Speaker 1: So you remember flattening the curve right back in twenty twenty,
Speaker 1: that simple line graph became this, well, this incredibly potent image.
Speaker 2: Yeah, it really did.
Speaker 1: It was everywhere exactly and it represented this statistical argument
Speaker 1: basically for why we all needed to, you know, drastically
Speaker 1: change our behavior during the pandemic.
Speaker 2: It's pretty interesting how a visual representation of data could
Speaker 2: become such a like a shared understanding almost instantly.
Speaker 1: Right, and renod Letur pointed out something interesting there. He said,
Speaker 1: the actual virus, the trace of it, was really only
Speaker 1: knowable to scientists. For the rest of us, its impact
Speaker 1: was understood through well data, through statistics like that graph.
Speaker 2: It's fascinating, isn't it. How quickly a fairly abstract idea
Speaker 2: just shown visually became such a central thing in public discussion.
Speaker 2: It really highlights the power these data visualizations have.
Speaker 1: Definitely, how they shape how we see things, how we
Speaker 1: react to the world. Yeah, and that's really what we're
Speaker 1: digging into today. We're not just looking at how visualations
Speaker 1: help us understand things, you know, on the surface, but
Speaker 1: taking a much more critical look. It's kind of a puzzle,
Speaker 1: isn't it. We're just swimming in data these days. Oh yeah,
Speaker 1: yet our ability to really interpret it, to make sense
Speaker 1: of it, maybe even question it. Well, it hasn't necessarily
Speaker 1: kept pace.
Speaker 2: That's a really important point. And you see this rise
Speaker 2: in skepticism even towards you know, well established science, plus
Speaker 2: the whole filter bubble issue.
Speaker 1: Right, how that shapes what we even see.
Speaker 2: Exactly, and the sheer volume of data itself can just
Speaker 2: be overwhelming. It makes it harder to understand, harder to
Speaker 2: interpret effectively.
Speaker 1: So okay, our mission today is to really unpack how
Speaker 1: these data visualizations work. We want to look at the
Speaker 1: assumptions underneath the decisions that often get hidden behind the scenes,
Speaker 1: and crucially why it's so important to approach them with
Speaker 1: a critical eye.
Speaker 2: Yeah, and maybe even think about not just critiquing what exists,
Speaker 2: but how we could create visualizations that offer different perspectives,
Speaker 2: challenge things exactly.
Speaker 1: And for you listening, this is really about feeling more confident,
Speaker 1: more informed, without getting totally lost in all the numbers
Speaker 1: and charts.
Speaker 2: Right, understanding these nuances, it can be a kind of
Speaker 2: shortcut to deeper insight, a way to cut through the
Speaker 2: noise and figure out what's actually being said.
Speaker 1: Okay, So let's start with this idea of the veil
Speaker 1: of neutrality. It often seems like DEBTA visualizations are just neutral, objective.
Speaker 2: Yeah, that's a common perception. You see a clean chart,
Speaker 2: a polished graphic, and you think, okay, the data speaks
Speaker 2: for itself.
Speaker 1: But that's often where it gets interesting, doesn't it.
Speaker 2: It really is because these seemingly objective visuals can actually
Speaker 2: hide the very human choices behind them and the broader context,
Speaker 2: you know, the political, the economic context.
Speaker 1: They are made in things like algorithms too. They seem neutral,
Speaker 1: but they're built on tons of decisions made by.
Speaker 2: People precisely, which leads to those two extremes you sometimes see. Yeah,
Speaker 2: while on one hand you get visualizations used almost like weapons,
Speaker 2: pushing specific agendas, often by powerful groups, and then on
Speaker 2: the other extreme you get this sort of blind distrust.
Speaker 2: People immediately reject anything visual that doesn't fit what they
Speaker 2: already think.
Speaker 1: So it's a real tension how they can inform versus
Speaker 1: how they can persuade or just reinforce biases.
Speaker 2: Yeah, the challenge is to encourage like careful examination, rigorous critique,
Speaker 2: not just knee jerk acceptance or rejection.
Speaker 1: And that careful examination. It needs to go beyond just
Speaker 1: does it look nice or is it easy to read?
Speaker 2: Right? Absolutely, there's often this big emphasis, especially in design circles,
Speaker 2: on making things efficient, clear, excellent visual displays, which is valuable.
Speaker 1: Sure, but it can overshadow the really fundamental question.
Speaker 2: Exactly, like how is this data gathered in the first place,
Speaker 2: who decided what to include or exclude? And how is
Speaker 2: it being presented in a way that might subtly guide
Speaker 2: how we interpret it. Even someone like Hans Rosling, who
Speaker 2: is brilliant, absolutely brilliant at using visualizations to correct misconceptions
Speaker 2: about global health, he didn't always push that deeper level
Speaker 2: of critical thinking about the data's origins.
Speaker 1: Okay, so let's make this concrete. Let's look at a
Speaker 1: specific example, David mccanless's Colors and Culture visualization.
