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.
This time last week I wrote about how we should not be surprised at rising levels of coronavirus in the states of Pennsylvania, New Jersey, Delaware, Virginia, and Illinois. After all, our elected officials reopened economies despite data saying they should do otherwise. On top of that, people have been engaging in reckless behaviour and seemingly abandoning the very behaviours that had been leading to declining rates. With those two failures, our last hope is that vaccines will come quickly and be widely taken by the public.
A week hence.
Well, we are beginning to see some divergent patterns, especially with new cases.
New case curves for PA, NJ, DE, VA, & IL.
Last week there was some evidence that New Jersey might be bucking the trend and headed downwards after weeks of rising new cases. And now that appears to be a more sustained trend as the line for the Garden State’s seven-day average clearly began headed the right direction this past week.
That’s the good news. The bad news is that we continue to see rising numbers of new cases in Pennsylvania, Delaware, and Illinois. Although if we want to try and find the positives in the bad, we can see that Delaware’s upward trend remains fairly shallow. Illinois, while steeper, is rising from a lower base as the Land of Lincoln managed to reach low, summer levels of new case spread earlier this year. And in Pennsylvania, there is a bend in the curve, an inflection point, that could indicate growth in the number of new cases is slowing. We still need to see it turn negative, but slowing growth is better than increasing growth.
Virginia splits the difference between those sets. It remains at an elevated level of new case transmission, but the upward tick we saw—unlike the other states—was not followed by a general surge in new cases. The little rise we did see, in fact seems to have perhaps shifted back downward.
One of the big questions in this current wave of new cases is will deaths rise? We are seeing increasing numbers of new cases and hospitalisations, but will deaths follow? The hope is that we have vaccinated enough of the most vulnerable populations to prevent them from suffering the most serious of results.
Death curves for PA, NJ, DE, VA, & IL.
So far so good. While death rates remain slightly elevated over summer levels, we do not yet see any signs of rising numbers of deaths. The only possible exception is Virginia, where cases bottomed out after the state added delayed death certificates from the holidays, but have risen in recent days.
Finally we have vaccinations. Here is the best news at which we can look. We can now say that at least 20% of the populations of Pennsylvania, Virginia, and Illinois are fully vaccinated. To be clear, that is still a long way from herd immunity levels, but that’s 20 percentage points more than we had four months ago.
Total full vaccination curves for PA, VA, & IL.
One big outstanding question is how much, if at all, can vaccinated people spread coronavirus? This is why we need to continue to wear masks and socially distance even those who have been vaccinated. But at some point—I don’t know when—these increasing levels of full vaccination should begin to flatten the new case curves. Could that be what’s flattening the curves in New Jersey, Virginia, and Pennsylvania? It’s too early to say, but one can hope.
In many cities through the United States, real estate represents a hot commodity. It’s not difficult to understand why, as have covered before, Americans are saving a bit more. Coupled with stay-at-home orders in a pandemic, spending that cash on a home down payment makes a lot of sense for a lot of people. But with little new construction, it’s a seller’s market.
The Philadelphia Inquirer covers that angle for the Philadelphia region and in the article, it includes a map looking at time to sell a house. And it’s that interactive map I want to look at briefly this morning.
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.
This morning I read a piece in Politico Playbook that broke down President Biden’s $2.25 trillion proposal for infrastructure spending. A thing generally regarded as the United States sorely needs. $2.25 trillion is a lot of money and it’s a fair question to ask whether all that money is really money for infrastructure.
Because, it turns out, it’s not.
Please, sir, may I have more train money?
That isn’t to say money spent on job retraining or home care services wouldn’t be money well spent. Rather, it’s just not infrastructure.
But politics and the English language is a topic for another day. Oh wait, somebody already did write about that.
Last week the Guardian published an article about drinking water pollution across the United States. Overall, it was a nicely done piece and the graphics within segmented the longer text into discrete sections. Each unit looks similar:
PFAs.
The left focuses on a definition and provides contextual information. It includes small illustrations of the mechanisms by which the pollutant enters the water system. To the right is a chart showing the levels of the contamination detected in the 120 tests the Guardian (and its partner Consumer Reports) conducted.
In almost all of the charts, we see the maximum depicted on the y-axis. And the bars are coloured if that observation station exceeds the health and safety limits. (The limit is represented by the dotted line.)
