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0:00
(upbeat music)
0:07
- Thank you very much everybody for connecting with me tonight, I'm humbled and very grateful for this opportunity, I'm really excited almost if I had all of you in front of me. Despite being here in my home office in East London, I do feel the person and the energy, and trust me, I'm really, really excited.
0:27
Is a great opportunity to be able to share my perspective working with data as a designer communicating story in perhaps unconventional ways, or less conventional let's say.
0:41
And I would like to share with you my experience as a data designer, as well as like share with you what we can do when we visualize data. So before I go into the actual me of what we do when we visualize data, seeing, sensing and simulating, I would like you to come with me on a journey. And we start this journey from this landmark, aside not far from the Royal Institution in the center of London. This is a water pump is actually not a real water pump,
1:13
it's a replica of the original incriminated water pump that in 1854 caused a cholera outbreak killing hundreds of people. So I'm taking you back in time to the 1800s. Cholera arrived in Britain, and this map shows the extent of the pandemic across the country. As we can see the dark areas are cities and they're the hardest hit.
1:42
People wanted the answer. And in the world of 1850s, cholera was believed to be spread by miasma, bad smelly air. Germs were not yet understood, and the sudden serious outbreak of cholera in Soho in London was obviously important and people started to really pay attention and try to come up with answers.
2:07
Some try to get the best data communicated in the most compelling way to support those answers and to support the theories or the beliefs of the time. Here we're seeing William Farr, a statistician of the time, this shows how the outbreak, the blue line picked in the summer months, hot weather equal smelly air
2:31
and therefore was kind of like a reinforcement for the miasma theory.
2:37
But it was John Snow,
2:39
no, not this John Snow, but this John Snow, a physician of the time, that came up with a unique approach.
2:49
He conducted a lot of observation with patients for months, and he started to realize that perhaps the 'cause of the disease was linked with drinking polluted water. I mean any wider because he noticed all victim actually did consume water from a specific wall. And he did something rather special, he map the cases during the housebreaking sower and the water pumps. And by seeing the locations of the depths and in the waters, we can see something quite interesting.
3:23
So let's zoom in a bit more into this map. Each death is represented with a black line, and it's place where the person lived.
3:34
And the pumps are represented with the blacks core that I'm highlighting here in blue, so they stand out of your more. And we can clearly see a concentration of cholera victims around the broad street water pump right there in the center. You see that there are lots of like black lines, but if we observe a bit more closely into this map we might see something isolated from the outbreak, the corner between new street and prop street doesn't have any cases.
4:10
So if we dig a bit more into this visualization we actually read that there's brewery there. Well, none of the 70 people working or living in the brewery developed cholera for different from the media neighborhood. And why is that? Well, John Snow actually observed that most of the people were either drinking water from a private well maintain by the brewery or just consuming beer this London after all.
4:41
So anyway to cut a long story short, although John Snow theory, didn't met some disbelief,
4:50
he convinced the London authorities to turn off the pump by removing the handle and this is why even the replica,
4:59
they are telling us about this story, doesn't have a handle. And you've resulted in immediate decline in the number of cases. Now, fast forward 170 years is keeping a couple of pandemics, here we are again with COVID-19. And the virus has been detected in nearly every country has this map shows. And this off this morning, those are the numbers. The virus is sickening, almost 94 million people,
5:32
and according to the World Health Organization as of this morning there were at least 2 million people
5:41
that died globally because of the pandemic.
5:46
So data visualization if become again key in trying to understand and provide answers to these complex dynamics. If we're a Googling COVID the first thing that we see before and you've written explanations are trends, charts, stats.
6:05
And the range of visualization that we've seen in the past months is really unprecedented. Our social media and news feeds are inundated with infographics, infection heat maps, chart, depicting transmission rates, death count, geographical spread, the impact of look of the lockdown, and now the vaccine count.
6:30
We've seen an incredible effort from researchers, scientists journalists designers really coming together to overcome the limits of the data. And those limits are due to the very challenge of quantifying this complex human experience. And we're using data to visualize the scale of the pandemic or to predict model and inform public policies and educate the public. And in this universe of COVID related visualization one that particularly stands out for me
7:05
is the simple graph, a diagram flattening the curve.
7:12
In my opinion is a great example of how information design can really empower storytelling can become a tool of communication. It has been so widely spread that it's become part of our vernacular. It's simple, both visually and verbally, and articulate so well why we need to take protective measures, because we want to spread the cases in order to protect the healthcare system.