Speaker 2: Right, that's a good one. On the surface, it looks
Speaker 2: pretty straightforward.
Speaker 1: Yeah, the title says it all colors and culture, the
Speaker 1: meanings of colors around the world. It's supposed to show how,
Speaker 1: I say, red means good luck here, but maybe danger
Speaker 1: or debt somewhere else.
Speaker 2: And McCandless positions himself as this, you know, data journalist,
Speaker 2: someone telling stories with data using spreadsheets and design.
Speaker 1: Tools, which sounds great, right, and a world full of
Speaker 1: data having someone make sense of it as appealing.
Speaker 2: Definitely, But when you start looking closer at that specific visualization,
Speaker 2: some questions pop up about its foundations.
Speaker 1: Yeah, this is where it gets really interesting. Where did
Speaker 1: the information actually come from? Because it doesn't seem like
Speaker 1: it's based on you know, deep academic ethnography or anything.
Speaker 2: No, it seems to rely heavily on well tertiary sources,
Speaker 2: specifically Wikipedia and what he calls the general Web, which
Speaker 2: probably means Google searches.
Speaker 1: Things like that, so readily available, but maybe not the
Speaker 1: most rigorous or nuanced sources.
Speaker 2: That's the concern. It raises questions about thoroughness, potential biases
Speaker 2: in those sources.
Speaker 1: You know, and maybe that explains some of the well,
Speaker 1: the inconsistencies in how things are categorized could be.
Speaker 2: I mean, you look at it, You've got continents like
Speaker 2: Africa and Asia, but then also America, which is in
Speaker 2: the comment in South America and Eastern Europe.
Speaker 1: Right, subcontinents were regions. Then you've got religions mixed in Hindu, Muslim,
Speaker 1: one indigenous group, Native American, and just two specific countries
Speaker 1: Japan and China.
Speaker 2: Yeah, it feels a bit arbitrary. There's no clear consistent
Speaker 2: logic behind the categories, like where's Western Europe?
Speaker 1: And the amount of information seems really uneven too, like
Speaker 1: America has loads of entries, but South America, the inside
Speaker 1: ring has hardly any Exactly.
Speaker 2: That imbalance suggests that data collection might have been skewed,
Speaker 2: maybe just reflecting what was easiest to find online for
Speaker 2: certain regions.
Speaker 1: So it makes you wonder what was the main goal here?
Speaker 1: Was it really about accurately showing the nuances of color
Speaker 1: meaning globally.
Speaker 2: Or was it perhaps more about creating a visually appealing
Speaker 2: infographic the form over the function.
Speaker 1: Maybe it raises that important question, doesn't it. We often
Speaker 1: assume a visualization is authoritative, but its validity really depends
Speaker 1: on the rigor of the underlying data.
Speaker 2: Absolutely always question the source.
Speaker 1: It's crucial, Okay, thinking bigger picture. Now we touched on
Speaker 1: who creates these things, and there's often a power imbalance
Speaker 1: there isn't there?
Speaker 2: Yeah? Definitely, State actors, big corporations, they generally have way
Speaker 2: more resources to collect, store, analyze massive data sets.
Speaker 1: Which means individuals, citizens, community groups, we're more likely to
Speaker 1: be the subjects of data collection rather than using data
Speaker 1: ourselves for civic stuff.
Speaker 2: That's often the case. So based on that access issue,
Speaker 2: what kind of power dynamics does that create in the
Speaker 2: world of data visualization.
Speaker 1: Well, it suggests that people with the resources get to
Speaker 1: frame the narratives, right, They control the data flow and
Speaker 1: how it's presented.
Speaker 2: Exactly, which is why there's such a strong argument for
Speaker 2: boosting data literacy more broadly.
Speaker 1: Giving people the skills to understand, interpret it, even create
Speaker 1: their own visualizations.
Speaker 2: Yes, it's a key step towards addressing that power imbalance,
Speaker 2: making people more active users, not just passive consumers or
Speaker 2: subjects of data.
Speaker 1: Okay, so we have this illusion of neutrality, this power dynamic.
Speaker 1: Now let's dig into how the visualizations themselves can sort
Speaker 1: of hide the work, obscure how they were made.
Speaker 2: Right. It's interesting how a really clear, polished, excellent visualization
Speaker 2: often makes the messy reality of its creation completely invisible.
Speaker 1: All the uncertainty, the arguments, the cleaning, the data filtering.
Speaker 2: Yeah, all that preparatory work that debates the choices, it
Speaker 2: just vanishes behind this slick, user friendly interface.
Speaker 1: It's like a magic trick. Almost it looks so clean,
Speaker 1: so authoritative, you forget all the human judgment calls that
Speaker 1: went into it.
Speaker 2: And it's not just visualizations, is it. It's similar with
Speaker 2: algorithms totally.
Speaker 1: Kathy O'Neill's book Weapons of Map Destruction talks about this right,
Speaker 1: how algorithms, often rolled out without much oversight, can bake
Speaker 1: in and even amplify existing inequalities.
