DataVis Contemporary Methods

Summary

This podcast explores the purpose of data visualization beyond aesthetics, emphasizing its role in conveying insights and telling stories. It delves into a case study from Alberto Cairo's 'The Functional Art', discussing global fertility rates and their implications. The host critiques a simplistic line graph for lacking depth, advocating for clearer, more engaging visuals that respect viewers' intelligence. The episode concludes with tips for effective data visualization and a nod to Hans Rosling's Gapminder project as a prime example of beauty and insight in data presentation.

Takeaways

• 📊 **Data Visualization Purpose**: Data isn't just visualized for aesthetics; it's a tool for gaining insights, telling stories, and understanding complex issues.
• 🌍 **World Population Story**: The script discusses global fertility rates, highlighting the differences between developed and developing countries and their implications on population growth.
• 📉 **Fertility Rate Trends**: There's a debate on whether fertility rates are rising in poor regions or declining in developed ones, with significant implications for future population dynamics.
• 📈 **Data Visualization for Storytelling**: Alberto Cairo's book 'The Functional Art' is referenced to emphasize how data visualization should serve the purpose of storytelling and revealing data's message.
• 📊 **Insufficient Line Graphs**: Simple line graphs that aggregate all data can be insufficient for detailed analysis because they don't show individual patterns or trends.
• 📋 **Spreadsheet Data Limitations**: Even when data is cleaned and organized in a spreadsheet, it can be challenging to compare and identify trends without visual aids.
• 📉 **Visualizing Fertility Rates**: The script describes how visualizing data in a line graph format can make it easier to understand the fertility rate changes over time for different countries.
• 🌐 **Global Fertility Rate Overview**: By visualizing all countries' fertility rates, one can get a general sense of global trends, but it might lack the detail needed for in-depth analysis.
• 📊 **Focused Data Points**: Highlighting specific data points can help tell a more focused story and support arguments about fertility rate changes and their causes.
• 🎨 **Infographics vs. Data Visualization**: The script differentiates between infographics, which may present data without revealing relationships, and effective data visualization that tells a story and stimulates the reader's intelligence.
• 🔍 **Testing Visualizations**: Before finalizing a visualization, it's important to test it with an audience to ensure it effectively communicates the intended message.

Q & A

What is the main purpose of data visualization according to the podcast?

The main purpose of data visualization is to gain insights, tell stories, and understand complex issues, not just to create pretty pictures.

What does Alberto Cairo's book 'The Functional Art' suggest about data visualization?

Alberto Cairo's book suggests that data visualization is a functional art that should be used to tell stories and expose what data is trying to communicate.

What is the significance of the fertility rate mentioned in the podcast?

The fertility rate is significant as it is the average number of children born to women in each country and it affects the population growth, which has implications for societal and economic issues.

What does the podcast suggest about the relationship between a country's fertility rate and its population growth?

The podcast suggests that if a country's fertility rate is below 2.1, which is the replacement rate, the population will shrink over time. Conversely, if it's higher, the population will be predominantly younger, potentially leading to higher crime and violence rates.

What is the author's claim in the article by Matt Ridley mentioned in the podcast?

Matt Ridley's article claims that fertility rates in rich countries are slightly increasing, while in poor countries they are slightly decreasing, suggesting that fertility rates will converge around 2.1 in a few decades, stabilizing the world population at 9 billion.

Why is the simple line graph insufficient to support the author's claim according to the podcast?

The simple line graph is insufficient because it aggregates all data from all countries into one line, making it impossible to see multiple patterns, such as where fertility rates are increasing or decreasing and their impact on countries and the world.

What did Alberto Cairo do to better understand the data on fertility rates?

Alberto Cairo went to the United Nations website, downloaded the data on total fertility rates, and cleaned it in a spreadsheet to better understand and visualize the data.

What is the challenge with identifying the years when the fertility rates of Spain and Sweden differed from the spreadsheet snapshot?

The challenge is that it's difficult to identify the years when the fertility rates differed because the data is not visually distinct, requiring memorization and comparison of numerous figures across the chart.

How does creating a simple line graph from the spreadsheet data help in understanding the story of fertility rates?

Creating a simple line graph from the spreadsheet data helps in understanding by providing a visual tool that makes the data more accessible, allowing for easy identification of trends and differences between countries like Spain and Sweden.

What is the key requirement of visualization mentioned in the podcast?

The key requirement of visualization mentioned in the podcast is that readers should be given just enough information to follow a presented argument and use their own intelligence to interpret or extract meaning.

What is the difference between infographics and data visualization as discussed in the podcast?