7:42
So if Snow's map is a great example of how these visualization can become a tool for problem solving to connect numbers, identifying sites and offer solutions, or in other words to innovate. I believe that the second example a more modern flattening the curve is a great example of an information display that really can empower storytelling and can inform a wider audience.
8:12
And I think that these are the two main purposes of why we were present data in a visual form, on one side is the problem solving, and on the other side is the communication, and the two purposes are interconnected. In fact, if we're thinking about problem solving
8:32
very often the main step of solving a problem is to all agree on the data, on the problem, on how to interrogate the data, and there are presentation of it in the way that we're communicating it is really crucial.
8:47
So tonight we're gonna be talking about data design, which sits at the intersection of these two disciplines, science and art. And I'm talking more broadly about data design and not just data visualization because what I do when I design with data can really take different forms from charts to infographics, interactive dashboards or physical experiences.
9:11
And the space that we work in, is it's really connecting to human matters. So if science is our quintessential human motto to try to manage and control the chaos of the world, I believe the art is our more emotional response to the chaos in the uncertainty of the world. And the way that we join these two together, when it comes to data design is through visual storytelling applied to data and statistics, and data becomes like the raw material to base our stories on.
9:48
So how do we bridge these two together? Well, we take advantage of visual intelligence,
9:55
we all visual thinkers, which means that we have an innate ability to generate, store, retrieve, transform visual images and sensations. When we're looking at the bar chart, we're not looking at it just with our eyes, but we're looking at it with our brain.
10:16
When visual information leaves writing it's sent to our brain and there is processed. And we want to really engage our brain, but from an pre-attentive processing perspective as well as not attentive method, which means the fast and this law systems that our brain work with. So to go quite briefly on how visual perception work to illustrate those concepts, I would like you to take a look at these table. kind of like a simple sequence of numbers, but it's gonna take us like a few seconds
10:55
to really understand the relationship between these numbers. And the reason is because we need to tap into our slow system the analytical brain, to calculate the difference between the number and understand the relationship. However, if I'm showing you the same numbers, in a visual form, in this case a column chart, our brain is engaged in a fraction of a second, our visual cortex can detect these patterns, and see an increase or decrease, and also the magnitude of these changes.
11:28
It's pretty incredible we are carrying with us this visual processor that is incredibly powerful which is our visual cortex, and its really, really fast.
11:39
And even young children can see the difference in color, contrast, length, width, in effortless and some cautious way. And for the reason data visualization is a powerful visual exercise, because it takes advantage of our pre-attentive processing. And knowing that we have those visual attributes our brain can make sense of, then we can actually organize the data in a visual form, so it communicates to our brain what we want to communicate, what you want our brain to see,
12:13
by grouping things, by making things move, by making things larger, smaller, we can draw people attention and we can create a visual hierarchy. What we see here is the least of all the pre-attentive visual attributes that our visual cortex is able to quickly spot.
12:36
And the person in you important visual perception was was this guy William Playfair, who in his notes wrote no study is less alluring or more dry and tedious than statistics, unless the mind and the imagination are asset to work. You might wonder always William Playfair. Well, his is regarded among many other things to be the father of this charting techniques, the line charts, the pie charts and the bar chart.
13:12
Conventions that were using steel nowadays after a few hundreds years.
13:19
But visual intelligence can do much more, before the invention of charting convention, we've made sense and try to communicate the complexity of our world in a visual form. And therefore we can actually take advantage of the meaning of images, the meaning that we have applied to symbols, to incode meaning in our stories. Data visualization is more than just representing numbers, ultimately it's about visualizing and telling stories. So we want to take advantage of our visual cortex
13:52
in the way that it reads patterns, but also the way that we can read meaning into images.
14:00
Millennium before written language, we have employed images to communicate, think about a cave painting, or the first properly
14:12
coded written system hieroglyphics were pictograms.
14:19
And this is not just in native civilization is actually native to each one of us as children extensively, we make sense of the world visually before we're able to communicate, and we can draw and then code what we see around us before we can write. These are drawings in response to a project that I did a few years ago when I was investigating gender stereotypes seen illustrated books for kids. And I a bit of like field work with children and asked them to draw the concept of a man and a woman.
14:58
And both kids here, a three and a four year old were able to articulate those abstract concept in a visual way.
15:09
So there's always one physician can really employ semiotics which is the study of how we associated meaning or how we read meaning into symbols, callers, iconography.
