-: All right, last but not least for this week,
I want to look at some examples of data visualization
and just think about them all in the context
of what we've been talking about all week,
chart selection, what to put in your charts, et cetera,
starting off with this.
We looked at this a few modules back
when we were talking about maps,
the idea that we can see the size of this iceberg
that broke away from Antarctica
in relation to other geographic things that we recognize,
Puerto Rico, Jamaica, et cetera.
This is cool.
But one thing I didn't mention, this is the right time
to talk about it, is that while I understand Puerto Rico
and how big it is compared to Florida,
the United States, whatever, sort of,
it's a little bit hard to make precise comparisons
when you're talking about weird shapes, right?
What's the percentage difference between this iceberg
and Bali, for instance?
Good luck, you can't do it.
That's why this other approach, which we also looked at,
is a better visualization for precise comparison.
We're really good at comparing the areas of rectangles.
Remember, we're bad at it with circles,
but we're pretty good at it with rectangles.
So I'm gonna do pretty good.
Clearly the closest comparison looks like it's Cape Verde
or maybe, I can't see it, I can't read the text,
it's too small, but one of the other ones we can see here.
Long story short, it's much easier to make
that precise comparison with this visual,
but it's nice to have that geographic comparison
to relate it to things that I kind of know about.
So they're both good.
That's why using both of them in that data story
was effective, it was a smart idea.
Another example of a data story
that has great data visualization.
We're gonna back into
the "Atlas of Sustainable Development Goals".
We looked at this a while back
when we were talking about story telling.
Here we're looking at the global health
amid a pandemic story, and again, good structure,
all this stuff we talked about last time,
but specifically looking at the visualizations.
This is about the pandemic, and the first part
about the pandemic is how local health risks
can become global health risks.
This is an example.
I mentioned this briefly during module four about mapping,
but you shouldn't make a map
just because you have geographic information.
Just because we have geographic information
about where zoonosis events occur,
when disease jumps from animals to humans,
doesn't mean we should put it on a map.
But this data story right now is about the idea
that yes, it's localized.
Zoonosis happens more here than it happens over here.
Therefore, if the point of the data story might be
maybe we should put more monitoring stations here
and more people here to sort of keep track of this stuff,
and we don't need as many of them over here.
And the idea that if zoonosis happens here,
we can expect the spread to occur in areas
that have connections with this area, et cetera, et cetera.
But of course it's all global
because people get on airplanes,
we now know very, very, very well, right?
Anyways, long story short, the map is a good visualization
for this part of this story.
As the story continues, we're looking at access
to health care as part of this global pandemic story,
and looking at it segmented by income level.
Low income countries have a very low median
nurses and physicians per 1,000 people rate.
Ah, another rate, right?
And this is a nice little swarm plot.
So not only do I get the median, but remember we talked
about show people the data if you have it.
Low income countries have a very low median,
fairly clustered at the low end,
but one interesting outlier up here.
High income countries have a very large spread
of access to health care.
It's not very monolithic, much higher median value
than low income countries, but the poorest,
or of the high income countries,
the one with the lowest access to health care,
it's as low as the number two in a low income country,
or maybe a little bit higher.
Long story short, a lot of nuance to be found here,
and I have the ability to find individual countries
if I want to look into them.
But think about this visualization.
It's a distribution diagram, yes.
I have access to all the data points, yes.
I can see the clustering, I can see the outliers,
all that stuff.
Good chart type to segment, to allow me to see
the difference between low income
and high income, et cetera.
Now, later on in the data story, right after that,
they're doing something similar.
They're showing me different data.
This is about the coverage
of essential health service by income.
Once again, though, they're segmenting
by low income, lower, middle, et cetera.
So of course they're gonna show me the same chart type.
This brings up an important point.
You should absolutely keep things fresh
and interesting for your audiences.
If you have a slide deck with 800 bar charts,
I'm gonna fall asleep, yes.
But if you have 800 slides where the task
I'm supposed to perform is exactly the same,
and it's all one-dimensional data,
therefore, and if you've decided on slide one
the bar chart is the right chart for that task,
you better repeat those charts 800 times.
So yeah, I might fall asleep,
but maybe that's because it's a boring data story,
not because you're repeating the same chart.
You must repeat the same chart
if you're asking me to do the same thing.
Otherwise I'm gonna be confused.
If I saw lower income, middle income, et cetera countries
and you were showing the exact same type of thing,
but this one was a box and whiskers plot
and the other one was a swarm plot,
I might be like, why are you swapping it out?
Why are you switching it up?
Or if this was bars or something, especially,
what are you doing to me?
You're confusing me, right?
Make the chart match the task.