But towards the end of the piece we get to lead, a particularly problematic pollutant. There is no safe level of lead contamination. But how the piece handles the lead chart leaves a bit to be desired.
But how bad is it, really?
The first thing is colour, but that’s okay. Everything is red, but again, there is no safe level of lead so everything is over the limit. But look at the y-axis. That little black line at the top indicates a discontinuity in the lines, in other words the values for those three observations are literally off the chart.
But does that work?
First, this kind of thing happens all the time. If you ever have to work with data on either China or India, you’ll often find those two nations, due to their sheer demographic size, skew datasets that involve people. But in these kind of situations, how do we handle off the charts data points?
There is a value to including those points. It can show how extreme of an outlier those observations truly are. In other words, it can help with data transparency, i.e. you’re not trying to hide data points that don’t fit the narrative with which you’re working.
In this piece, it’s never explicitly stated what the largest value in the data set is, but I interpret it as being 5.8. So what happens if we make a quick chart showing a value of 6 (because it’s easier than 5.8)? I added a blue bar to distinguish it from the the rest of the chart.
It’s pretty bad.
You can see that including the data point drastically changes how the chart looks. The number falls well outside the graphic, but it also shows just how dangerously high that one observation truly is.
But if you say, well yeah, but that falls outside the box allowed by the webpage, you’re correct. There are ways it could be handled to sit outside the “box”, but that would require some extra clever bits. And this isn’t a print layout where it’s much easier to play with placement. So what happens when we resize that graphic to fit within its container?
And resized
You can see that All the other bars become quite small. And this is probably why the designers chose to break the chart in the first place. But as we’ve established, in doing so they’ve minimised the danger of those few off-the-charts sites as well as left off context that shows how for the vast majority of sites, the situation is not nearly as dire—though, again, no lead is good lead.
What else could have been done? If maintaining the height of the less affected bars was paramount, the designers had a few other options they could have used. First, you could exclude those observations and perhaps put a line below the 118 text that says “for three sites, the data was off the charts and we’ve excluded them from the set below.”
I have used that approach in the past, but I use it with great reluctance. You are removing important outliers from the data set and the set is not complete without them. After all, if you are looking to use this data set to inform a policy choice such as, which communities should receive emergency funding to reduce lead levels, I’d want to start with the city in blue. Sure, I would like everyone to get money, but we’d have to prioritise resources.
I think the best compromise here would have actually been a small tweak to the original. Above the three bars that are broken (or perhaps to the right with some labelling), label the discontinuous data points to provide clearer context to the vast majority of the sites, which are below 0.5 ppb.
As easy as ABC
This preserves the ability to easily compare the lower level observations, but provides important context of where they sit within the overall data set by maintaining the upper limits of the worst offenders.
Credit for the piece goes to the Guardian’s graphics department.
Last week I wrote about how the inevitable rise in new Covid-19 cases was occurring in Pennsylvania, New Jersey, Delaware, Virginia, and Illinois. Now, one, in the last week, we saw no evidence of states preparing to reinforce their public health and safety restrictions. And two, whilst we have no data on people not following guidelines, anecdotally a large group of people threw a party in my building’s common amenities space so it does seem like people are feeling less inclined to wear masks, socially distance, and isolate to their own households.
Those two conditions, of course, do not help reduce the case count. Instead they add to it. So it should come as no surprise that Covid-19 continues to rapidly spread in our five states, though some are doing worse than others.
New case curves for PA, NJ, DE, VA, & IL.
New Jersey and Pennsylvania arguably performed the worst. If we look at the peak to trough decline from early winter’s surge to late winter’s nadir, we can see that New Jersey has reached 40% of that peak. Pennsylvania enjoyed a better decline and so has a large gap, but is still nearing 20% its previous peak.
Illinois is also remarkable—again not in a good way—as its peak to trough fall was even greater than Pennsylvania’s, however it’s also now clearly rising. The Land of Lincoln, however, did manager to reach late summer levels of new cases—good. But those are now rising—bad. Delaware too is seeing a rise, albeit at a slower rate than its two tristate neighbours.
Only Virginia’s rise remains slight, barely discernible in the chart.
Deaths, while not exactly good news, aren’t exactly good news either. Last week I mentioned how they had stalled out and stopped declining. That is better than rising death rates, but the levels of deaths per day is still higher than we saw last summer. In other words, things could be significantly better even in pandemic terms.
Death curves for PA, NJ, DE, VA, & IL.