Speaker 2: She gives that stark example of the teacher fired by
Speaker 2: a flawed algorithm. The algorithm didn't account for key context,
Speaker 2: and the hidden human choices in its design had devastating
Speaker 2: real world consequences.
Speaker 1: So the point isn't that every visualization should look messy,
Speaker 1: but we need to be aware of this, this rhetorical effect.
Speaker 1: How hiding the decisions can create this false sense of
Speaker 1: absolute truth precisely.
Speaker 2: And we can actually learn a lot here from a
Speaker 2: related field, critical cartography map making. How so well. Critical
Speaker 2: cartography emerged when PEP people started questioning what they call
Speaker 2: the cartography of progress. This was mapping driven mainly by
Speaker 2: technical goals, seemingly neutral.
Speaker 1: But ignoring the underlying ideologies like why map this, how
Speaker 1: map it? Who benefits?
Speaker 2: Exactly? Just like decisions behind visualizations can be hidden. The
Speaker 2: bias is embedded in maps were often overlooked for a
Speaker 2: long time. Critical cartography brought those questions to the.
Speaker 1: Foe, and Halpern argues visualization goes even further. It doesn't
Speaker 1: just reflect the world, it actually shapes how we think
Speaker 1: about it.
Speaker 2: Yeah, Calpern's argument is quite profound. She suggests visualization literally
Speaker 2: trains our perception, confines what counts as rational or reasonable,
Speaker 2: and even transforms how we think about governing populations. This
Speaker 2: idea of governmentality who Co's concept increasingly tied to data
Speaker 2: calculation and neoliberal economics in the twentieth century.
Speaker 1: Visualization makes things visible that aren't immediately obvious.
Speaker 2: Right, and she talks about this shift towards experiential vision,
Speaker 2: trying to get us to almost think like a machine.
Speaker 1: Like those old IBM exhibits at worldfairs, multi screen things
Speaker 1: bombarding you with info.
Speaker 2: Kind of The underlying idea, maybe was that presenting information
Speaker 2: visually dynamically could bypass old ways of understanding and create
Speaker 2: this direct, intuitive grasp of complex systems.
Speaker 1: Now, most histories of data is presented as this straight line. Right.
Speaker 1: Progress innovation leading to better innovation is that the whole story.
Speaker 2: That's the conventional narrative, often a linear path, like everything's
Speaker 2: heading toward one perfect endpoint.
Speaker 1: Think of timelines, Yeah, the timeline metaphor itself, right.
Speaker 2: It's so ingrained, it's hard to think of time differently.
Speaker 2: But the philosopher Ari Bergson, writing way back, critiqued that
Speaker 2: very idea. He said, interesting narratives often move in multiple directions,
Speaker 2: they aren't just straight lines.
Speaker 1: He was writing around the time of Murray's early motion
Speaker 1: capture work, wasn't it and he felt that focus on
Speaker 1: measured clock time ignored our personal experience of time, his
Speaker 1: idea of duration.
Speaker 2: Exactly, and even then people were you using charts to
Speaker 2: argue for this idea of progress, this acceleration of science
Speaker 2: and art. It shows how historical accounts often have built
Speaker 2: in biases. Emphasizing linearity and.
Speaker 1: Speaking of biases, it's crucial to remember that Western ways
Speaker 1: of visualizing aren't the only.
Speaker 2: Ways, absolutely not. Take the kipu from ancient Andean cultures.
Speaker 2: It's a fascinating non Western system. It's not just about
Speaker 2: abstract numbers. It involves embodied data, physical knowing, and.
Speaker 1: David Turnbull's book Maps Where Territories highlights others.
Speaker 2: Yes, lots of examples challenging our typical Western map concepts.
Speaker 2: Varican drawings used as land claims, okay, Aboriginal bark paintings
Speaker 2: from Australia Marshall Islands, stick charts, stick charts. Yeah, they
Speaker 2: weren't for navigation like our maps. They were more like
Speaker 2: teaching tools, helping mariners learn to feel the ocean swells
Speaker 2: and currents. Navigation was a physical skill.
Speaker 1: Wow.
Speaker 2: Even Inuit coastal maps carved from wood. They had this
Speaker 2: tactile physical quality. You'd feel the coastline.
Speaker 1: So these aren't just abstract pictures. They're tied to physical experience,
Speaker 1: different way of knowing the world.
Speaker 2: Okay, let's shift. Let's look at some specific visualizations explicitly
Speaker 2: made for critique or reform. The Brooks slave ship diagram.
Speaker 2: That's a powerful one.
Speaker 1: It is incredibly powerful in surveillance studies. It's become really central.
Speaker 1: It argues for putting race and blackness right at the
Speaker 1: start of surveillance history.
Speaker 2: More so than Bentham's Panopticon, even.
Speaker 1: In many ways. Yes, the Panopticon was about subtle control,
Speaker 1: the possibility of being watched. The Brooks diagram depicted the
Speaker 1: horrific reality of a slave ship, a policy of terror
Speaker 1: that defined people as cargo.