15:24
And we can take advantage of semiotics to encode the worst story. But we must draw and you put an observation here, that if our pre-attentive system can read these pre-attentive attributes or these visual attributes the way that we do these four lines from way, in a universal way, when it comes to the meaning of symbols, especially of things that are far from what they represent because they've encoded some sort of meaning with learned, those are very much dependent on the society.
15:59
So we are taught about these meanings are conventional representation that will learn and we need to be mindful of that, and how we apply those conventions.
16:12
So let's preach these two ideas of how we can reach visual patterns, as well as make sense of visual metaphors to communicate data and stats. Let's look at a couple of example.
16:27
How much water on the earth is actually a really interesting dataset? We read that 71% of Earth's surface is covered by water.
16:38
So most of our planet is covered by water, however, only 1% is drinkable water.
16:47
So that's a hard to read, the next step might be to put in a visual form, like a chart, a pie chart. This is definitely a step further in the communication of the data, it helps, but we can do more. We can create an image that is truly worth a thousand words. It's more memorable and more intuitive, it just becomes tangible. We can see the volume of the water and even the 71%, so all the ocean actually, when we're looking at this year these droplet in comparison to the volume of earth
17:23
is actually not very big. And looking at the drinkable water, is like it really makes you pose and think is such a precious resource. In other great example here of ineffective display of data, that goes perhaps beyond our traditional statistical presentation of climate is warming stripe. The represents the global temperature change from 1850 to 2019 was created by Dawkins, a climate science scientists working at the University of Reading, who explore the visualization of the same data
18:03
in many, many ways. And finally, in 2018 or 19, came up with this chart, it's been incredibly popular because it really engaged our visual cortex in an immediate way. Straight away without the many details we know what's going on, we can read time and we can read temperature, and how the temperature is increasing. And chances are that you've come across this visualization because it's been incredibly popular from magazines cover to even be used as a backdrop of a concert.
18:42
And they would say that, if people are even embracing it
18:50
to a point that they wear it
18:54
it's done something really special. And well, if you're not into the dress type of data driven product then you can go online and you can find all sorts of gadgets or personal items with this visualization.
19:12
Why so successful? Well, I believe that is because it moves away from the conventional data visualization language of this data. Here we're seeing a more conventional representation, that perhaps provides more means for analysis, more details but it's definitely less compelling. Can you imagine this chart being used as a backdrop of a festival? Probably not.
19:43
Worming stripes paints a picture, and it really borrows the language of art are becoming more and more emotional, more intuitive, more human. And in a way similarly to call a few painting, the you're seeing here, it kind of like strips out all the distractions, and uses only color to convey meaning.
20:09
Now let's moving to my experience working as a data designer, so moving among these two disciplines of science and art,
20:17
I would say this I wear different hats when I'm navigating the two. I'm the explorer when I'm trying to extract this story in seeding sides of the data, and thinking about the purpose and the audience,
20:33
I need to find a way to communicate these stories. And in the case, I become a guide, where I want to create an effective display that connects the data, the stories with my audience, and really opens their minds to the message and reaches their heart.
20:52
I'm a designer at the works with data as a creative material. So I'm not the interesting in the data per set, but I'm very interested in the stories that we can find in the data and ultimately data store knowledge, is all about how we interrogated, What questions do we ask, and then the stories that we are representing and how we represent them.
21:15
And is a great space to be working on because I'm constantly learning about the world data we live in. Each datasets presents a new challenge, and they do learn about different things. So with my book Infographic History of the World, I learned about the evolution of the world, and the evolution of humankind.
21:42
With this book was about the complexity of our brain, or perhaps the urban ecosystem that we live in, or racial discrimination and how it starts at birth , or tools that can allow us to prevent fraud or identify at least. So I feel really fortunate to work in this space, because I work as across many different fields, many different sectors, and also medium, from print to digital, as well as experiential. And I've been lucky enough to even bring data visualization
22:17
inside life theater performances, with large scale projections, to talk about climate change or data privacy.
22:29
Tonight, I'm gonna pick three projects, and I'm gonna try to discuss with you what we can do when we visualize data. And I'm gonna try to address this question with these three points. We try to make the data visible, and more than the data, the stories feasible. And we want really people to feel the implication of this data with sensing and ultimately we wants to stimulate an action.
22:56
We want to be part of a conversation. And to address these three points, I'm gonna use three projects, and I'm gonna take you a bit also on a journey of my career.
