- Oh, hi.
I didn't see you there (laughs sarcastically).
How long is a waiting line
consisting of 24 king cobras stretched end-to-end?
How heavy would a large school bus be
if it's only filled with clones of Nicholas Cage?
Sometimes it's pretty hard to get a sense of numbers
or imagine what they mean.
But thankfully, there are experts in the world
whose job is to make those numbers tangible.
One of these people was Florence Nightingale,
a dedicated nurse, a pioneer of modern nursing,
and a statistician who made lasting changes
to Britain's healthcare system.
Another is Mona Chalabi, a data journalist who's dedicated
to visualizing everything from how hairy you are,
to vaccination rates,
to healthcare inequalities based on race and ethnicity.
Let's start with Florence's story.
Time machine, 1854 please,
during the Crimean War in Scutari,
what is now Uskudar, a district of modern day Istanbul.
(gentle music)
You might know her as the lady with the lamp,
that nurse who saves lives
during the Crimean War of the 1850s.
The hospital guru Florence Nightingale,
named after the Italian city of her birth, Florence.
She was a strong-willed, kind of awkward,
and also an educated lady
who at one point cared for a tiny pet owl named Athena.
But it's also important to recognize
that Florence was a strong supporter
of the British colonial rule that caused the death
and subjugation of Indigenous people all around the world.
Through her writing, we know that she believed
that imposing the British culture and norms on other people
to be very necessary
for the expansion of the British empire.
The alternative, and I'm quoting here,
"Would be simply preserving their barbarism
for the sake of preserving their lives."
So, yeah, Florence and many other iconic science figures
throughout history have problematic stories.
Many times, their contributions to their societies
came at horrible costs to other people,
impacting their lives even today.
So as we talk about these figures,
we have to be aware of the complicated legacies
that these people have.
Okay, back to the Crimean War when Florence was dispatched
to a hospital in Scutari.
Wounded British soldiers ended up at this hospital
where Florence was in charge of supervising 38 nurses.
She was going around trying to take care of the wounded
as best as she could.
Getting them food, clean utensils, and towels,
making the hospitals more resistant to the cold weather,
and providing cleaner water.
But many of the soldiers there were dying
from illnesses unrelated to their actual battle wounds.
Things like typhus, typhoid, and cholera.
Why, you ask?
"Look at this place. It's filthy.
I mean, look at this.
It's a rat.
It's... Ew, ew, ew, ew.
It's licking a bandage now.
Why?
Hey. Hey, bring it back.
And there it goes.
The rat stole the bandage.
Wow. Yeah, okay.
This place is legit filthy."
Eventually, the British government sent out supplies
and people to help clean up the hospitals.
And after that, the death rate super plummeted.
Okay, fast forward some years, and the war is over.
Florence is back in England and everyone is like,
"Oh my God, it's Florence.
Fangirl, fangirl.
Woo.
You're so brave.
You're so famous.
We love you Flo-flo."
But Florence is not about the celebrity glam life.
You see, Florence was not only a capable nurse,
but as I said before, she was also a great statistician.
And she didn't forget the lesson she learned
during the Crimean War.
So when Florence got back to England,
she was very adamant to prove her point using stats.
Stats, stats, stats, stats.
So with some help, she started collecting numbers
from military hospitals near the battleground,
and from military hospitals across Britain.
"So, how many people died here?
And was that from the battle wounds
or was it like um... The filth literally killed them?
Aha. Aha."
And based on those numbers, she confirmed her hunch.
"Hey, y'all, we need to make our hospitals cleaner.
Soldiers in the hospitals are dying from all the dirt
and poop and sewers infecting them
rather than the wounds themselves."
She prepared a whole report to prove her point.
But she knew it couldn't be a regular report.
You know, a big, boring one filled with numbers
that none of the ministers
or decision-makers are going to understand.
Who's going to read that?
So she took refuge in data visualization.
She gathered up all her data
and put them in a fancy-looking chart
that turned all the numbers
into an easy to understand image.
Each wedge corresponds to a different month from April 1854
to March 1856.
See the blue parts?
Those are all the deaths from diseases, not war wounds.
The pink sections are war-related deaths
from injuries suffered on the battlefield.
And the black bits are other causes of death.
Florence's use of data visualization
to show the importance of sanitation in hospitals
was a big deal,
like when I decided to never again get bangs.
With her visualization, it was pretty obvious
what was really causing all the deaths.
Soldiers were 10 times more likely to die
from diseases spread through filth
than from battle wounds themselves.
So, Florence proposed that parliament do something
to improve overall public health.
You see, hygiene was not just a problem in hospitals,
but also a problem in the streets
and homes of people in England.
Poop, poop everywhere.
And all the dirt was getting people really sick.
Eventually, parliament passed the Public Health Act of 1875
which forced landlords to connect their pads
to main sewage lines.
Life expectancy in England went up.
So, hurray for data and no more poop on the street.
This is one of the first moments that we know of
where data visualization
successfully influenced public policy.
Data visualization can help us understand numbers
and figures better.
And that's not just limited to pie charts, bar charts,
or the chart with lots of dots on it.
Yep. That one.
Data visualization can get fun and creative too.
Like the work of Mona Chalabi,
the superstar data journalist.
- I grew up in East London, where I was raised by my parents
who were both the Iraqi immigrants.
When I was much younger, I really, really loved maths.
I found it really kind of exciting and satisfying.
But that feeling of satisfaction
kind of really quickly started to slip away
when I went to college.