Speaker 2: And those tiny figures on the diagram, they aren't just
Speaker 2: identical blobs, are they. They seem to have different gestures,
Speaker 2: some looking back right. Simone Brown, a scholar in this area, argues,
Speaker 2: those details suggest to kind of resistance even within that
Speaker 2: horrific representation, And unlike the Panopticon meant to be copied,
Speaker 2: the Brooks diagram was shown as something so awful it
Speaker 2: demanded its own elimination.
Speaker 1: Its power is in revealing that legacy.
Speaker 2: Yeah, the ongoing legacy of s.
Speaker 1: Then there's Florence Nightingale, her polar Area diagram from the
Speaker 1: Crimean War, another fascinating case.
Speaker 2: Nightingale's chart is famous for his visual innovation, the Rose diagram,
Speaker 2: but it's crucial to see it as a situated critique,
Speaker 2: a feminist critique really. How So, she used statistics to
Speaker 2: directly challenge the dominant, almost romanticized view of soldiers dying
Speaker 2: heroically in battle. Her data showed overwhelmingly that most deaths
Speaker 2: were from preventable diseases due to terrible sanitation.
Speaker 1: So it wasn't just presenting numbers. It was an argument
Speaker 1: a direct challenge to the status quo, pushing for reform
Speaker 1: in military medicine.
Speaker 2: Exactly, and she strategically sent her report with the diagram
Speaker 2: to people in power. A very effective piece of persuasive visualization,
Speaker 2: though we should also note it was still produced within
Speaker 2: a certain Eurocentric colonial context.
Speaker 1: Okay. And then a very different example the Great Trigonometrical
Speaker 1: Survey of India.
Speaker 2: Right the GTS, done by the British East India Company.
Speaker 2: This really shows how visualization can impose a conceptual framework
Speaker 2: to support control.
Speaker 1: It wasn't just mapping what was there.
Speaker 2: No, it was about creating an imperial space, imposing a
Speaker 2: British way of seeing India onto the land itself, making
Speaker 2: it seem manageable, categorizable for the colonizers.
Speaker 1: Turning complexity into something seemingly ordered and controllable through the
Speaker 1: map precisely.
Speaker 2: And this went along with the population census, seemingly just
Speaker 2: counting people, but it actually created new identity categories and
Speaker 2: became a tool for bureaucratic control.
Speaker 1: Arjent Oppaduri talks about this right number is creating the
Speaker 1: illusion of bureaucratic control.
Speaker 2: Yes, though interestingly, over time those very categories and ways
Speaker 2: of counting were sometimes turned back against the colonial power
Speaker 2: by the people being counted.
Speaker 1: Okay, fast forward a bit W. E. B. Dubois and
Speaker 1: the American Negro Exhibit at the nineteen hundred Paris Exposition.
Speaker 1: That sounds incredibly innovative, it.
Speaker 2: Really was remarkably prescient. Duboy and his team created about
Speaker 2: sixty and drawn charts, graphs, maps, and drawn one. Yeah,
Speaker 2: and they weren't just formally inventive, reimagining visual forms using
Speaker 2: color coded maps spiral graphics. They're also incredibly powerful in
Speaker 2: their message. They aim to directly counter racist stereotypes about African.
Speaker 1: Americans, focusing on things like population, land ownership, income education,
Speaker 1: especially in Georgia Exactly.
Speaker 2: The goal was to demonstrate with data that any perceived
Speaker 2: racial differences were due to social conditions, systemic barriers, not
Speaker 2: inherent inferiority.
Speaker 1: So at a World's Fare that often glorified colonialism, he
Speaker 1: presented this authoritative, data driven counter narrative.
Speaker 2: A powerful statement about progress and resilience despite immense hardship,
Speaker 2: really groundbreaking work.
Speaker 1: And then we have auto and marine neurath and isotype.
Speaker 1: Another hugely influential visual system.
Speaker 2: Isotype, the International system of typographic picture education. Yeah, often
Speaker 2: seen as the foundation for modern infographics, but their goal
Speaker 2: was bigger than just nice pictures. It was educational, deeply social.
Speaker 1: They wanted to empower ordinary people, help them understand complex
Speaker 1: social and economic stuff in modern cities.
Speaker 2: Exactly, they had this department of Transformation in their museum.
Speaker 2: Marine Noirath played the crucial role of the transformer.
Speaker 1: She took the raw stats and figured out how to
Speaker 1: frame them, select organize before Gerdarn's created the iconic pictograms.
Speaker 2: Precisely, it's a process very much like mapping, discovery and structuring.
Speaker 2: Now isotype has been criticized, maybe for oversimplifying sometimes, but
Speaker 2: its influence is massive. Think of the London Underground map,
Speaker 2: sacrificing geographic accuracy for clarity of connections. That owes a
Speaker 2: lot to Neurat's focus on conveying essential information.