23:10
And I hope there is gonna be something of interest for everyone, because we're gonna be talking about war, music, and social change.
23:21
So let's start with the first point, we talked about how data visualization is taking advantage of our ability is visual creatures, our visual intelligence and how we want our brain to connect with the data through our visual cortex in a very fast way, but then obviously tap into our rational brain to the implication of the data, and what the stories mean. There're usually just steps when we visualize data, the first step is well gathering the data of course,
23:57
and then analyzing it and extracting these sites, understanding what is the data saying, and what would be compelling for our audience to know.
24:09
And then the second step is the rendering. When we created this realization, and this visual display should really explain our insights and take our reader to a journey of discovery.
24:22
I'm gonna take an example from my book, The Infographic History of the World, to illustrate these two steps. And just a couple of words about the book. So this is us collaboration with data journalist James Ball, and it was our attempt to narrate history in an unconventional way through 100 infographics.
24:47
We looked at pre-story to the modern world, they use, you can see there are four chapters, and they evolve in the look and feel to kind of mark the passing of time. All of these 100 infographic started with a simple question. And for the specific of the infographic, that I'm gonna be discussing with you tonight, the starting point was these, how do the world's conflicts compare?
25:16
Quite a broad and generic question.
25:19
We can address these in many ways, and data research led us to this dataset, the Polynational War Memorial. A least the wars of the 20th and 21st century, in which we see the time, their regional distribution of the wars, as well as the cost, the battle deaths.
25:45
At a glance by putting actually a filter of looking the wars, the cause, they claimed more than 10,000 lives. Well, we're seeing the war is being a constant element of the century, with a staggering figure when it comes to the human sacrifice, 95 million deaths in 133 wars just over a hundred years. And that's really the story that I want to visualize. I want to visualize all the wars and the fact and the human sacrifice.
26:22
So let's go back to the process, we need to see the data in order to extract the stories, and then we want to render it to attract our readers
26:34
attention and to explain the story. So step one,
26:40
I need to put this dataset into a visual form to engage more visual cortex and to be able to see and spot patterns. So I find that chart that allows me
26:51
to plot all these different dimensions. I have time in year in a conventional way represented horizontally. Then the cost of the war is there elements that I really want to focus on, and therefore I use the pre-attentive attribute of size, which is equal to magnitude in a very direct comparison, and therefore each bubble represents the conflict tall in terms of like the number of deaths. And then the position on the y-axis represents the duration of the conflict.
27:21
So it's a simple scatter plot using bubbles, in our brain is engaged here, because now we can finally see how these words compare in the relationship across the century. We see two main conflicts on the first half of the century and there are two outliers I would say in terms of the size,
27:46
the great war and the second world war, caused a lot of destruction, many, many death. But then in the second half of the century. you might notice that our density in terms of the bubbles is getting greater and also their position according to the y-axis is getting higher. It means that there are more wars lasting longer, and there is an outlier in terms of the duration, one war that's been going on for six decades, that is making my vertical axis really tall.
28:24
And that's Israel and Palestine.
28:27
So one more step in the processing, I try a log scale here because I want to spread out the range of duration between one and 15 years where I have the most density. There are lots of bubbles of relapsing, and then I just want to spread them to see them individually.
28:48
I know by doing that, I'm skewing a straightforward representation.
28:55
This is pretty much as far as I go in terms of like seeing the data in an exploratory manner to find a story. And to me, the stories really like, I want to visualize all of these wars, and the pattern the fact that it actually did in the second half of the wars we see more of them is very interesting.
29:17
So we're gonna the second step, when I'm thinking about how am I gonna encode this dataset to be part of a series of a hundred infographics in the concrete possibly a hundred bar charts or hundred bubble charts.
29:33
So I look for inspiration, and I try to think deeply about what the data stands for. I usually find my inspiration or the references that you use in science, nature, art, proper culture, I look elsewhere, not in the database field. First of all because you want to do something unique, but second because what you're presenting is not data, so looking at other data visualization doesn't usually help. What we really want to think is about this story, that the data is not rating.
30:08
And in this case is in a photography, and proper culture that I find the inspiration. This field of poppies becomes a metaphor that I can use to encode my data. And when I see this image that recalls in my mind in flood the films,
30:29
I can click and I can join these two together because I know the shape of the data.
