African Descent in African Americans

A study published last week explores the long-lasting impact of the Atlantic triangle trade of slaves on the genetic makeup of present day African Americans. Genetic genealogy can break down many of what we genealogists call brick walls, where paper records and official documentation prevent researchers from moving any further back in time. In American research, slavery and its lack of records identifying specific individuals by name, birth, and place of origin prevents many descendants from tracing their ancestry beyond the 1860s or 50s.

But DNA doesn’t lie. And by comparing the source populations of present day African countries to the DNA of present day Americans (and others living in the Western hemisphere), we can glean a bit more insight into at least the rough places of origin for individual’s ancestors. And so the BBC, which wrote an article about the survey, created this map to show the average amount of African ancestry in people today.

Average amount of African genetic ancestry in present day populations of African descent

There is a lot to unpack from the study, and for those interested, you should read the full article. But what this graphic shows is that there is significant variation in the amount of African descent in African-[insert country here] ethnic groups. African-Brazilians, on average, have somewhere between 10–35% African DNA, whereas in Mexico that figures falls to 0–10%, but in parts of the United States it climbs upwards of 70–95%.

In a critique of the graphic itself, when I look at some of the data tables, I’m not sure the map’s borders are the best fit. For example, the data says “northern states” for the United States, but the map clearly shows outlines for individual states like New York, Pennsylvania, and New Jersey. In this case, a more accurate approach would be to lump those states into a single shape that doesn’t break down into the constituent polities. Otherwise, as in this case, it implies the value for that particular state falls within the range, when the data itself does not—and cannot because of the way the study was designed—support that conclusion.

Credit for the piece goes to the BBC graphics department.

Monday Covid-19 Data

The data from Monday provided yet more evidence that the outbreak is flattening in several states. However, in some, the outbreak continues to pick up steam. Does this runs contrary to the idea that as a country is flattening? Not necessarily, but it is important to remember that a country that spans a continent and holds 330 million people will experience the pandemic differently at different times. So some states like Washington will be first, and others will be last.

Our five states cover the range of worsening to stabilising. We hope that those stabilising states soon enter the improving phase. Though to beat the dead horse, I would add that just because a state is improving doesn’t mean we can all go back to life like we knew it two months ago. That would likely result in us being right back here shortly thereafter.

The situation in Pennsylvania
The situation in Pennsylvania

Pennsylvania continues to be a state where the pandemic is spreading within the denser metropolitan areas of Philadelphia and Pittsburgh, leaving the central T to see fewer cases that spread more slowly.

The situation in Delaware
The situation in Delaware

Delaware might be approaching an inflection point, given that its most populous county, New Castle, is about to reach 1000 cases. (By the end of today it likely will if its new case trend holds.) We know that deaths lag new cases, and so the worry is that the number of deaths will begin to increase rapidly. The hope is that the slow initial growth of the outbreak will have left hospitals able to better cope over the longer time frame than if everyone had gotten sick all at once.

The situation in Virginia
The situation in Virginia

Virginia is a state that we will contrast to New Jersey, which I will write about last. Because Virginia is a state where it appears the outbreak is beginning to pick up steam and accelerate, rather than flatten. There was the significant drop in cases on Sunday, but that was due to the state’s enhancement of reporting data. (Their website now includes many new statistics.) But just like that the Monday data showed over 450 new cases on Monday. The question will be whether or not over the remainder of the week those new case numbers fall from over 450 to less than 400 to show that the state can flatten the curve before the outbreak becomes especially severe.

The situation in Illinois
The situation in Illinois

Illinois has shown a lot of variability in its day-to-day numbers, hence the advantage of the rolling average. But even that has appeared a wee bit jagged. It’s tough to see the curve flattening just yet, but if we receive updates today and over the next few days that cases are consistently lower, than we might just be able to say the curve is flattening.

The situation in New Jersey
The situation in New Jersey

And of course in New Jersey we have a state where the curve really and truly has flattened. We have yet to see sustained evidence of a decline in the number of new daily cases. As I said before, this might be more a situation where the outbreak has stabilised and roughly the same number of new cases is being reported daily. Of course the hope is that whatever that rate is falls below the excess capacity threshold of the state’s hospitals.

But I also want to take a look at the state of New Jersey with a degree of granularity. Because, as I noted with Virginia above, not all states are at the same point in their outbreak. And the same can hold true within states. We know that the outbreak in New Jersey began in the north and was very late in reaching some parts of South Jersey. So the same metrics we run for the state, we can run for the counties—though the data I have been collecting from the states only goes back as far as St. Patrick’s Day.

New Jersey's curves
New Jersey’s curves

The northern counties, where the state has been hardest hit, have clearly begun to see the curve bend. But in the south the story is a bit more mixed. Some, like Burlington and Ocean, have seen the curve noticeably flatten. But in Camden and Mercer Counties, home to Camden and Trenton, respectively, the evidence is not quite there. Instead, in these populous counties there exists the very real possibility that the outbreak will continue accelerating for hopefully a very short while.

Credit for the pieces is mine.

Researching the Family History in Ashland, Wisconsin

I’m presently off in the northern reaches of Wisconsin, Ashland in particular, researching part of my family’s history. To aid me in understanding just how this frontier-following family moved over one century, I put together a crude map and a timeline to give me context (and jog my memory) while searching through files in the courthouse.

The movements of the Spellacy family
The movements of the Spellacy family

I am calling the map a migration map. It shows the locations where family members moved to in 1849: Sheboygan (from New Brunswick, Canada). And then how they quickly began to disperse, but slowly head north to Ashland County, before most ultimately headed to the West Coast. (My direct ancestors are that group near the bottom that move back to the in-laws original home of western Massachusetts.)

What I struggle with keeping in mind is that here we are looking at a perfectly rendered and understood map of modern Wisconsin. But in 1849, the state was but one year old and most of the towns to which this family would be going were only a decade or so old and still very much frontier towns without amenities. (Which is why I imagine the women of the family stayed in Milwaukee until the settlements in the north were, well, settled.)

To the right is a timeline. The details are not terribly important and in fact it is poorly designed. But, it was quick to make and will hopefully help me keep the names straight and the places for which I am looking top-of-mind.

Put the two together and you have an example of how I create visualisations for myself just to help me with my own work and research.