Speaker 1: And they had that distinct way of showing quantity right,
Speaker 1: repeating standard units instead of just scaling one image up.
Speaker 2: Yes, like their chart on Great War casualties, each little
Speaker 2: soldier figure represents a million soldiers, color coded for wounded
Speaker 2: or killed. Very clear systematic.
Speaker 1: That systematic approach influenced later designers too.
Speaker 2: Definitely, people like Yan Cheechold with his new typography, Canare
Speaker 2: and Calvert's UK road Science, all focused on legibility efficient
Speaker 2: information transfer.
Speaker 1: It's a good reminder that even seemingly simple visual languages
Speaker 1: have philosophies behind them. Okay, let's talk about putting different
Speaker 1: data sets together.
Speaker 2: Yeah, combining data sets can definitely reveal interesting connections, like
Speaker 2: plotting your commute time against transit spending, my show or relationship.
Speaker 1: Yeah, but there's a potential pitfall, which is well.
Speaker 2: As you layer more and more data, you can start
Speaker 2: to lose sight of the specifics of how each individual
Speaker 2: data set was made. The focus shifts to the connections
Speaker 2: and the context. The potential biases of the original data
Speaker 2: can get sort of blurred.
Speaker 1: Which brings us to this idea of data as assemblage.
Speaker 1: Kitchen and Lareo talk about this right.
Speaker 2: They see data not just as raw numbers, but as
Speaker 2: part of a complex sociotechnical system includes the tech, the politics,
Speaker 2: the social factors, the economics, all interconnected.
Speaker 1: Like a recipe. It's not just the ingredients, but the chef,
Speaker 1: the oven, the cultural context.
Speaker 2: That's a great analogy and the controversy around Michael Mann's
Speaker 2: hockey stick graph showing global temperatureize is a perfect example
Speaker 2: of a data assemblage in action.
Speaker 1: Right, That graph became iconic and face huge backlash.
Speaker 2: Yeah, that single visualization and the science network behind it
Speaker 2: became a major target for corporate and political interest trying
Speaker 2: to downplay climate change. They tried to attack the analysis,
Speaker 2: split the data, anything to undermine it.
Speaker 1: It shows that facts derived from data need constant work
Speaker 1: to maintain their validity within these networks of power and interest.
Speaker 2: Exactly, data doesn't exist in a vacuum.
Speaker 1: Now, when we talk data, we often mean numbers quantitative,
Speaker 1: but there's also qualitative data, text, images, descriptions. How do
Speaker 1: they interact in visualization?
Speaker 2: So conventionally quantitative is countable, mathematical, qualitative is descriptive, needs interpretation.
Speaker 2: Often qualitative data gets turned into quantitative data by categorizing it.
Speaker 1: Like coding open ended survey answers into numbers.
Speaker 2: Right, which allows for analysis, but you inevitably lose a
Speaker 2: lot of the original richness and nuance. It's a trade off.
Speaker 1: You also mentioned different levels of measurement indexical data like fingerprints,
Speaker 1: attribute data like eye color, and metadata data about data.
Speaker 2: Yeah, all these distinctions matter for analysis and Underlying all
Speaker 2: this are different philosophical worldviews about reality itself. Creswell talks
Speaker 2: about five positivist, post positivist, constructivist, advocacy, pragmatist that shape
Speaker 2: how we even ask questions and represent findings.
Speaker 1: And trying to fit the messy world into niaque categories.
Speaker 1: That gets tricky fast.
Speaker 2: Doesn't it?
Speaker 1: Oh?
Speaker 2: Absolutely? Heck William can't use the simple word book. What
Speaker 2: is a book? The author's text, the physical object, a manual?
Speaker 1: The edges are blurry, and applying categories to society, like
Speaker 1: the gender pay gap, gets even messier. You have to
Speaker 1: define work pay gender, and those definitions themselves are loaded
Speaker 1: with assumptions, often reflecting dominant ideas that might exclude unpaid
Speaker 1: work or non binary genders.
Speaker 2: Right, categories are useful, essentially even for thinking. We group
Speaker 2: things with shared properties, but the frameworks we impose are
Speaker 2: always context dependent, never perfectly capturing.
Speaker 1: Reality, which leads to Ian Hacking's idea of dynamic nominalism.
Speaker 1: Naming and categorizing can actually make up people.
Speaker 2: It's a fascinating concept. Hacking argues that the act of
Speaker 2: classifying people defining categories like sexual orientations or psychological conditions
Speaker 2: shapes those very identities in society. The labels often created
Speaker 2: by experts influence how people are seen and how they
Speaker 2: see themselves.
Speaker 1: So visualizing data about groups can actually reinforce and solidify
Speaker 1: those labels, affecting policy and perception.
Speaker 2: Yes, it's a feedback loop, and Lorio extends this to
Speaker 2: making up spaces. She argues institutions like Statistics Canada through
Speaker 2: things like the Atlas of Canada, do something similar for geography.