Last week? Deaths continued to stubbornly persist at those elevated levels. We remain vigilant, looking for any indication that deaths will follow the rates of new cases and hospitalisations and begin to climb.
The hope, of course, is that we have vaccinated enough of the most at risk populations to prevent a surge in deaths. But, we just don’t know yet. The only good news is that vaccinations continue to progress.
Vaccination curves for PA, VA, & IL.
Illinois has surpassed 18% of its population being fully vaccinated. Virginia is not far behind at 17.75%. Pennsylvania, because of the bifurcated nature of its data reporting, remains unclear. It sits at 17.8% fully vaccinated, but Philadelphia has not posted updated data since late Thursday. It’s likely that the Commonwealth has joined Illinois in surpassing 18%, but it’s not fully certain.
Also this past week, the CDC updated its guidance for the fully vaccinated, saying that it was safe for them to travel. I take some issue with this, primarily on the messaging front.
First, we need to be clear about what fully vaccinated means. It means two weeks after your final dose. For Johnson & Johnson’s vaccine, that means two weeks after your shot as you only receive one. For both Pfizer and Moderna, you are only fully vaccinated two weeks after your second shot—not before. And keep in mind with Pfizer you need to wait three weeks between first and second dose. With Moderna it’s four weeks. In other words, with J&J you need to wait two weeks after your first (and only) shot before you can begin to follow the loosened guidelines. If you receive Pfizer’s, you need to wait five weeks from your first shot, assuming you do receive your second three weeks later, and with Moderna it’s six weeks, again assuming the recommended four week gap.
The problem is that only about 20% of the US population is fully vaccinated. And with the virus spreading at high rates and at high levels, it poses a significant risk as the newer, more lethal, and more infectious variants could take root in the United States and overwhelm the healthcare systems of the 50 states. We do not yet know if fully vaccinated people can spread the virus if they do become infected.
I think the advice should have remained to refrain from all but essential travel until we reached a high percentage of fully vaccinated folks. I ballparked earlier this week something like 2/3 the estimated amount of full vaccinations required for herd immunity (est. at 75%). In other words, keeping restrictions on travel until at least 50% of the US becomes fully vaccinated.
We remain several weeks away from that milestone, unfortunately. I understand the desire/urge people have to get out and do things and enjoy spring after a year of isolation. Sadly, if winter was the darkest/hardest part of the pandemic, I think that makes spring and early summer the most challenging. Because we see progress, we see the light at the end of the tunnel, and it coincides with warmer weather and we want nothing more to get out and do things and see people. But that is the last thing we need to be doing at this point.
I’ve often described the vaccination as the marshmallow test. In a study, scientists presented kids with a marshmallow. They could eat the marshmallow immediately, but if they waited 15 minutes, unsupervised, they could then have an additional marshmallow. We are all just grabbing that first marshmallow whilst the promise of a more normal summer is ours if we can wait just 15 minutes.
Last week the Philadelphia Inquirer published an investigation of the staggering number of horse deaths in Pennsylvania’s race track facilities. I found the article fascinating, but admittedly at a point or two a wee bit squeamish when the author described how horses essentially die. Then about halfway through the article I ran into the first of two graphics looking at the data.
Seeing red…
The first is pretty simple, a timeline of deaths over the course of one year, 2019. Overall it works, you can clearly see clusters of racing deaths, but that those clusters spread across the year. When I sat with the graphic for a moment, however, a few things began to stick out at me. The first was a distracting vibration in the background. Not the alternating beige and blue of the months, but if you look closely you’ll see tightly spaced lines within the colour fields: presumably the days of the month for aligning the deaths.
On a large enough graphic it makes all the sense to tick off sub-monthly increments, but in this space I would have probably opted to show only the months. Maybe weeks could have worked, as that approach may have reinforced the statistic about a horse dying every six days on average.
The second point is the black stroke or outline of each dot. Here the designer faces a challenging constraint. Essentially, the smaller the dot (or the symbol) the brighter the colour. In a rich, blood red colour you have a dark heavier colour. Compare that to say a stop sign that is bright red. It has a lighter feel. The blood red colour, in a given space, has let’s say an amount of black ink or pixels—I’m simplifying here—mixed in with the red. But in a large area, there’s enough red ink or pixels to still be clearly blood red. The stop sign red has no other colours but red. And in large areas, it can be an eye-stabbing amount of red—precisely why it’s likely so useful for, you know, stop signs.