30:37
I go back to the drawing board, and now I've got a new visual elements, that I didn't have on the scatter plot is the stem. So I'm thinking what if I use the stem to actually draw duration both horizontally on my timeline, but also vertically. In the case, I can actually overcome the skewing proportion of the duration, if I use a lock scale. And this is the final outcome, where we see that all the wars, each poppy represents one conflict
31:11
is anchored on the timeline by the stem, and it grows horizontally and vertically, and when it stops growing is when the war ends and there we see the size, the toll of the conflict, the human sacrifice with the symbol of commemoration is the poppy.
31:34
And I guess like, even if you don't understand the poppy, is a visual metaphor because you might not be familiar with it, the book has been printed and distributed in different countries. It's still been visualization, the resonated because it borrows a belief that the rhetoric's that we use in poetry like oxymoron
32:01
there was the basic of in front of the fields. There is a striking opposition of life and death that when you see these image you kind of like stop and do a bit of dissonance, right? Between the appearance and the content, it surprises you, it makes you pause, it makes you stop, it makes you take you then.
32:25
So one more thing, actually on this one, due to the nature of the outcome, it needed to be contained in this part of the book. And it couldn't possibly label all the wars. And there was of like a pity, because I did think they all the wars needed to be labeled in a way, but it was practically impossible to feed all the labels. So a year after the book came out,
32:51
to coincide with the centenary of the great war, I decided to launch any interactive experience proposing the same visualization but in an interactive form. I take the users through a brief introduction of the poppy being actually adopted after the great war is a symbol of commemoration. In fact great war was stopped to be the war to end all wars.
33:19
And then as a user you're dropped into these interactive landscape that allows you to really digging into the data, and to see different stories, with a more kind of like user driven experience here you can really see what you want, and what we resonate with you, whether is reading the data or the story attached to each individual poppy, or perhaps compare different regions. Here you see developed countries, Europe and North America on the right and the rest of the world on the left.
33:56
And I guess like these visualization went viral and reasonated,
34:02
because in my opinion really breached these two field of science and art. So it's still in an accurate way, represents all the data in the kind of statistical form, but it really borrows the language of art to communicate and to create a deeper connection and a deeper meaning.
34:26
And I believe that what it creates this really empathy. So it's not just about seeing with the eyes, seeing with your brain, with the mind, but really with the heart to create a bridge into the stories that we're presenting and making people feel the data, and getting a bit closer to the data that we're presenting.
34:51
So I would argue that in our process when we are visualizing data, there are not just these two steps processing, analyzing the data, rendering, visualizing it, but there I the next step is really the experience, the sensing.
35:11
When we're trying to visualize the data sets with my rush, to just go into whatever is available to us because of the tool that we're using is giving us a pie chart or a bar chart. And there might be enough in terms of like rendering the data, but that doesn't mean that our job is complete, because if our audience doesn't actually understand why one bar is longer than another, or why some things have drastically change,
35:43
increase or decrease, then we're not really communicating effectively the data in the story. So it's really important that we think about the experience, what are we trying to communicate, and that we're doing everything that we can possibly do when we're rendering these data in a bar chart, do we need more label? Do we need to change the color of one bar to really make sure that our readers
36:08
are getting closer to the data into the stories?
36:14
And in a way we want to strike the right code and make people really feel the data. And when I'm talking about sensing I'm not talking about touching in non-literal sense, but really like making people sense the implications.
36:34
And I do think that data visualization can offer a lens to experience the world in a different way.
36:42
So oftentimes we think about this process in a linear way when we go from a spreadsheet into a visualization that if it's done really well can become a communicative experience. However, I would argue that data and the stories that we're presenting does not containing our spreadsheets. The data is just a placeholder for something else, for our world experiences, for our human experiences. So if we're thinking about this visualization as a tool, as a lens to see the world,
37:15
then we can really adopted is a way to extract meaning from any experience. So what if we start from an experience, and we use the visualization to extract the data, to process it, and then to propose it and to offer a new snapshot and a new experience to have a deeper understanding of the starting point.
37:37
So yeah, data is information
37:41
that is found all around us, in where we go, what we consume touch, watch, or hear.
37:48
And design can become a tool to make the invisible visible to render the unseen.
37:55
So in the next project, I would like you to take you on a journey of what we hear imaging and feeling while we're listening to a song, and this project is called oddityviz. Is a visual deconstruction of David Bowie's famous space oddity. Is a collaboration with a data researcher, a musicologist, Miriam Quick, and it deconstructs these I chronic piece of popular culture space oddity, giving form to what we hear, imagine, and feel where we're listening to the song.