Speaker 1: They create and reproduce a specific way of imagining the
Speaker 1: territory and its people, like Charles Booth's poverty maps, influencing
Speaker 1: how poverty was understood and measured exactly.
Speaker 2: His categories had real consequences. So how we visualize isn't
Speaker 2: just passive reflection, it's actively shaping reality.
Speaker 1: And Donna Harroway challenges that whole idea of objective, detached
Speaker 1: knowledge right. She advocates for situated knowledge right.
Speaker 2: Haroway critiques what she sees as a masculinist view of
Speaker 2: knowledge that pretends to be universal and objective, often devaluing
Speaker 2: other ways of knowing. She argues all knowledge comes from
Speaker 2: a specific position, a specific context.
Speaker 1: So we should value the local, the involved perspective, not
Speaker 1: strive for some impossible God's I.
Speaker 2: View precisely acknowledge the Knower's position, and that's crucial not
Speaker 2: just for creating data, but for analyzing and sharing it too.
Speaker 2: Having subject experts, people with lived experience involved is vital.
Speaker 1: Hey, let's look at some examples of people using visualizations
Speaker 1: specifically for activism and critique. Access now and their hashtag
Speaker 1: keep eyed on campaign tracking Internet shutdowns.
Speaker 2: Yeah, that's a great example of data viz for advocacy.
Speaker 2: They've built this sustainable platform for data gathering, focusing on
Speaker 2: community standards to minimize harm. Their maps of shutdowns saying
Speaker 2: the DRC provide crucial evidence.
Speaker 1: And then here the blind Spot using data sonifications for
Speaker 1: visually impaired Ethiopians experiences with tech access. That sounds really innovative.
Speaker 2: It is translating survey data into sound and spoken narrative
Speaker 2: makes the experience of digital exclusion incredibly vivid and accessible
Speaker 2: in a different way, really impactful.
Speaker 1: Also, the Yemen Polling Center working with Data for Change
Speaker 1: in a conflict zone making survey results accessible to policymakers
Speaker 1: there seems vital.
Speaker 2: Absolutely in places where traditional data collection is dangerous, Visualizing
Speaker 2: and sharing survey findings ensures crucial information about people's needs
Speaker 2: gets to those who need it.
Speaker 1: Okay, let's switch gears entirely the quantified self movement. What's
Speaker 1: the story there.
Speaker 2: Right, quantified self. It's basically this global community of people
Speaker 2: really into self tracking, using tech, wearables, apps to monitor
Speaker 2: all sorts of.
Speaker 1: Data like health stuff, sleep, heart rate, but also mood, productivity,
Speaker 1: food intake.
Speaker 2: Yeah, pretty much anything you can measure. They share their data,
Speaker 2: their methods, their insights at conferences, online forums. It's like
Speaker 2: personal science.
Speaker 1: You see people tracking allergies, DNA everything. Gary Wolf mentioned
Speaker 1: thousands of these projects starting online. But there's a critique too, right,
Speaker 1: whose interest does this serve exactly?
Speaker 2: There's a critique like from Ebgeeny Morozov calling it tailorism
Speaker 2: within applying principles of scientific management maximizing efficiency, but to yourself.
Speaker 2: Nicholas Felton's detailed annual reports are often cited as an.
Speaker 1: Example the navel gazing criticism too. Is it just self obsession?
Speaker 2: That's another critique, But the counter argument is that this
Speaker 2: self tracking produces genuinely new kinds of knowledge. People discover
Speaker 2: things about themselves they wouldn't otherwise know. Gary Wolf talks
Speaker 2: about the insights shared in forms. Felton himself found unexpected
Speaker 2: patterns and.
Speaker 1: This impulse to measure the self. It has historical roots
Speaker 1: back in the nineteenth century.
Speaker 2: Oh yeah, connections to nineteenth century productivism maximizing human output,
Speaker 2: and definitely to Etchian Jules More's motion capture studies.
Speaker 1: Right capturing the body as a theater of motion. His
Speaker 1: work aimed for objectivity automating observation.
Speaker 2: Yes, contributing to this focus on productivity efficiency. You can
Speaker 2: see echoes of that human motor metaphor the glorification of
Speaker 2: speed in some contemporary self tracking.
Speaker 1: And measuring bodies historically connects to managing bodies right control definitely,
Speaker 1: from early ideas like Ketillai's average man, to Galton's eugenics
Speaker 1: and composite portraits trying to find criminal types.
Speaker 2: To Bertilion's anthropometry measuring bodies for identification exactly.
Speaker 1: Bertillan's system was eventually replaced by fingerprinting, but the principle
Speaker 1: lived on. Think Post nine to eleven data valence biometric
Speaker 1: borders like Louisa Moore studies with US databases.
Speaker 2: Galton's composits were trying to prescribe an average a type.
Speaker 2: Winckenstein used composites differently, more descriptively.
Speaker 1: Right, Galton wanted the probable type. Bickenstein was more about
Speaker 1: showing possibilities, family resemblances, a different use of the visual composite,
Speaker 1: and artists have responded to this biometric surveillance, often playfully.