But at the small scale of these very small dots, you still proportionally have the same amount of red and black ink, but with fewer and fewer amounts, the eye can begin to experience difficulty in truly reading the colour for what it is. For example, in an area of say 49 pixels (7×7), while the ratio of red to black may be consistent, you still only have a total of 49 pixels with which to convey “red” to the reader. Consequently, in smaller spaces, you may find that designers sometimes opt for brighter colours, a la stop sign red, than they would in larger fields of colour.
Here we have a nice use of brighter red, green, and yellow. (I will quickly add that the choice of red and green can be problematic for colour blindness, but I don’t want to revisit that here.) But to provide better separation between those small, circle sized fields of colour a border probably helps. A thin black line, or stroke, makes sense. But the black is darker than the colours themselves, thus it can draw more attention than the colour fill. And that begins to happen here. I wonder if a thin white stroke may have been less distracting and placed more emphasis on the fill colours.
As I said, overall a really nice if not sobering graphic in an important but disturbing article. I think a few small tweaks could really bring the graphic over the finish line. Pun fully intended. Sorry, not sorry.
Two weeks ago I wrote about how new cases in the states of Pennsylvania, New Jersey, Delaware, Virginia, and Illinois were stalling out, i.e. no longer declining. Additionally, with the exception of Illinois, they were stalling at rates far higher than what we saw last summer. I wrote
This means that the environment is ripe for a new surge of cases if people stop following social distancing and begin resuming indoor activities with other people. Sadly, both those things appear to be occurring throughout the US.
Two weeks hence, one of one thing inevitably occurred.
New cases are now rising in all five states. I wrote about the flat tails of the curves for the seven-day averages. A quick look at the chart shows those have swung upwards, in some cases sharply.
New case curves in PA, NJ, DE, VA, & IL.
Two weeks ago I referenced Europe as a cautionary tale. Governments there eased up on their restrictions, cases surged, and then as hospitalisations rose, governments had to reimpose restrictions and effect new lockdowns. Europe has typically been 3–4 weeks ahead of us throughout the pandemic. So that we are now at a point where we are seeing rising cases, absolutely none of this should be surprising.
The evidence has been in our faces for weeks, plus we have the European example to look at. Reopening makes no sense until we can get case numbers lower, especially with new more virulent and lethal strains of coronavirus now circulating.
Deaths too have been trending the wrong way over the last few weeks.
Death curves for PA, NJ, DE, VA, & IL.
We have seen the curves largely bottom out. And if you look closely, these bottoms are higher than the rates we saw last summer, in some cases more than 3× as much. This flattening occurred just a few weeks after cases began to flatten. The question becomes, will they rise in a few weeks time? Or have we vaccinated enough of our most vulnerable populations?
That’s the real wildcard.
Right now, we have only fully vaccinated about 15% of the populations of Pennsylvania, Virginia, and Illinois.
Vaccination curves for PA, VA, & IL.
Is that enough to prevent hospitalisations and deaths in what looks like will be a fourth wave?
Or just shake my hand, because today marks the second St. Patrick’s Day spent in isolation. I am lucky, of course, because two years ago I spent the holiday in Dublin. One of those bucket list kind of things. There I ran into a(n American) friend who was coincidentally in town. Then the next day I took the train to Cork to visit another friend. If you don’t count weddings, I think that was the last big trip I took.
Two years hence, I am here in my flat alone on a holiday meant to be spent with family and friends. But in the last year, I made significant progress on my Irish genealogy. For part of that progress I took two additional DNA tests. So this St. Patrick’s Day seems like a good time to reflect on those tests.
For those that don’t know, I do a lot of genealogy work as a hobby. Primarily I focus on paper records, but DNA is an important piece of the puzzle. In a sense, it is the only record that cannot lie. It will reveal your biological connections to family that may have been otherwise lost. And it cannot be faked.
But that’s only true for your genetic matches. Those are the real power of taking a DNA test. I would bet, however, that most people initially take the tests for the ethnicity estimates. On a day like today, how Irish are you? How Irish am I?
That’s a lot of green.
Not surprisingly, I’m pretty Irish.
Of course, if you look at me, those Irish values do not quite equal each other. So what’s the deal? After all, the underlying DNA does not change from spit tube to cheek swab.