The difference is that infographics may present one-dimensional numbers without revealing relationships or patterns, whereas data visualization aims to be a functional art that respects and stimulates the intelligence and curiosity of readers, allowing them to tell multiple stories from the presentation.

What are some tips for creating effective data visualizations as mentioned in the podcast?

The tips include focusing on clarity, highlighting key data, simplifying complex data, maintaining accuracy, engaging the viewer, and testing for understanding before finalizing the visualization.

Why is Hans Rosling's Gapminder project considered an excellent demonstration of data visualization?

Hans Rosling's Gapminder project is considered excellent because it uses interactive bubble charts to show changes in global health and economics over time, making complex data accessible for wide audiences and demonstrating how data visualization can be both beautiful and insightful.

Outlines

00:00

📊 The Purpose of Data Visualization

This paragraph introduces the concept of data visualization, emphasizing its role beyond aesthetics to include insight generation, storytelling, and understanding complex issues. The speaker uses Alberto Cairo's book 'The Functional Art' to illustrate the idea that data visualization is an art form meant to convey data's stories effectively. The case study focuses on global fertility rates, addressing misconceptions about population growth in poor regions versus aging populations in developed countries. The discussion highlights the importance of distinguishing between countries' fertility rates to understand their impact on the world's population dynamics.

05:01

📈 Analyzing Fertility Rates with Data Visualization

The speaker delves into a case study about fertility rates, using Matt Ridley's article as a reference point. Ridley's article challenges apocalyptic views by showing trends of fertility rates converging around the replacement rate of 2.1. However, the speaker critiques the simplicity of a line graph used in the article, arguing it fails to reveal detailed patterns across different countries. Alberto Cairo's approach is then highlighted, where he cleans and organizes UN data on total fertility rates, demonstrating the difficulty of discerning patterns from raw data in a spreadsheet. The paragraph concludes with the idea that data visualization should simplify complex data into accessible, meaningful forms to aid understanding.

10:02

📉 The Impact of Data Presentation on Storytelling

This section discusses the importance of data presentation in storytelling, using a line graph of Spain and Sweden's fertility rates as an example. The speaker explains how a well-designed chart can reveal the story of population changes over time, making it easier to understand complex data. The paragraph also touches on the limitations of presenting all data at once, suggesting that focusing on key data points can better support an argument. The speaker advocates for visualizations that respect and stimulate the intelligence of the audience, allowing them to interpret and extract meaning from the presented information.

15:02

🌐 The Evolution of Data Visualization and Infographics

The speaker explores the evolution of data visualization and the rise of infographics, noting the difference between the two. They critique infographics for often presenting one-dimensional numbers without revealing relationships or patterns, which can misrepresent the complexity of data. The paragraph emphasizes the need for visualizations that are clear, simplified, accurate, and engaging. The speaker provides tips for creating effective visualizations, including focusing on clarity, simplifying complex data, maintaining accuracy, engaging the viewer, and testing for understanding. The paragraph concludes with a reference to Hans Rosling's Gapminder project as an example of data visualization that is both beautiful and insightful.

🎓 Developing a Critical Outlook in Data Visualization

In the final paragraph, the speaker reiterates the goal of developing a critical perspective in data visualization. The aim is to produce work that is not only visually appealing but also serves as 'functional art,' respecting and stimulating the intelligence of the audience. The speaker encourages the development of visualizations that are beautiful, engaging, and convey meaningful stories. The paragraph ends with a reminder of the importance of testing visualizations for effectiveness before finalizing them, ensuring they convey the intended message accurately.

Keywords

💡Data Visualization

Data Visualization refers to the graphical representation of information and data. It is a crucial concept in the script as it is the primary method discussed for gaining insights and telling stories with data. The video emphasizes the importance of visualization beyond creating aesthetically pleasing images, highlighting its role in understanding complex issues and supporting arguments with evidence.

💡Insights

Insights are the deeper understanding or knowledge gained from data analysis. In the context of the video, insights are the revelations or discoveries made possible through effective data visualization. They are what allow viewers to comprehend the underlying messages and trends in the data, such as shifts in fertility rates across different countries.

💡Fertility Rate

The fertility rate is the average number of children born per woman in a country. It is a central concept in the video, used to illustrate the application of data visualization in understanding demographic trends. The script discusses how changes in fertility rates can impact a country's population dynamics and is a key metric in the case study presented.

💡Replacement Rate

The replacement rate, specifically 2.1 children per woman, is the level at which a population can sustain itself without growth or decline. The script uses this concept to discuss the implications of fertility rates falling below this threshold, leading to population shrinkage, which is a critical point in the analysis of demographic trends.