Once you've assigned a chart to a task, repeat it
so I'm not asking essentially, I'm gonna be distracted
and thinking about, why are you switching charts on me?
So repetition as appropriate.
Later on in this chart, they have a connected scatterplot,
one of those master class charts
that we were just looking at.
Again, a very effective chart
when you have time to explain it to your audience.
And last but not least, actually two more.
I love a slope graph.
Here we have 20 years of data, 1990 to 2019.
It's not a line chart going up and down and up and down
and up and down showing me all the volatility.
The point of this part of the data story
is simply we started here, we ended here.
So a single line with two data points,
and the slope of the line showing that change,
is a very effective visualization
when you don't want to show, emphasize,
talk about all the ups and downs in between.
Basic and wise, right?
And by the way, we can see very clearly,
based on the slope of the line,
we're very good at reading slope of the line.
Niger has a very different value than Zimbabwe,
and by the way, sub-Saharan Africa
looks very different than Europe and Central Asia.
So slope graphs are great visualizations.
And now, last but not least, this.
Look at this.
We have thousands of data points here
very compressed in a tight space using matrix heat maps.
We talked about this a couple, a few videos ago.
These are really powerful.
I have every single individual data point.
If I want to see Malaysia in 2012 or whatever this is,
95%, boom, there it is.
At the same time, big picture story not lost.
This is looking at immunization rates,
again, segmented by income level.
Low income countries have much lower immunization rates
than high income countries.
I can see that by the color.
And over time though, like in the high income countries,
it started out even lower, and it got better.
In some outlier countries,
they have pretty darn good immunization rates.
High incomes countries were lower back in the day,
much higher now, but consistently over time
doing pretty well.
There's clearly a pattern.
The story is there.
And yet I have access to individual data points.
By the way, a little secret trick.
If you're creating these, which you can do right now,
go in Excel, go to your table, use conditional formatting,
use coloring.
Boom, now you have a highlight table, a matrix heat map.
Use more creative colors than red and green.
We talked about that already.
And on top of that, nice little trick.
First of all, hide the values,
because those are visually distracting.
That's an easy thing to do in Excel.
You can Google it.
But also change the cell and row,
the column and row separators.
Instead of a gray or a black line, turn it white.
That's what makes this look so nice,
because it looks like space in between the individual cells,
these white separators, versus putting the data into jail.
It's a simple little trick.
That's assuming your slide background is white.
Whatever your slide background color is,
make the separators that color,
and then it makes it look like they're spaced apart
rather than segmented in a more harsh way.
Nice little design trick.
All right, another one.
I love this one.
From Reuters again.
I love Reuters, they do great work.
Stopping the spread, reaching herd immunity
through vaccination.
This is a whole data story all about herd immunity
and vaccination and blah, blah, blah.
But these simulations are super cool.
This is meant to explain how herd immunity works,
what it is.
And so each square is a person.
Orange means they're infected,
not orange means not infected.
Okay, and so what we can see is that
when no one is vaccinated, almost everybody gets infected.
20%, 40%, still the vast majority get infected.
But once you hit 60%, assuming efficacy rates
of the vaccine and infection rates.
Obviously this didn't work out quite this way.
But once you hit whatever the magic number is
based on those things, this is what happens.
This shows you via live simulation,
you can see every time this runs,
the pattern looks visually different.
This shows you how herd immunity works.
You should run out of people to infect very quickly
when you have a certain vaccination rate.
Sometimes more, sometimes less.
All right, great data story, really nicely visualized
using not a typical chart, but using a very effective
visual mechanism.
Last but not least, one of my favorite
data visualizations of all time.
This is showing the murder rate in Honduras.
You're supposed to take the magazine out,
tack it up on a wall and throw a dart at it,
and your odds of being murdered,
if you were a man in Honduras in 2012
were the same as the odds of hitting this rectangle,
compared to the United States, compared to Singapore.
So I can read the numbers, right?
One in 599, one in 13,000, one in 256,000.
There's clearly much lower odds of happening than this,
I get it, but humans are very bad at probabilities.
So making it a visual experience
and even a tactile experience is very powerful.
I could hit this square by accident very easily.
I could literally never, ever, ever, ever,
ever hit this square.
It aint gonna happen.
You are not gonna be murdered in Singapore.
So extremely effective data story telling.
Two paragraphs of text.
Very simple, but very effective data visualization
with a few squares.
These squares would need to be sized by area,
not width or height, double the area,
not double the height if it's double the number.
But I think we could all execute on this
in PowerPoint in five minutes, right?
So this is an example of don't be hung up on your charts.
You might be tempted to show this as a bar chart.
Maybe there's another way.
You can come up with better ideas.