38:30
And the Genesis of this project was this question. What would the music look like through the lens of data visualization? A bit of context, why David's Bowie why space oddity? Well, when their artists passed away, I was kind of like I'm drawn to think about his work, and the way that inspired me, and I really wanted to use my craft data visualization to celebrate his work. So I've reached out to Miriam, who I worked with, and I also knew the share the PhD music.
39:06
So she would be the perfect expert to guide me through the data discovery. And together we embarked in this journey, they started with space oddity because was the first masterpiece that really launched Bowie to international fame.
39:23
And really our ambition was can we use data to understand better his craft and to come back to the original song with a deeper understanding, with more we more knowledge if you want.
39:37
So the song is spectacular is rich in culture connections, is about an astronaut's Major Tom, who goes into space and never comes back. Also bear in mind when he's released he's 1969, [Indistinct] just about to land on the moon. And it was not a surprise that the BBC refused to play until the pole 11 astronauts it's safely return. Not just that, according to David Bowie himself, the three inspiration was seeing Kubrick's film 2001 a space oddyssey. You actually watched a movie several times,
40:13
and he said that he was stolen out of his mind, he was stripping when he came out with the first few lines of the lyrics. So I extremely excited to actually dig into the song
40:27
and see how Bowie piece it together to become this beautiful experience. And Miriam really helped in terms of like bringing her expertise, or a scientific approach to deconstruct the music. We collected data from a musical perspective but also interpretative and emotional, so there was a lot of like listening to the song on repeat and we're still no sick of it which is obviously a really good sign. At the outset of the project, I tried to identify a way to create a system
41:04
that allow those to then codify all these insights
41:10
that we were gathering. And the medium of divinal [Indistinct]
41:20
helped me to kind of like navigate the many choices that needed to take. So if you're thinking about the divinal and how it work, obviously was there the way that the song was distributed when was released, but if you're zooming in into the groove, those are actually data visualizations. They were just not design or device to be seen by our eyes by our brain but didn't need also off the record player. So I thought, what if we could expand this grooves, and show the different layers of data,
41:55
through data visualization or infographic storytelling. And they got really excited, so we tried different materials, and then we actually use acrylic to engrave data visualization in these data sculptures, that resemble dare LP.
42:13
Here, you can see that this system is based on a Muslim time. They becomes the connector between the time visualization, time begins at 12 o'clock and it goes around the record. So this length of the song, one turn once spin around circumference, And each record contains one layer of our deconstruction from the narrative to the texture, the rhythm, the structure, the lyrics, melody, trip and emotions. And I'm gonna share a couple with you
42:49
just to kind of like show you the top stories that we found. So starting with the most obvious element narrative we tracked Major Tom's trajectory from earth. We're seeing here a very sober representation of where the two characters are, ground control, his next to the center, the central label and is depicting with a solid white
43:17
for when he's stalking is shaded instrument for when there is so sorry shaded lines for instrumental sections. On Major Tom is this periling line, and again there are the two vocals and instrumental sections. There are some kind of like doted lines that connects the two, as you can see here, and those are the attempts to call each other. And we know the story because the two are talking, we know and we can track where a Major Tom is, because he's reporting back to ground control,
43:57
his distance and where is.
44:00
And the dialogue between the two characters is really the key element of the song. It reminds us of radio transmissions, and when disecting actually the grammar of dialogue is really informal, memorable, there is a lot of repetitions. Think about the team cone, the liftoff. the countdown short sentences, quite a lot of repetition. And it's almost like that we could be listening in on to kids on walkie-talkies. And I didn't know this, but this song was written to be a duet,
44:38
but in dance probably performed both roles, of course it was an amazing performer. So you're posed the more low bar baritone pitch, for ground control commands, and then Major Tom has a higher wide ranging melodies.
44:56
And each instrument plays a roles in narrating this story,
45:02
and it really becomes like a balancing act between the most traditional orchestral running instrument and the non vintage sensitizer.
45:12
When we're looking at all the instruments used, there is attention between the past and the future.
45:19
And when we're looking at the roles of these instruments, it's interesting to looking at the way form of the various tracks. They compose the song, because we can see some very interesting thing for instance, the strings are silent until the moment of countdown, and then there is a suspense that is spilled with a countdown. And then there is this beautiful crescendo, this is mostly done through the strings that gets louder and louder. And we know that it's got like an emotional response,
45:53
because in an empirical way, we did track how people felt and their emotional response during the song, so interview 50 people. And we noticed that as they we're listening to the song, their emotional response was peaking in the moment of liftoff.
