I thought I would talk about a thing
called a Nightingale diagram.
All right, what's that?
So it comes- it comes down to the fact
that I was looking at,
somewhat morbidly, looking at Covid
mortality data
and was kind of unimpressed with the
ways that I had available to plot it.
And there's always this problem that
when you look at the usual way the data
gets plotted,
it gets like plotted January
through to December and then
it goes back to January again when
you're looking at multiple years and so
there's always this kind of
discontinuity in the data at the end of
the year, which of course is not any in
any way
real it's not physical it's just the way
you choose to plot the data.
So I was trying to see if there was any
way of plotting it in a way that kind of
gets rid of that discontinuity at the
beginning and the end of the year. It
turns out this is a solved problem, in
fact it's a long solve problem.
The reason why the solution is a
thing called a Nightingale diagram is
because it goes back to Florence
Nightingale in the middle of the 19th century
who as well as you know being famous for
her nursing was actually also famous for
her data visualisation.
We tend to kind of think of data
visualisation as being something that
was only invented
with the computer age and with modern
graphics but actually it turns out it
has a pretty long history.
But she was one of the very early
proponents; and in fact not only was she
producing beautiful plots but she was
doing them for much the reason where-
that people do data visualisation now
which was to try and make an argument to
convince
people of her point of view, in this case
politicians.
She ended up publishing a book and a
whole load of pamphlets which were
distributed
kind of making her case; and it was
really the case for
good cleanliness standards when you're
treating sick people,
and that was really what kind of- how she
saved many many lives
was by convincing everyone that actually
good cleanliness was the way to do it.
The plot she came up with was instead of
plotting things as a line she plotted
things around a circle.
And the her plots went through various
iterations, you can kind of see as she
was in kind of improving the way she was
presenting the data. So let me show you
the first one
that you came up with: this was the thing
she referred to as a bat-wing diagram,
and in essence you're going around in a
circle and as you go around the circle
for the different months of the year
how far out you put the point is to do
with the thing you're measuring. And she
was also actually measuring mortality,
this was death
in the Crimean war, this was death of
soldiers in the Crimean war. And so you
could actually see
when death rates were very high and
then then she kind of jumps over to this
second diagram
to continue the circle around and really
this is showing when she started
introducing her her various
sanitary measures for looking after the
wounded soldiers
that the death rates plummeted. And this-
I mean this makes a rather salutary
point that the-
what what's actually measured here
radially outwards is the deaths
per thousand soldiers per year
effectively;
and it actually peaks at over a thousand.
So what this is saying is that if the
death rate had continued at that rate
the entire
entire army would have been killed in
less than a year
so she was really making rather a strong point.
As I say it was kind of radially outwards,
she didn't like this version of the plot
because because she shaded it in to
create this kind of bat-wing effect
she was concerned that people would be
looking at the area and thinking the
area was what was representing rather
than the radial displacement.
And and of course they don't quite scale
the same way because because
area goes as distance squared and so
actually the the area
is measuring a different thing from what
the radius is measuring. So in her next
sort of iteration, and the
the plot that she's most famous for,
for illustrating this she kind of came
up with a
coloured version of the same plot. Only
now she has actually scaled it so that
the areas are representing death
and the extra subtlety she's introduced
is she's now got colours in here which
represent different things.
So for example the blue were the
soldiers who were dying of disease
in the hospitals whereas the pink was
the soldiers who were dying of their wounds,
of actually the injuries they'd received.
And this very graphically makes this
incredibly strong point that actually
the main thing that was killing the army
wasn't actually getting shot
it was what happened after they got shot
that they then got cholera and various
other nasty diseases and were dying in
the hospital.
And again she shows the effect of what
happens when she introduces her
mitigation steps and suddenly the
numbers drop
right back down again. So all this
inspired me to think
that the right way of presenting the
mortality data when I was trying to
understand what was going on with Covid
mortality was to do the same thing.
So I actually made a a version of her
plot;
and this is actually like like her first
plot this is actually one of these ones
where the radial distance is measuring
the mortality.
And so I was really just interested to
see quite how out of line
this last year has been in terms of of
deaths in the population. And it's- you know
it's pretty blatant when you look at
this that nothing much was happening
until you get to April and then suddenly
things jump up to these incredibly high
values, drop back down again to more or
less nothing very much but then actually
you can see from October onwards in the UK,
deaths have been riding high above the
average. The black line here is the last
10 years,
the pink is last year, and then I
switched over to do it in red for 2021.
And so in 2021 we're now hopefully at
least turning things back over again and
coming back down.
But even for the last data that was
released in the UK for the middle of
February there's still
significant excess mortality over any of
the previous decade.
Professor, tell me about how this deals
with the January-December problem.