Speaker 2: Yeah. Artists like Rafaela Zanohemmer his Zoom Pavilion or Level
Speaker 2: of Confidence use facial recognition tech but turn it back
Speaker 2: on itself, exposing its limits, its biases, questioning that idea
Speaker 2: of perfect identification.
Speaker 1: And we can't ignore how surveillance disproportionately affects certain groups
Speaker 1: racial bias driving while black stop and.
Speaker 2: Frisk absolutely Simone Brown's work on Dark Matters highlights how
Speaker 2: surveillance technologies and practices are deeply intertmined with race and
Speaker 2: existing societal biases.
Speaker 1: Coming back to quantified self, there's this idea of the
Speaker 1: spectacular body, as Debor Lupton calls it, our digital cells,
Speaker 1: sometimes taking precedence.
Speaker 2: Yeah, some people describe feeling an identity crisis, almost managing
Speaker 2: themselves by proxy through their apps, trusting them data over
Speaker 2: their own physical feelings, like Sarah Williams experience.
Speaker 1: But there's another side too. Maybe just documenting daily life
Speaker 1: serially can be life affirming beyond just numbers.
Speaker 2: Definitely, think of artistic practices like the Ulipo group or
Speaker 2: Encoara's date paintings, a focus on the every day, the
Speaker 2: serial documentation as meaningful in itself, not just for optimization.
Speaker 1: Okay, let's zoom out to the city data and the
Speaker 1: city smart cities. How does visualization fit in there? Well?
Speaker 2: The smart city rhetoric often presents data visualization as this
Speaker 2: key tool for citizens, informed, engaged people collectively tuning their environment.
Speaker 2: Data as this stable, secure foundation for managing urban.
Speaker 1: Life, based on the idea that life itself can be
Speaker 1: managed by bandwidth like song Do in South Korea, Data
Speaker 1: as these recombinable units.
Speaker 2: That's often the underlying biopolitical hypothesis. Yeah, but there's a critique.
Speaker 2: Does visualizing a problem like air quality maps in Copenhagen
Speaker 2: actually equate tossolving it?
Speaker 1: Right? The risk of just making things visible without addressing
Speaker 1: root causes or community engagement becoming just tokenism.
Speaker 2: Exactly, presentation over substance sometimes, but they're also more critical.
Speaker 2: Community focused urban visualization approaches.
Speaker 1: Like Christian Old's emotion mapping.
Speaker 2: Yeah, projects like that try to move beyond just data collection,
Speaker 2: using tech to map emotional responses, then fostering discussions about
Speaker 2: why people feel that way, lack of public space, gentrification,
Speaker 2: whatever the underlying issues are. It's kind of a paradox
Speaker 2: using tech to try and recuperate situationist ideas of exploring
Speaker 2: the city.
Speaker 1: And thinking about the climate crisis its huge scale. Can
Speaker 1: focusing on everyday life individual actions really address that.
Speaker 2: It's a tough question. Mackenzie work talks about the spectacle
Speaker 2: of disintegration, how the scale can feel paralyzing. But some
Speaker 2: projects try to shift perspective. Natalie Jeramajenko's work like.
Speaker 1: One Trees or the Phonology Clock Right.
Speaker 2: They move beyond a purely human centric view, visualizing ecological processes,
Speaker 2: biogeochemical time, reconnecting us to non human systems.
Speaker 1: Contrasting Guide to Board's revolutionary psychogeography with Kevin Lynch's more
Speaker 1: pragmatic cognitive mapping in the US.
Speaker 2: Yeah, different approaches to understanding the city's effects versus making
Speaker 2: it legible for planning. Today, we see challenges with black
Speaker 2: box automated planning and.
Speaker 1: Smart cities, like the Sidewalk Labs controversy, in Toronto, where
Speaker 1: decisions felt opaque, community consultation may be lacking.
Speaker 2: Exactly, which highlights the need for genuine community involvement. And
Speaker 2: there are examples of visualizations by communities, like.
Speaker 1: The Levels poster about Philadelphia Parks.
Speaker 2: Yeah, made by Hector and students, a situated visualization showing
Speaker 2: hidden decision making resource disparities between neighborhoods.
Speaker 1: Very powerful for conflict urbanism Aleppo using diverse data, satellite images,
Speaker 1: social media to critically analyze urban damage during war.
Speaker 2: A crucial use of visualization for understanding conflicts impact and
Speaker 2: Annising's idea of patche capitalism fits here too, seeing how
Speaker 2: visualization supports desirable behaviors like with nudge theory, like.
Speaker 1: The new invert projecting energy use onto a power plant
Speaker 1: plume or bicycle barometers counting cyclists.
Speaker 2: Right, Those try to encourage shifts and behavior. Vandermore and
Speaker 2: Hill distinguished between functional, informative and situated urban visualizations. The
Speaker 2: bike barometer kind of hits all three.
Speaker 1: And lastly, Sheath Bunting's Status project mapping the conditions for
Speaker 1: being classified as a terrorist.
