My Irishness

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.

Credit for the piece is mine.

The Sun’s Over the Yardarm Somewhere

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?

A choropleth map of per 21+ capita sales of wine and spirits in Pennsylvania.

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.

Credit for the piece is mine.

It’s Been a Little While, But I Haven’t Gone Very Far

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.

Stay tuned.

Credit for the piece is mine.

No Matter What You Say, I’m Still Me

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.

Clearly there’s more work to do.

What It Is to be Asian American

Pew recently released a report into the Asian American experience. The report used 66 different focus groups to gather feedback and then summarised that with quotes, video bits, and lots of text. But at the beginning of the report was a nice little graphic that detailed the composition of the focus groups.

Lots of blocks and slices.

This is not a fancy graphic, nor need it be given its supplemental role to the overall piece. But I think it does a reasonable job of showing the construction of the overall focus group demographics, a key point to understanding the responses.

On the left we have a simple count of the number of focus groups by origin. For Indonesians we see there were two focus groups. And thus we have a number two besides the two blocks. Here the two is entirely extraneous and serves as a distracting visual sparkle at the end of the blocks. The advantage of using blocks as opposed to say a bar is that you can visualise the individual components or units, in this case there were two distinct focus groups of Indonesian origin. A user reading this chart should be able to count two blocks. And if they cannot count two blocks, I suspect they would be unable to grasp what the “2” means let alone the rest of the report.

To the right we have two pie charts. My…reticence…to use pies is well-known to long-time readers of Coffeespoons. But here we have the same type of data, counts of focus groups, and I have to wonder: why the designers did not stick with the same model of using individual blocks?

Here I chose to redesign the pie charts.

Nothing here is really new, I just removed the labels because people can count if they need to know the exact number. The labels add visual clutter to the design. And then of course I removed the pie charts and replaced them with blocks like on the left. I was even able to keep the layout roughly the same, albeit within my own graphics template.

Credit for the original goes to the Pew graphics department.

Credit for the redesign is mine.

Top Gun

Last night I went to see Top Gun: Maverick, the sequel to the 1986 film Top Gun. Don’t worry, no spoilers here. But for those that don’t know, the first film starred Tom Cruise as a naval aviator, pilot, who flew around in F-14 Tomcats learning to become an expert dogfighter. Top Gun is the name of an actual school that instructs US Navy pilots.

Back in the 1980s, the F-14 was the premiere fighter jet used by the Navy. But the Navy retired the aircraft in 2006 and it’s been replaced by the F/A-18E/F Super Hornet, a larger and more powerful version of the F/A-18 Hornet. So no surprise that the new film features Super Hornets instead of Tomcats.

And so I wanted to compare the two.

The important thing to note is that the Tomcat flies farther and faster than the Hornet. The F-14 was designed to intercept Soviet bombers that were equipped with long-range missiles that could sink US carriers. The Hornet was designed more of an all-purpose aircraft. It can shoot down enemy planes, but it can also bomb targets on the ground. That’s the “/A” in the designation F/A-18. In the role of intercepting enemy aircraft, the F-14 was superior. It could fly well past two-times the speed of sound and it could fly combat missions over 500 miles away from its carrier.

In the interception role, however, the F-14 had another crucial advantage: the AIM-54 Phoenix missile. It was a long-range air-t0-air missile designed for the Tomcat. It does not work with any other US aircraft and so the Hornet uses the newer AIM-120 AMRAAM, a medium-range air-to-air missile.

There are plans to design a long-range version of the AIM-120, but it doesn’t exist yet and so the Hornet ultimately flies slower, less distance, and cannot engage targets at longer ranges.

However, dogfighting isn’t about long-range engagements with missiles. It’s about close-up twisting and turning to evade short-range missiles and gunfire. And even in that, the F-14 could use four AIM-9 Sidewinder missiles whereas the F/A-18 carries only two on its wingtips.

By the 2000s F-14 was an older aircraft and while the moving, sweeping wings look cool, they cause maintenance problems. They were expensive to maintain and troublesome to keep in the air. But they are arguably superior to what the Navy flies today.

Moving forward, the Navy is beginning to introduce the F-35 Lightning II to the carrier fleets. Maybe I’ll need to a comparison between those three.

Credit for the piece is mine.

Boston: Sportstown of the 21st Century

Tonight the Boston Celtics play in Game 1 of the NBA Finals against the Golden State Warriors, one of the most dominant NBA teams over the last several years. But since the start of the new century and the new millennium, more broadly Boston’s four major sports teams have dominated the championship series of those sports. In fact tonight marks the 19th championship series a New England team has played since 2001. And in those 18 series thus far, Boston teams have a 12–6 record.