So really there's nothing- you know,
because you're just going around in a
circle all I've done is I've just sort
of joined the lines up. So there's
actually a- there's a sort of
long-running spiral going around here
effectively.
And so as you can see- so 2020 here was in this
tasteful shade of magenta and when you
get to the end of the year
it just joins on to the red for 2021.
There are all sorts of subtleties you
see in the data, like there's this
dramatic
strange V-dip at the end of the year. And
actually you can see there's a couple of
others at other points in the year;
they're just basically when there were
holidays in the UK because this is when
dates- when deaths were being registered and
if the the registry offices are closed then the
the registration of deaths just gets
delayed. So you can see all sorts of
quite subtle things in the data but the
the basic issue of the fact that the
years just join on seamlessly to each
other as you just go round and round
rather than getting to the end of a line
and having to go back to the beginning again.
It's a much sort of cleaner, tidier way
of presenting the data.
Does it cause problems sort of writing
over yourself all the time though going
around in these laps?
Sort of all the overlap? - I mean in
principle you sort of lose- you you lose
the data, right, in that it all lies-
ends up lying underneath the previous
year. But in this case you know I was
really just trying to show
that that most years don't vary that
much, so the fact that data was all kind
of lying on top of each other;
you can see in some years, you know, in
January-February there's a worse flu
epidemic than another year
so that the number the numbers peak out
a little bit higher, but really there
isn't a lot of variation from year to
year and actually having the data all
lying on top of each other in these sort of
circles just underlines that point that
there isn't very much and that last year
really was very
exceptional. - Professor, if these
Nightingale graphs were invented so long ago,
by Florence Nightingale, and they're as
good as you say they are and clean and
I agree they they seem really clean and
clever, why aren't they everywhere? Why
don't I open up the Times newspaper in
the morning and see
Nightingale graphs? Why do I see the
old-fashioned, you know x and y?
I honestly don't know. I guess it's
partly because people are very used to
seeing
old-fashioned x and y graphs and so
actually you know you don't have to
explain anything to them
when you're showing them that kind of
graph. And the sort of range of data you
can plot with these are
quite limited in the sense that actually
you need something which is sort of
cyclic, which goes around like the months of the year.
So the other- I think the original
purpose they were used for, a little bit
before Florence Nightingale was using them,
was plotting wind direction and how
often the wind was coming from different
directions. And again that's something
which you can kind of
show in that way because you've got
something which goes naturally around in
a circle, in that case the compass rose.
But it has to be something which is
naturally cyclic, which this is. - Professor
you're an astronomer,
can we expect to start seeing these Nightingale
graphs in some Mike Merrifield
astronomy papers in the years ahead?
I guess so, if there's something- in fact
yeah you could well do
in that, for example we were looking at
the angles at which
galaxies are moving a little while
ago and actually if I thought about it
this would have been a really good way
to present the data.
The other places you see this a little
bit are in kind of power diagrams,
they're used for things like microphones
that show you the direction in
which the microphone has sensitivity as you go around
and you know as you point the microphone
in different directions. But of course
radio astronomers do the same thing when
they're pointing their radio dishes,
that actually they have, you know, there's like a-
where where
the radio dish when it's pointed in a
particular direction, it's most sensitive
to things that are directly in front but
of course its
power doesn't drop off immediately as
you go in different directions.
So I think it's used a little bit in
radio astronomy as well but really I've
never seen them used in astronomy.
She called them coxcomb diagrams which are-
and I had to look that up, it's actually
the name for the hat that a jester wears.
And I think it was sort of intended to
be a little bit self-deprecating that
this is just a fancy way of presenting
the data to get people's attention,
just as the jester wears this fancy hat
to get people's attention.
So she called them that; they're also
sometimes called honeycomb diagrams for
reasons I don't quite understand and I
think that might originally be
meant to be something else. - Professor as
I said you're an astronomer, why are you
sitting around plotting
Covid mortality rates in the UK?
Just because I'm a scientist and I'm
nosy, right that's the nature of science,
you're just kind of interested.
And you know I'd heard various people
make various statements about
rates of mortality and so on and I just
wanted to see for myself so I wanted to
go back straightly straight to the the
original source of the data,
which in the UK is you know the Office
for National Statistics. But they only-
they'll provide you with excel
spreadsheets with the data in
and I wanted some way of then
visualising that data, so that's why I
ended up doing it.
You can catch plenty more from Professor
Merrifield on our sister channels
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And instability is something where if
something starts to grow it will
continue to grow.
So if you start with a little
perturbation it just becomes a bigger
and bigger and bigger perturbation.
And it turns out there is this thing
called the Kelvin-Helmholtz instability
which will kick in in this case. So let
me draw the same thing again
but with just the beginnings of kind of
an instability in the middles.