💡Population Dynamics

Population dynamics refers to the changes in a population's size and structure over time. The video script uses this concept to frame the discussion on fertility rates, aging populations, and the global implications of these demographic shifts. It is integral to understanding the broader context of the data being visualized.

💡Case Study

A case study is an in-depth analysis of a particular subject or situation. In the script, the case study of world population fertility rates is used to demonstrate how data visualization can be applied to real-world issues. It serves as a practical example of the principles discussed throughout the video.

💡Alberto Cairo

Alberto Cairo is mentioned in the script as the author of 'The Functional Art,' a book that emphasizes the storytelling aspect of data visualization. His work is cited as an influence on the approach to data visualization discussed in the video, advocating for its use as a means to communicate complex ideas effectively.

💡Gapminder Project

The Gapminder Project, created by Hans Rosling, is highlighted in the script as an example of data visualization that is both beautiful and insightful. It uses interactive bubble charts to show changes in global health and economics over time, demonstrating how complex data can be made accessible to a wide audience.

💡Information Graphics

Information Graphics are visual representations used to present information in a clear and engaging manner. The script discusses the rise of infographics as a response to the need for making complex data more understandable. However, it also contrasts them with data visualizations that reveal relationships and patterns, suggesting a preference for the latter in terms of depth and storytelling.

💡Functional Art

Functional Art, as described by Alberto Cairo, is the concept of creating visual representations that are not only aesthetically pleasing but also serve a purpose, such as conveying information and telling stories. The script uses this term to encapsulate the goal of effective data visualization, which is to respect and stimulate the intelligence and curiosity of the viewers.

💡Engagement

Engagement in the context of the video refers to the viewer's active involvement and interest in the data visualization. The script emphasizes the importance of creating visuals that are not just informative but also engaging, ensuring that the audience remains interested and motivated to interpret the data presented.

Highlights

The purpose of data visualization is to gain insights, tell stories, and understand complex issues.

Alberto Cairo's book 'The Functional Art' emphasizes the storytelling aspect of data visualization.

Fertility rates are a critical global issue, with implications for population growth and societal dynamics.

Contrary to common beliefs, fertility rates in rich countries are slightly increasing while decreasing in poor countries.

A line graph can be insufficient to support claims due to aggregation of data from multiple countries.

Data from the United Nations on total fertility rates can be overwhelming and hard to interpret in spreadsheet form.

Visualizing data in a line graph format makes it easier to identify trends and differences between countries.

A visual tool like a line graph can save the viewer the effort of memorizing and comparing numbers.

Visualizing all countries' fertility rates can provide a general snapshot but may lack specific insights.

Highlighting key data points can help tell a specific story and support an argument.

An increase in per capita income and child survival rates are factors that influence fertility rates.

Visualizations should provide enough information for readers to follow an argument and interpret the data.

Infographics should not just present data but reveal relationships and patterns to be meaningful.

Hans Rosling's Gapminder project demonstrates how data visualization can be both beautiful and insightful.

Data visualization should be tested for understanding to ensure it conveys the intended message.

The goal is to create visualizations that are functional art, engaging, and respectful of the viewer's intelligence.

Tips for effective data visualization include clarity, simplification, accuracy, and viewer engagement.

