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