46:14
There are many more stories that we found during our deconstruction, and you can find those@audittvs.com as well as like listening to the song with our visual deconstruction that is been animated throughout the song.
46:30
So to wrap this project, it took months of exploration and many hours of listening to the song and repeat, so definitely as creators, we came back with it, through to the initial experience, with a much deeper understanding of Bowie craft.
46:48
We were incredibly impressed and inspired. And really now our exhibition, there was the outcome of this project. We invited the public to take this journey with us to see the invisible and try to give form to the music,
47:08
to come away with a deeper understanding of the song and the masterpiece.
47:15
And if we're thinking about data visualization is a lens that allows us to process
47:22
through the rendering of an experience, and extract the new data, and then piece it back together to communicate that deeper and experience, then we can really use it as a tool to understand the world that we live in any experience, because we can actually extract data from anything.
47:43
And in this process, I would say they wish to had destroyed element. We are not just creating inexperience when we're visualizing data but we're trying to really create a shift. We want to stimulate our reaction in action. We want to create a transformation. So tonight I hope that you will want to listen to space oddity after my talk and perhaps think about it, and the stories that you've just heard through data visualization and experience it in a new way.
48:22
So how do we create this shift and why is it important? Well, because it's an act of communication. We want to pick our audience curiosity, challenge their assumption, or simply be part of a conversation, but it's really important that we decide upfront. What is the type of communication that we're trying to engage our audience with?
48:46
They do believe they oftentimes when we were just rendering data, and we publish it is a quick snapshot and we think our job is done. We don't really think about the implication of this communication. We think okay, I've done my job, I've created a bar chart here, is render, is done. But really what we're trying to do is communicating an important story with the bar chart. And the bar chart is not a conclusive answer, is possibly just the best possible understanding
49:16
that we have at the moment in time of the dataset. So data visualization comes with a lot of arrogance
49:26
if you want, when he presents itself as a conclusive answer. And I do believe that what we do when we're creating these data snapshots,
49:37
just a snapshot of the reality that we live in. So they should really be the beginning of a conversation. And I see data is a vehicle to explain our human experiences. Even if you provide evidence of our theory, and so forth, we should really engage in this act of communication with a broader perspective. And really thinking that the process that we've taken, where there was like data collection, the way that we have analyzed it, the way we have interrogated the data, capture our own bias,
50:15
our own understanding of the subject, and how to frame it, how we encode it, how we visualize it. Obviously they're all decisions that the creative stake, and they come with limitations, right? In that totally representing the full complexity of the world.
50:34
So I would like us to think about really the limitation of what we do when we're visualizing data, and portraying the world that we live in. As a Belgian surrealist Rene Magritte made us rethink
50:50
about our ability to truly capture reality with this painting, by throwing, or by painting a pipe and saying, this is not a pipe. I do believe that in data visualization, we should also really think harder, about our ability to really capture the full complexity.
51:13
This is the premises of the next project. The premise is really a limitation, the sober reality of the limitation of the data that we had to fully capture the extent, and the depth of the Me Too Movement. So we use dat visualization in this case to address this question, can social media become of your call to force the social change and help reshape traditional views, to create an impression of their shared experience with the hashtag Me Too?
51:49
The project is called Me Toomentum, and again, he's a collaboration, this time with Lucia Koncincova. And, you know address this question in offer like capturing
52:03
if social media can actually become a catalyst for social change by looking at a limited amount of data? So in our Corpus, as we scraped API of Twitter, we had about 200,000 tweets after cleaning the dataset, which in the full scale of what the Me Too Movement is been, was a drop in the ocean. Obviously the Me Too Movement is been going on for many years. It was just on social media that it became this powerful international voice,
52:40
that shed a light on the magnitude of the problem. But yeah, it's called like deep roots and Twitter was only actually one of the many challenges that those voices too hold.
52:58
But we decided to focus on Twitter because it's where at the end of 2017 revelation of the scope of Weinstein wrong action, really took hold in response to this tweet by Alyssa Milano, many, many people came forward, in an act of protest and really to create change.
53:25
So we looked at the DPI, and we extracted all these personal stories, and after we had the data, we had a completely blank and the creative block where it was paralyzing looking at these stories. And I was really trying to find a way to unlock the creative process. And here art really helped me to overcome all these limitations since also maybe release a bit the weight of the data.