Transcripts

welcome back um today's pod is going to

be about contemporary methods of data

visel a really more than that it's

really going to be about why we

visualize data um we don't just

visualize data to make pretty pictures

um we visualized to gain insights tell

stories and understand some complex

issues now I'm going to sort of answer

this question by talking through a bit

of a case study and this case study

comes from Alberto Cairo and his uh book

on the functional art which really says

it all DAV is is a functional art into

in um telling us stories and doing

things to kind

of expose what data is trying to tell

us okay so to start with let's just tell

the story of the world's population um

particular in particular the fertility

rate um which is the average number of

children born to woman in each country

um some claim that the

fertility rate is rising in poor regions

which is and it's going to be an issue

if the Earth has to support 78 n billion

people um others focus on Aging

populations in developed countries where

fertility rates uh below 2.1 which is

the replacement rate um have been

dropping um and as we know on maybe if

you don't know if a countries

replacement rate is below 2.1 the

population will shrink over time and if

it's much higher obviously the PO

population will will be predominantly

younger so if it's higher the

population's younger which leads to a

higher violence and crime

numbers is a stat okay so let's keep

going with this story in um the article

by Matt Ridley here um he contradicted

those apoc apocalyptic views by

discussing two Trends fertility in rich

countries is slightly increasing while

in poor countries it's slightly

decreasing um for instance in Brazil's

fertility rate it dropped from over six

children per woman in the 1950s to two

in

2010 um due to these Trends the author

suggested that fertility rates would

converge around 2.1 in a few decades

which stabilizes world population at 9

billion the discussion was supported by

a simple line graph which we can see

there showing world population

increases you know compared to previous

years we can see there like 1955 down to

2005 that it's actually

decreasing down there so while it's

simple and clear this graphic or you

know line graph is insufficient to

support the author's claim because it

Aggregates all of the data obviously

into of all countries into one simple

line graph so we can't see multiple

patterns in it so whose fertility is

decreasing where is it actually

increasing and how will it impact um

countries and the world you know that's

all missing from this one chart so Cairo

went to the United Nations website and

downloaded the current un un data on

totally total fertility rate

and clean it cleaned it in a spreadsheet

which is similar to the process that

we're going through this semester with

your my time and our time project um so

let's just have a little look at that we

can see there's the data that he got

from the

UN it's a snapshot of the spreadsheet

anyway uh can you I know that you can't

probably see it but it' be almost

impossible to identify the years when

the fertility rates of say Spain and

Sweden they're all the countries down

that ACC there the fertility rates of

Spain and Sweden differed um it's

challenging because you must memorize

and compare the various numerous figures

across the chart so we it would be very

difficult I mean we could do it but it's

not a very easy way easy read by any any

means okay so CYO then and this is the

same process cleaned up the data in a

spreadsheet program you know we're using

Excel this semester that looks like Exel

to me um but you can still see that

extracting data even though he can

highlight the two countries he wants

Brazil and

Sweden in that light blue there you can

still it's quite rough or tough to kind

of still make any meaning out of those

numbers it's probably even harder that

you can't even see this on this podcast

but let bear with me we'll we'll get

there okay so here's the extracted data

or Snapshot from the spreadsheet looking

at

this could you tell me the difference in

what years the difference between

fertility rates of Spain and Sweden grew

and which years it dropped probably

could if you looked at them long enough

you know you could go oh yeah the task

seems quite simple but it forces you to

do something very difficult you have to

look up a number memorize it compare it

to the next one across memorize that and

then compare them across the two lines

it's it's starting to get very very

difficult to do it's just too hard we

probably wouldn't even bother so what if

we designed a simple chart with the data

that's actually in this spreadsheet in

the middle

there let's see what we've got now is a

visual tool that actually helps us

answer the question it's a simple line

graph makes it more accessible Spain's

fertility rate St starts higher than

Sweden's we can see that very easily

there um but but

um it starts dropping around where does

it start dropping so Spain starts

dropping from the 70 you know 1970

onwards it starts in this Decline and it

passes Sweden's you know increase in

1982 or something like that um so it's

starting to tell a more complex story I

can read this chart and tell you the

story of it from just looking at that

you can do this too um but by giving us

the the num by giving these numbers the

proper shape and in this case it's this

line line drawing

here um it's saving us the energy of

trying to memorize those numbers until

we see a visual form of data or numbers

it's very difficult for us it's very

difficult to tell any story whatsoever

um no one would be bothered to

understand that table that we saw just a

moment ago this one here um but putting

it into this simple line graph we can

easily understand that hey Spain's

population started or replacement rate

fertility rate started at a certain

number dropped in the

1970s and it's now coming up a little

bit but still stabilizing below that 2.1

replacement rate it's at 1.43 we can see

speden has started at the replacement

rate 2.21 dropped a bit come up a bit

dropped a bit and is stabilizing or on

an upward trend of 1.87 at the moment

so you can see I've just made up the

story from reading the graph okay so now

what would happen if we if we wanted to

visualize all of the data that was just

Sweden and um Brazil but if we looked

into seeing all of the

countries we could if we had the legend

of the colors sort of tell the story of

all of the countries there and to look

at what's happening to um fertility rate

generally we can can tell one overall

snapshot picty idea we can kind of go oh

okay the average number of children per