I had grown up in an area of East London
that was incredibly multicultural.
All kinds of different people lived around us.
All kinds of different cultural backgrounds.
And then I kind of like landed in this college campus
that was overwhelmingly white.
- Sounds familiar.
I remember how it felt sometimes
to be the only brown person in the room.
It's isolating socially,
and you start questioning your own knowledge,
experiences, and expertise.
And on top of all of that, Mona had to deal with a topic
that just felt very disconnected.
- So initially,
I had tried to study economics and philosophy.
And the further that you got into economics,
the more that these formula were just presented to you
as abstract.
I have a really clear memory of kind of sitting
in this university auditorium and looking at this textbook
that had this formula in it.
And I found it really difficult to grasp those concepts
and also to care about them.
When they weren't attached to the real world,
I just didn't really care anymore.
- Eventually, Mona dropped out of the undergrad program
to go to the land of croissant and camembert, France.
Not to study baguetteology, which obviously I would,
but to do a Masters in International Relations.
During her degree, Mona started working on a project
that involved lots of data collection.
- I really enjoyed making the charts.
I would kind of obsess for hours over things like the font
and the placement and the colors that I'd use.
They stay in people's memories better
and they have greater impact.
My role was to take all of this statistical data
about what Iraqis needed,
specifically Iraqis who had been displaced
as a result of the conflict, and then create figures
that could be taken to international donors to say,
"Hey, this is what these Iraqis need.
Can you supply those goods, those services?"
So, I recall one particular conference
where there was an Iraqi that was there
who was responsible for actually conducting
the questionnaires themselves.
And he said to us,
"You gave us surveys to ask the Iraqis:
do you need food or blankets?
But what we actually need is electricity generators.
And that just wasn't on the survey."
And it was just so transparent
how data can create self-fulfilling prophecies,
this idea of a feedback loop that you see the world one day
and then that reinforces that vision of the world
and then recreates the world as it is.
It became so clear to me
the importance of disseminating information
as widely as possible,
but also especially to get the information
in front of people who you're claiming to represent.
At its base, data journalism is about representation, right?
A chart is claiming to represent a constituency of people.
And very often those people didn't ask necessarily
to be represented in that way.
So the very least you can do is ask those people,
"Did we get it right?"
- So, obviously the natural path for her
after this experience was data journalism.
Communicating data to more and more people.
But yet again, like in college,
Mona found herself surrounded by people
and ideas that felt just not at all like her.
- I first moved to the US
to work for a data journalism site.
I was really excited
to be working with other data journalists,
but we didn't have the same approach to journalism.
Their goal was to communicate
to people who described themselves as geeks and nerds.
It was an almost entirely white male organization.
I was the only person of color.
I was the only immigrant writing on the staff.
It was just awful.
Like, I was repeatedly made to feel like an outsider.
And so I just quickly became quite depressed really.
I recall one situation in particular really vividly.
We were sitting in a conference room
and we were talking about how to cover
the UK general election.
And I recall one of the editors saying,
"Who could we have to cover the UK election?"
And I'm sitting there in this room
with 25 people that I've worked with for a year.
I'm the only British person in the room.
They know that I covered politics in the UK
before coming to join them.
And there was a pause. And then one of the editors was like,
"Of course, I know who we could ask."
And he mentioned the name of a guy who is American
who was still in college.
And up until that point,
the only thing he had written for us were a couple of pieces
about the joys of Scrabble.
And I was just like, "Okay, like, you don't see me at all.
There's no point me saying to you,
I'm qualified to do this."
- Mona's job was to make information visible,
but she herself felt invisible.
Even the data she was working with
did not really represent her.
It was just so disconnecting.
Eventually though, Mona found a platform and a style
that really worked for her.
And that made her feel seen again.
- I started to draw these data visualizations
that I would put up on Instagram.
Even though it sounds superficial
to have the validation of strangers,
seeing your work and saying,
A, this makes sense to me,
and B, this is useful information for me to have,
really solidified my identity as a journalist
and that I could do this.
And so I just leaned into doing more of that.
I started to create more
and more of these hand-drawn data visualizations.
And, yeah, kind of haven't stopped since.
- Mona has visualized many datasets.
From topics related to animal extinction,
to police brutality, and even the cost of living or dying.
Mona downloads data from different databases
and then gets to work.
She has a pretty unique style.
Imperfect lines, whole numbers, none of those percentages
estimated to the second decimal point.
What does that even mean?
Her style is just very human.
- When most people look at a computer-generated graphic,
it presents the world with the sharp edges
and this degree of precision
that feels like it's not quite true to our lived experience.
I think I'm communicating
that uncertainty is inherently a shaky line.
And that's the way that we know the world.
For example, somewhere between 5 and 10% of women doing X.
It's not down to decimal places.
So, I really wanted to communicate
the uncertainty with which we understand the world around us
as a way to build trust.
- Data visualization is not just arranging numbers
on a page or a screen.
It's about telling stories about the data.
Breathing life into the numbers.
And it's important for people to feel represented
and seen in those stories.
Like what Mona is trying to do.
Also, data visualization can serve different purposes.
Whether it's to improve conditions in hospitals
through policy, or bringing attention to social
or environmental issues.
We just have to be creative, caring,
and find a space for all of us in the numbers.
A waiting line consisting of 24 king cobras
stretched end-to-end would be only a few feet longer
than the Statue of Liberty is tall.
A large school bus filled with Nicholas Cage clones
would weigh as much as two T-Rexes.