Let’s go Celtics.

Of the 12 titles won, the New England Patriots account for half with six Super Bowl victories out of nine appearances. The Boston Red Sox have won all four World Series they have played in since 2001. Rounding out the list, the Celtics and Bruins have each won a single championship with the Bruins appearing in three Stanley Cups and the Celtics in two NBA Finals. Tonight begins their third.

Credit for the piece is mine.

Kids Do the Darnedest Things: Shoot Other Kids

Last month, a 2-year old shot and killed his 4-year old sister whilst they sat in a car at a petrol station in Chester, Pennsylvania, a city just south of Philadelphia.

Not surprisingly some people began to look at the data around kid-involved shootings. One such person was Christopher Ingraham who explored the data and showed how shootings by children is up 50% since the pandemic. He used two graphics, one a bar chart and another a choropleth map.

The map shows where kid-involved shootings have occurred. Now what’s curious about this kind of a map is that the designer points out that toddler incidents are concentrated around the Southeast and Midwest. And that appears to be true, but some of the standouts like Ohio and Florida—not to mention Texas—are some of the most populated states in the country. More people would theoretically mean more deaths.

So if we go back to the original data and then grab a 2020 US Census estimate for the under-18 population of each state, I can run some back of the envelope maths and we can take a look at how many under-18 deaths there had been per 100,000 under-18 year-olds. And that map begins to look a little bit different.

If anything we see the pattern a bit more clearly. The problem persists in the Southeast, but it’s more concentrated in what I would call the Deep South. The problem states in the Midwest fade a bit to a lower rate. Some of the more obvious outliers here become Alaska and Maine.

As the original author points out, some of these numbers likely owe to lax gun regulation in terms of safe storage and trigger locks. I wonder if the numbers in Alaska and Maine could be due to the more rural nature of the states, but then we don’t see similar rates of kid deaths in places like Wyoming, Montana, and Idaho.

Credit for the original piece goes to Christopher Ingraham.

Political Hatch Jobs

Earlier this week I read an article in the Philadelphia Inquirer about the political prospects of some of the candidates for the open US Senate seat for Pennsylvania, for which I and many others will be voting come November. But before I get to vote on a candidate, members of the political parties first get to choose whom they want on the ballot. (In Pennsylvania, independent voters like myself are ineligible to vote in party primaries.)

This year the Republican Party has several candidates running and one of them you may have heard of: Dr. Oz. Yeah, the one from television. And while he is indeed the front runner, he is not in front by much as the article explains. Indeed, the race largely had been a two-person contest between Oz and David McCormick until recently when Kathy Barnette pulled just about even with the two.

In fact, according to a recent poll the three candidates are all statistically tied in that they all fall within the margin of error for victory. And that brings us to the graphic from the article.

It would be funny to see a candidate finish with negative vote share.

Conceptually this is a pretty simple bar chart with the bar representing the share of the support of those polled. But I wanted to point out how the designer chose to represent the margin of error via hatched shading to both sides of the ends of the red bar.

In some cases the hatch job does not work for me, particularly with those smaller candidates where the bar goes negative. I would have grave reservations about the vote should any candidate win a negative share of the vote. 0% perhaps, but negative? No. I also don’t think the grey hatching works as well over the grey bar in particular and to a lesser degree the red.

I have often thought that these sorts of charts should use some kind of box plot approach. So this morning I took the chart above and reworked it.

Now with box plots.

Overall, however, I really like this designer’s approach. We should not fear subtlety and nuance, and margins of error are just that. After all, we need not go back too far in time to remember a certain candidate who thought she had a presidential election locked up when really her opponent was within the margin of error.

Credit for the piece goes to John Duchneskie.

Russo-Ukrainian War Refugees: 12 April

Another week, more combat and refugees in Ukraine. I’m going to try and hold the war update until tomorrow pending some news that hasn’t been confirmed yet: the fall of Mariupol. Instead, we’re going to again look briefly at the refugee situation in Ukraine—technically outside. I haven’t seen a recent number on the internally displaced, though we have begun to see some people return to Ukraine especially in the north and around Kyiv. It’s unclear to me if the data includes those people returning.

Regardless, we are at over 4.6 million Ukrainians who have fled Ukraine.

Slowing down of late.

The question now is as Russia refocuses its effort now on the Donbas—though fierce fighting has been waged in the area for eight years now—will these numbers begin to see a notable change.

Credit for the piece is mine.