the Great Famine and for whom there is no record prior to the Great Famine. In other words, they likely fled their home for fear of starvation and then in one generation their children all left England for America.
Irish genealogy is incredibly difficult, but it can also be incredibly rewarding. But you have have to keep digging and digging for even sometimes the shallowest roots.
It’s been a little while since my last post, and more on that will follow at a later date, but this weekend I glanced through the Pennsylvania Liquor Control Board’s annual report. For those unfamiliar with the Commonwealth’s…peculiar…alcohol laws, residents must purchase (with some exceptions) their wine and spirits at government-owned and -operated shops.
It’s as awful as it sounds. Compare that to my eight years in Chicago, where I could pick up a bottle of wine at a cheese shop at the end of the block for a quiet night in or a bottle of fine Scotch a few blocks from the office on Whisky Friday for that evening’s festivities. Here all your wine and spirits come from the state store.
And whilst it’s awful from a consumer/consumption standpoint, it makes for some interesting data, because we can largely use that one source to get a sense of the market for wine and spirits in the Commonwealth. That is to say, you don’t need to (really) worry about collecting data from hundreds of other large vendors. Consequently, at the end of the fiscal year you can get a glimpse into the wine and spirit landscape in Pennsylvania.
So what do we see this year?
To start I chose to revisit a choropleth map I made in 2020, just before the pandemic kicked off in the United States. Broadly speaking, not much has changed. You can find the highest per 21+ year old capita value sales—henceforth I’ll simply refer to this as per capita—outside Philadelphia, Pittsburgh, and up in the northeast corner of the Commonwealth.
The great thing about per capita sales are that, by definition, it accounts for population. So this isn’t just that because Philadelphia and Pittsburgh are the largest two metropolitan areas they have the largest value sales—though they do in the aggregate as well. In fact, if we look at the northeast of the Commonwealth in places like Wayne County we see the second highest per capita sales, just under the top-ranked in Montgomery County.
Wayne County’s population, at least of the legal drinking age, is flat comparing 2018 to 2022: 0.0% or just six people. However, sales over that same period are up 20.2% per person. That’s the 15th greatest increase out of 67 counties. What happened?
A little thing called Covid-19. During the pandemic, significant numbers of higher-income people from New York and Philadelphia bought second properties in Wayne County and, surely, they brought some of that income and are now spending it on wine and high-priced spirits.
Wayne County stands out starkly on the map, but it does not look like a total outlier. Indeed, if you look at the highest growth rates for per capita sales from 2018 to 2022, you will find them all in the more rural parts of the Commonwealth. Furthermore, almost every county that has seen greater than 15% growth is in a county whose drinking-age population has shrunk in the last five years.
Overall, however, the map looks broadly similar to how it did at the beginning of 2020. The top and centre of the Commonwealth have relatively low per capita sales, and this is Appalachia or Pennsyltucky as some call it. Broadly speaking, these are more rural counties and counties of lower income.
I spend a little bit of time out in Appalachia each year and have family roots out in the mountains. And my experience casts one shadow on the data. Personally, I prefer my cocktails, whiskies, and gins. But when I go out for a drink or two out west, I often settle for a pint or two. That part of the Commonwealth strikes me as more fond of beer than wine or spirits. And this dataset does not include beer. I have to wonder how the data would look if we included beer sales—though lower price-point session beers would still probably keep the per capita value sales on the lower end given the broad demographics of the region.
Finally, one last note on that second call out, Potter County having the lowest per capita sales at just under $42 per person. The number struck me as odd. The next lowest county, Fulton, sits nearly $30 more per person. Did I copy and paste the data incorrectly? Was there a glitch in the machine? Is the underlying data incorrect? I can’t say for certain about the third possibility, but I did some digging to try and hit the bottom of this curiosity.
First, you need to understand that Potter County is, by population, the 5th smallest with just over 16,000 total people living there. And as far as I can tell, it had just three stores at the beginning of 2022. But then, before the beginning of the new fiscal year, one of the three stores closed when an adjoining building collapsed. It was never rebuilt. And so perhaps 1/3 of the local population was forced to head out-of-county for wine and spirits. Compared to 2018, per capita sales in Potter County declined by 62%, and most of that is within the last year as the annual report lists the year-on-year decline as just under 54%.
In coming days and weeks I’ll be looking at the data a bit more to see what else it tells us. Stay tuned.