woman generally speaking looks like it's

going down like they're all flowing down

or some of them actually going up and we

could pick out the ones that are going

up or swimming against the tide there we

can see I can't even see what country

they are but we could say oh there's an

exception of 1 two three or four but

most they're all going down in the end

so we can tell a story from this chart

as well

but it it's an Overkill it it tells a

general picture but it's not really

telling us much about going on it's a

great snapshot let's move on to the next

one where we can see that all of the

other data has been grayed out and what

remains here are important data points

for the storytelling here uh we can see

that only the rich countries here have

been highlighted and a couple of poor

countries um what what can we see from

this um the author argued that a sudden

drop in a country's fertility rate is

usually precipitated by several factors

an increase in the average per capita

income and you can see that mapped

across the fertility rate there and the

per capita

um and the fact that more children

survive the first years of their life

and spending more time in school are

correlated to better family planning so

this graphing allows us to see evidence

supporting you know discussion of the

evolution of fertility um we can see if

a hypothesis has any valid validity and

I what I'm this is a longwinded way of

me saying to you that this is one of the

key requirements of visualization that

readers should be given just enough

information to enable them to follow a

presented

argument and use their own intelligence

to do an interpretation or extract

meaning

so rather than the previous example

where we've got everything I think

that's great to show a trend of all the

countries decreasing but to tell the

specific story of why we've picked out

just the the key data points that we

want to talk about in the other

developed countries which actually

mainly increasing their fertility rate

which was contrary to what was

hypothesized at the start of this whole

story

here okay so looking back across the

examples that we've had this week and

last

week one of the key things I think they

have in common is that they require an

active and an Engaged reader from the

more contemporary examples we just

looked at um and the one on the right

there um to the ones we looked at last

week we can see that there is a lot of

information being

presented

um but we can easily start to tell

multiple stories from the visualizations

that have been done on the

data um which is why there's a

ubiquitous or you know there's have been

a rise of information Graphics um and

visualizations to make meaning in in our

contemporary World um but I wanted to

talk a little bit about infographics and

their difference to these visualizations

or data Vis that we see here um and it's

my way going on a little bit of a r so

it brings me to this phenomenon that we

can see at the moment this idea of uh

infographics rather than

infographics um the complaint here by

Phil geord who made this particular

infographic is that even though it looks

very legible it looks like we're being

presented with lots of information and

looks ah yes there's a lot of data in

there um the complaint is that there's a

presentation of one-dimensional numbers

there's no relationships or patterns

revealed um and it does a disservice to

this broader field of information Design

Within which data Vis and infographics

kind of fit um you can see there the

number of infographics I've seen this

week in a big number well it's supposed

to be important the percentage of those

which landed but no simple Graphics 100%

the same figure as a paraph which makes

this look more complicated which I would

agree with 100% countries are with in

when I saw these infographics on my

computer screen yes in the United

Kingdom um all very nicely rendered uh

infographics but fairly meaningless when

you want to tell a story from them we

could have just said I'm in

England um Etc so what we're aiming for

you to develop in this UN in this

subject this semester is a critical

perspective that help helps you to

create work that qualifies as a

functional art Alberto Ciro's functional

art that means producing visuals that

aren't just beautiful and engaging and

I'd say that these are quite lovely

they're quite nicely rendered vectors um

but that it also respects and stimulates

the intelligence and curiosity of your

readers it tells them something more it

allows them to tell multiple stories

from your

presentation so let's go through a

couple of tips to help do it well I

would say all of your infographics and

your data vises here need to focus on

Clarity they need to highlight key data

so use a visual hierarchy to emphasize

the most important information and

trends that number three they need to

simplify complex data that means you

need to break down complex data into

more understandable visual forms

not just show it all number four we need

to maintain accuracy um make sure that

it's represented accurately and honestly

um number five Engage The viewer you

you'll be creating clear visuals that

are not just informative um but also

engaging to keep the in the reader

interested in actually understanding

what's going on in your

visualization and number six it's not

talk spoken about a lot but test for

understanding before you're going to

finalize your final visualization you

need to test it with quite a few people

to see what they think your

visualization is saying making sure that

it conveys the right information

effectively and that it does what you

think it's actually

doing okay so I'm going to make this a

short pod this week because I'm going to

leave you with um another very important

example to look at Hans rosling's

gapminder project which I believe is an

excellent de demonstration of how data

viz can be both beautiful and insightful

and it's I guess it's now becoming a bit

more of a historical example but it was

quite radical when he started it he used

interactive bubble charts um to show

changes in global health and economics

over time and he he I think he really

foregrounded um how complex data could

become accessible for very general and

wide audiences so rather than listening

me go on about it I'm going to get you

to watch that pod this week as the end

of this of this session so to reiterate

before I leave you um our goal in this

uh unit is to develop a critical Outlook

that helps you enable that helps you or

enables you to produce work that

qualifies as functional art that it's

beautiful it's engaging and it honors

the intelligence of your readers so

that's it from me thank you and I'll see

you in class