DataVis Contemporary Methods
Summary
TLDRThis 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
- π **Data Visualization Purpose**: Data isn't just visualized for aesthetics; it's a tool for gaining insights, telling stories, and understanding complex issues.
- π **World Population Story**: The script discusses global fertility rates, highlighting the differences between developed and developing countries and their implications on population growth.
- π **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.
- π **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.
- π **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.
- π **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.
- π **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.
- π **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.
- π **Focused Data Points**: Highlighting specific data points can help tell a more focused story and support arguments about fertility rate changes and their causes.
- π¨ **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.
- π **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
π 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.
π 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.
π 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.
π 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.
π 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.
Mindmap
Keywords
π‘Data Visualization
π‘Insights
π‘Fertility Rate
π‘Replacement Rate
π‘Population Dynamics
π‘Case Study
π‘Alberto Cairo
π‘Gapminder Project
π‘Information Graphics
π‘Functional Art
π‘Engagement
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
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