Speaker 2: A very personal political mapping exposing the often arbitrary, bureaucratic
Speaker 2: logic behind state classifications and their real world impact.
Speaker 1: So we've seen visualization reveals and conceals. Let's talk aesthetics.
Speaker 1: How does the look of a visualization affect us?
Speaker 2: It's crucial. Visualization makes some things visible, but it also
Speaker 2: makes the production process invisible. And the drawing style itself
Speaker 2: really influences how viewers engage, how bias they perceive it
Speaker 2: to be.
Speaker 1: Like research, showing handskips, styles feel more approachable, encourage more scrutiny.
Speaker 2: Exactly if it looks too slick, too perfect, maybe you're
Speaker 2: less likely to question it. Compare that to say, flee
Speaker 2: Perkotchevich's hand drawn cart to col Air maps of Anger.
Speaker 1: They look very different.
Speaker 2: Yeah, much more informal, evoking creativity, clearly showing the human
Speaker 2: hand it highlights the human origins, the perspective, challenging that
Speaker 2: purely objective feel.
Speaker 1: And traditionally, mapping and visualization often served colonial powers right,
Speaker 1: reducing complex places to calculable forms.
Speaker 2: Yes, Avoduri talks about this in colonial India, making landscapes
Speaker 2: and populations legible for administration, which leads us to alternative
Speaker 2: practices that deliberately resist.
Speaker 1: That, like the beehive collective Their posters are incredibly dense narrative.
Speaker 2: Right, Beehive prioritizes process participation complexity over slick efficiency. They
Speaker 2: use these huge detailed posters and do oral teach ins.
Speaker 2: It's about shared learning, connecting local stories to global systems.
Speaker 2: Very community focused.
Speaker 1: For bureau datudes. There are diagrams mapping sociotechnical systems embrace
Speaker 1: complexity totally.
Speaker 2: They don't simplify. They show the overwhelming interconnectedness of power
Speaker 2: structures forces you to grapple.
Speaker 1: With it, and iconoclasistas focusing on community lead mapping the
Speaker 1: process itself being performative.
Speaker 2: Yes, like Beehive, the active collective mapping representing together is key.
Speaker 2: It's about building collective knowledge and agency ties into that
Speaker 2: Zapatista idea, making a world where many.
Speaker 1: Worlds fit, acknowledging diverse perspectives, which brings us finally to
Speaker 1: decolonizing data visualization.
Speaker 2: Right. This requires really examining the foundations, even simple things
Speaker 2: like how we name places. Thinking about so called Canada
Speaker 2: signals the contingency of naming and.
Speaker 1: Margaret Pierce's project Coming Home to Indigenous Place Names.
Speaker 2: A powerful example. It embodies vincularidad, that integral relation with
Speaker 2: territory central to many Indigenous world views. It stresses accountability
Speaker 2: and design centering indigenous.
Speaker 1: Knowledge, and many of these critical projects have this educational
Speaker 1: goal right reciprocity, acknowledging lived experience like Prayer's critical pedagogy.
Speaker 2: Absolutely challenging top down knowledge transmission, recognizing community expertise. But
Speaker 2: it's complex, especially for non Indigenous folks advocating for decolonization
Speaker 2: without reproducing settler logic. Tuck and Yang remind us decolonization
Speaker 2: isn't just a metaphor.
Speaker 1: It requires centering the perspectives of those most affected and
Speaker 1: acknowledging how visualization's historical claims to rationality, transparency, universalism were
Speaker 1: often instrumentalized by colonial powers.
Speaker 2: Exactly, so decolonial visualization needs different approaches. Think of Torres
Speaker 2: Garcia as America in Britida flipping the Map, or.
Speaker 1: Alfredo Jars a logo for America in Times Square.
Speaker 2: Yeah, challenging dominant perspectives offering differently rooted knowledges. Critical visualizations
Speaker 2: can contribute by focusing on process community engagement, aligning with
Speaker 2: Haroway's call for situated knowledge.
Speaker 1: So wrapping this up, the key takeaway has to be
Speaker 1: that data visualizations aren't neutral windows onto truth, not.
Speaker 2: At all, definitely not. They're construction arguments, packed with choices, biases,
Speaker 2: hidden decisions.
Speaker 1: And developing a critical eye towards them is just essential now.
Speaker 2: Yeah, looking beyond the surface, questioning the assumptions, the context,
Speaker 2: the potential agendas. It's about active engagement, not passive acceptance.
Speaker 1: So here's a final thought for you to chew on.
Speaker 1: How might your own understanding of the world change if
Speaker 1: you actively looked for visualizations that challenge your assumptions, that
Speaker 1: challenge the dominant stories.
Speaker 2: Yeah, think about whose perspectives are often missing or sidelined
Speaker 2: in the data you.
Speaker 1: See day to day, and maybe explore alternative ways people
Speaker 1: are visualizing information representing different kinds of knowledge. What new
Speaker 1: insights could that spark for you.