53:59
So I borrowed the symbol of the dandelion that we find again in popular culture,
54:06
carrying different meanings
54:08
is often used as a symbol of hope. We blow it and we wish for something to change, and you also capture, in my opinion these two characteristic of the Me Too Movement. Is fragility when it comes to the individual voices or the individual stories, but it's strength when it comes to all these voices together. And I saw in this visual metaphor potential to paint a picture of the data.
54:41
So when it came to interrogating the data,
54:46
I use this metaphor and thinking about how those forces are connected, and really thinking about the science of dandelion, right? When its in is seeds form, it's got all these points are connected with lions. And so I felt how could I use those to explore different angles of the dataset? And we came out with three snapshots. The first one's spreading, looks into the geographical footprint of the movement. Then we look, we're looking to the themes, so the most popular words that are associated
55:21
with hashtag Me Too. And then finally, the third snapshot is about the most popular tweets, and looking at these trending voices.
55:36
So starting with the analyzing the geographical footprint, is not a surprise that most of the tweets are in North America in the states. First of all we actually just creeping the English hashtag and the Me Too it's been translated in many different languages. But also for the first six months of the conversation, most of it was in the U S. However, it truly reached everywhere,
56:08
many, many countries around around the world. So we thought what we're looking in terms of just interesting story, so we plotted the number of tweets, so tweet count, but more interestingly, we thought there was the Twitter popularity. So how many times those tweets were retweeted or shared. And we see the actually in many regions where maybe there weren't the many voices, those voices were really heard, like in Bangladesh for instance, really sticks out.
56:42
The second infographic offers a summary of what themes were containing our dataset. We struck the demos frequent terms, and we clustered them and colored them by the theme.
56:57
The center is clearly rooted by violence, sexual harassment,
57:03
but we also see accusation and solidarity, calls to actions, and many of these words will resonate
57:16
with shock when we're seeing the perpetrators, and the victims as well as like, where the violence took place.
57:32
And finally, the third snapshot is about looking at who were the key voices.
57:41
And by looking at just the most popular tweets, the one that had more than a 1000 of retweets, we extracted 1,500 tweets, and we plotted them all on some sort of timeline. And the seeds that stick out, the most are the ones that were retweeted thousands and thousands of times. But it's interesting to know the correlation between the size of the seed that represents the number of follower and the distance from the center. So the bigger the seed, the more followers the Twitter had
58:19
at the time of tweeting.
58:21
And when was zooming in, we actually see that those tweets they're really going far, they resonated a lot, they were shared a lot.
58:32
Are mixed like some obviously popular people, but others are just common people that were sharing personal stories, interesting point of view,
58:43
or just being part of this important conversation to challenge the status quo and to be part of them.
58:56
The Visualization is available@metomenterm.com, and you can explore all the tweets as well as like you're looking at the timeline and how the movement is evolve.
59:09
So to bring it to a close into recap, I hope it in the next time you're looking at a spreadsheet, you'll feeling inspired rather than overwhelmed by the data. And think about the way you're presenting data is much more than creating an accurate chart. We want to engage people in an act of communication when we want them to see the insights, understand this story, and feel the implication of this story, and stimulate a response.
59:37
And design I believe is one of the best tool that we have for making information useful not just presenting it visually, but giving people voice to work, think, and respond to it. And we can take people closer to understand the world we live in if our act of communication is well is well-designed. And in that obviously comes a huge responsibility, as creators, we must understand that we have the power to influence or shape how people understand the world around us, through data visualization,
1:00:14
understand that there is a bias in the process, and we can't remove ourselves as creators. And we do decide what stories matter, and what stories not needs to be represented and how. So, I think that if we adopt an approach to this collaborative in nature between disciplines, we've got a really powerful tool data design, that can link and marry science and art, data and visual communication. And the two can come together to create meaningful and more human outcomes.
1:00:52
We should focus on dialogue among disciplines, and embrace what makes art so special. I believe it's this ability to make people stop, think and also is inquisitive and critical, and we need a lot of that, especially right now that we're facing so many large scale societal, ecological, technological transformation, we really need a more humanistic approach a multiple perspective into statistics in order to truly unleash the potential of data. So I like to close with an observation,
1:01:29
data visualization is certainly a snapshot of a complex world, but it's powerful because it can take us a step closer to understand the world we live in.
1:01:42
Thank you so much for listening, I hope you enjoyed it. It's been a long time, since the Tuesday night is a faulty special, thank you. (audience applauding)
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