I last posted to Coffeespoons a year ago. Well, I’m back. Sort of.
Over the last year, there has been a lot going on in my family and personal life. Suffice it to say that all’s now relatively well. But the last 12 months forced me to prioritise some things over other things, and a daily(ish) blog about information design and data visualisation did not quite make the cut. And over all that time I also picked up a few new interests and hobbies, the most significant being photography.
Nevertheless I still enjoy information design. So I’m back. Though I doubt I will be posting every workday. After all, that’s when I have to go through my photographs and the other things I work upon nowadays. But, I don’t want to completely neglect this blog.
To ease back into the process, I updated a county map of the United States I last updated at the end of 2019, before the pandemic struck.
Where I’ve been in dark purple and counties through which I’ve driven or taken the train in light purple.
But I can’t really say I’ve travelled that far away from Philadelphia over the last year. The only work trip was to Chicago and for holidays I’ve travelled north to the Berkshires and New England several times. I’ve also added Providence and crossed off Rhode Island from the states I’ve visited. Finally, I’ve spent some time working remote from hotel rooms allowing me to watch baseball in nearby Minor League ballparks, Salisbury, Maryland’s Arthur Perdue Stadium, among others.
What remains abundantly clear are the two major phases of my life to date. I was born and raised in the greater Delaware Valley (Philadelphia, southeastern Pennsylvania, and southern New Jersey) and lived eight years in the Midwest (Chicago). And what connects all the journeys I’ve made from those home bases, if you will, is the tenuous county-wide tether stretching along I-80 across Indiana and Ohio into I-76 in Pennsylvania.
Unfortunately I still haven’t made it beyond the United States yet post-pandemic—hopefully that will begin changing in 2024—and so I have no updates for that map.
I cannot quite say when the next post will be. I don’t think it will be 12 months. But will it be monthly? Weekly? I can’t quite say. I doubt I will return to daily posting, because as those who know me well know, that was an enormous amount of time I spent every week preparing, writing, and posting content. But I also know well that a regular update frequency is critical to a blog, so that’s a thing I will be thinking about as 2023 begins to fade into autumn and winter.
As many long-time readers know, I was long ago bitten by the genealogy bug and that included me taking several DNA tests. The real value remains in the genetic matches, less so the ethnicity estimates. But the estimates are fun, I’ll give you that. Every so often the companies update their analysis of the DNA and you will see your ethnicity results change. I wrote about this last year. Well yesterday I received an e-mail that this year’s updates were released.
So you get another graphic.
The clearest change is that the Scottish bit has disappeared. How do you go from nearly 20% Scottish to 0%? Because population groups in the British isles have mixed for centuries. When the Scottish colonised northern Ireland, they brought Scottish DNA with them. And as I am fairly certain that I have Irish ancestors from present-day Northern Ireland, it would make sense that my DNA could read as Scottish. But clearly with the latest analysis, Ancestry is able to better point to that bit as Irish instead of Scottish. And this shouldn’t surprise you or me, because those purple bars represent their confidence bands. I might have been 20% Scottish, but I also could have been reasonably 0% Scottish.
Contrast that to the Carpatho-Rusyn, identified here as Eastern European and Russian. That hovers around 20%, which makes sense because my maternal grandfather was 100% Carpatho-Rusyn—his mother was born in the old country, present-day Slovakia. We inherit 50% of our DNA from each of our parents, but because they also inherit 50%, we don’t necessarily inherit exactly 25% from our grandparents and 12.5% from our great-grandparents, &c.
But also note how the confidence band for my Carpatho-Rusyn side has narrowed considerably over the last three years. As Ancestry.com has collected more samples, they’re better able to identify that type of DNA as Carpatho-Rusyn.
Finally we have the trace results. Often these are misreads. A tiny bit of DNA may look like something else. Often these come and go each year with each update. But the Sweden and Denmark bit persisted this year with the exact same values. If I compare my matches, my paternal side almost always has some Swedish and Danish ethnicity, not so for my maternal side. And importantly, those matches have more. Remember, because of that inheritance my matches further up on my tree should have more DNA, and that holds true.
That leads me to believe this likely isn’t a misread, but rather is an indication that I probably have an ancestor who was from what today we call Sweden or Denmark. Could be. Maybe. But at 2%, assuming the DNA all came from one person, it’s probably a 4th to a 6th great-grandparent depending on how much I and my direct ancestors inherited.