The first thing to know is that in one sense, ethnicity is, like so many things, a social construct. Super broadly, every individual is unique—except twins. Of course humans have spread across the globe and in that spread, certain regions have evolved incredibly slight differences between the populations. In addition to those genetic differences, the populations created civilisations and cultures. An ethnicity, in a sense, is a group of people who share that culture, civilisation, and genetic similarities vis-a-vis genetic differences across the world.
Importantly, within those groups, we still have differences. The Irish, for example, are known for freckles and red hair. But not all Irish have those traits. Instead, again super broadly, we say that for a group of people, a certain percentage will share a certain set of features. Consequently, within an ethnic group, you will still have variations and outliers. In some cases because generations ago a traveller from a different group entered the gene pool for some reason or another. And while the offspring might identify entirely with their new civilisation and culture, their genes don’t lie and a DNA test would reveal their traits from their ancestor’s foreign gene pool.
The second point to make is that Ireland is a fairly modern creation. Ireland did not exist as a sovereign state until 1922. Before then, the idea of Ireland existed. The country, however, did not. A better example would be German or Italian. Neither Germany nor Italy existed until the 1870s and 1860s, respectively. If you have “German” ancestors who arrived in Philadelphia in 1848, you don’t have German ancestors. You have ancestors from one of the various principalities or bishoprics comprising the German Confederation. Italy had the Venetian Republic, the Kingdom of the Two Sicilies, and many others. Being Irish, German, or Italian is thus a modern construct.
The third point is that identifying anyone as any of these ethnic groups requires a baseline for a comparison. To do that, you need a reference population in the area you are going to define as Ireland, Germany, or Italy. But humans have migrated throughout history. Ireland was conquered by the English. Germans…well, let’s just say Germans have a history with conquering parts of Europe. And so you can see exchanges of genetic information among populations pretty easily. And over time, those genetic populations evolve.
Take those three points and add them together in admixture test and your results are really only good back to about 500 years. And even then, you may find yourself belonging to something incredibly vague and all-encompassing because, especially as with France and Germany, there’s been too much mixture to get so granular as to fit ourselves within the borders of modern political states.
In the above results, you can see my “Irishness” varies from 63% to 75%. Though, as far as I know 21/32 (66%) of my 3xgreat-grandparents arrived from Ireland. That’s why I say I’m 2/3 Irish. But, genetically, I may be more or less because those 21 might have English or Scottish ancestors. Ancestry says I may be 18% Scottish, but whilst I have ancestors who lived in Scotland, I’m not aware of any ancestors born and raised for multiple generations in Scotland.
And then that’s just how Ancestry defines it. Compare that to my results from My Heritage. Because of the aforementioned difficulty in separating out certain population groups, they lump the Irish, Scottish, and Welsh together. Add my Ancestry Irish and Scottish together and I have 81%, not far from My Heritage’s 85% estimate. Then look at my results from Family Tree. They estimate me as 75% Irish, but add in the 10% Scandinavia and I’m up to 85%.
That brings me to my last point about DNA tests. It’s probably fair to say that I’m something like 80–85% genetically from the British Isles/North Sea region. What about the other 15–20%?
You will often hear you receive half your DNA from each of your parents. And they get half from each of theirs and so on and so forth. I’ve had conversations with folks who take that to mean they get 25% from each grandparent and 12.5% from each great-grandparent et cetera. But that’s not quite true.
You do receive 50% of your DNA from your father and the other 50% from your mother. But that 50%, well that’s a sort of random sample from the share your parents received from their parents.
My maternal grandfather was 100% Carpatho-Rusyn. For generations, his ancestors lived, reproduced, and died in the Carpathian Mountains. If we received exactly half from each previous generation, I should expect 25% of my DNA from my grandfather. But Ancestry, which has the best representation of this small ethnic group, says it’s 17% (though they give it as a range of being between 2 and 27%). In other words, I’m missing seven percentage points.
And so if you take a DNA test and you know you have a great-great grandparents of Irish descent, you may only see a small fraction in your results. If your connection to Ireland (or anywhere else) is even further back, the result becomes smaller still. In fact, beyond 5–7 generations back, you may not even inherit any genetic material from a specific ancestor in your family tree.
But ultimately, for today, as I wrote in one of my very first posts here on Coffeespoons, back in 2010, on St. Patrick’s Day, we’re all at least a little bit Irish.
Hopefully next year we’ll be able to celebrate in person.
