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Data Beyond the Screen: Sculpting Community Voices with Rahul Bhargava
681 Plays7 months ago
In this week’s episode of the PolicyViz Podcast, I interview Rahul Bhargava from Northeastern University on the topic of data physicalization. We discuss the role of community engagement and societal impact in communicating data and including different people and communities. Our conversation touches upon teaching combined majors at Northeastern and expanding data engagement through Rahul’s participatory art methods. We explore the limitations of visual learning and advocate for including diverse voices via data sculptures and embodied experiences.
Keywords: data beyond the screen: sculpting community, screen sculpting community voices, zbrush, sculpting community voices with rahul bhargava, jon schwabish, data visualization, tableau, flourish, bar graph, flourish data visualization tool, bar chart race, Rahul Bhargava, rahul bhargava, dr rahul bhargava, fortis, bone marrow, blender sculpting tutorial, screen sculpting community, zbrush sculpting, how to sculpt in blender, community voices, meow wolf denver, sculpting community, mathematics, Al, machine learning
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Transcript
Introduction to Data Physicalization
00:00:13
Speaker
Welcome back to the policy of this podcast. I'm your host, John Schwabisch. On this week's episode of the show, we talk about something I've been keenly interested in over the last six or seven months, which is data physicalization, which is a big jumble of consonants and vowels. But basically it's thinking about creating and working with data beyond the screen in the analog world. So working with popsicle sticks or Legos or blocks or post-it notes.
00:00:39
Speaker
And to help me better understand this work and what it means to work with communities and people and groups beyond the computer is Rahul Bhargava from Northeastern University who runs his own lab at Northeastern and works in this physical world with different people and communities to help them
00:00:59
Speaker
own their own data and to help them understand the world around them. And so I think what you're going to hear in this conversation is not only how to think about bringing data visualization into the physical space, which I think is interesting on its own, but also I think what you're going to hear and what you're going to learn about
00:01:18
Speaker
is how to engage with communities and how to engage with communities in this space of working with collecting and analyzing
Community Engagement through Data
00:01:26
Speaker
data. So I think you're really going to enjoy this week's episode of the podcast. It is sort of pulling together a string of conversations I've done over the last six months, first with the editorial team from the book Making with Data a couple of weeks ago from
00:01:42
Speaker
Deepmar Ulfenhuber, who wrote the book Autographic Design, and now with Rahul Bhargava from Northeastern. So I think there's a nice thread here about this type of way of thinking about and working with data. So no more talk for me. Let's head over to the conversation between me and Rahul. Hey Rahul, how are you? Good to see you again. It's been months since I saw you in the summertime.
00:02:07
Speaker
Yeah, yeah, yeah, it's a different year. It is a different year. I wish it seemed longer than it is. Yeah, right, yeah. Well, really good to see you again. I'm excited to chat with you about your work.
00:02:18
Speaker
I actually got inspired by seeing your talk at the conference we were at in, in Maine in the summertime. And then you actually did like a kind of real time data physicalization project, like just kind of like spur of the moment, which was very, I thought it was very cool to sort of like, you're just like, Hey, we're going to have this break and yeah, we're going to do this thing. So, so I thought maybe you could tell folks about your background, the work that you do, and then, and then the work that you do at your lab at Northeastern.
00:02:46
Speaker
to get us started. Sure. Yeah. So happy to share my I actually am what I call a recovering computer scientist. So I trained in technology, but then I've spent a lot of years trying to unlearn some of those lessons, particularly around like ethics and impact in society and those sorts of things. So since then I ventured over into into education and robotics and things like that. And I ended up I think like a lot of people of my generation who sort of cut their teeth
00:03:16
Speaker
in industry in the early 2000s, sort of stumbled on data science accidentally in a time where there weren't really programs that would help you learn how to work with data. And that's totally different now, but I think a lot of people of my generation, that's the origin story of how they came into working with data in a very computational, quantitative kind of way. So I didn't come at it from the design side, I came at it from that computation side.
00:03:44
Speaker
Now I am a professor at Northeastern in journalism and art and design, which is totally different than when I started. And speaks, I think, to how I like to think about the intersections between things being where the fun is.
From Computer Science to Art: Bhargava's Journey
00:04:01
Speaker
So I am a professor here at Northeastern. I get to teach students. I do a bunch of research and I run a lab called the Data Culture Group.
00:04:08
Speaker
which really is just a home to ask the kind of questions I'm interested in that are about how we can, as we shift more to a data-fied society with data impacting civic decision-making, company organizational stuff, all sorts of pieces of our lives.
00:04:26
Speaker
are now touched by information and build around it. How can we change who gets a seat at the table when that happens? When it happens with a spreadsheet and a computer, a certain set of people show up and feel comfortable. And that is fine. But that leaves out a whole set of other people. And I think that's problematic. And we can talk some more about why. So I try to create a bigger toolbox
00:04:50
Speaker
for bringing people together around data in new ways. And that includes lots of art space, participatory, things we can talk about like data murals and sculpture and theater and things like that. Yeah. Are you teaching journalism students and design students and computer science students? Exactly. One of the funny things about Northeastern is that they have these combined majors where it's, it's not a double major, but it's actually a student that is
00:05:20
Speaker
getting a degree in computer science and journalism. And that's what their degree says. So like I mentioned, they're at the intersection. So I, in my classes, I get a lot of students that might be data science and journalism or interaction design and data science or, you know, mechanical engineering and graphic design, things like that. Right.
Arts-based Methods in Data Engagement
00:05:44
Speaker
Right. So the cross cutting of all those different areas. Um, well, let's talk about the data physicalization stuff you do. Um, from my reading of your work, it really spans a huge breadth of types of projects from the big, uh, I have in mind that the table, you weld it together, I think with your wife, right? Of the silverware all the way to more of these participatory projects. So I don't know where you'd like to like to start, but maybe, maybe you could help.
00:06:14
Speaker
give people a sense of the types of things that you do. Yeah, sure. So I think maybe starting with what people know, data visualization, right? Uses the, like what we can see to represent data. And I think we tend to privilege sight as a sense, just in society in general. We focus more on what we can see than sort of what we can taste, what we can hear, what we can smell, what we can touch.
00:06:41
Speaker
And I just think that's limiting, um, not just to everyone, but also to people that learn different ways. Um, so one of my approaches has been to say, Hey, what if we just broaden that spectrum? Um, and, uh, yeah, I still work in the visual, um, but, uh, with the, the physicalization is a word you used. And I tend to think about that meaning encoding data onto the physical attributes of some object in 3d and.
00:07:10
Speaker
I find that word hard to say, so I tend to split it up, just physicalization. It's just a lot of syllables for me. And I get the parallel to visualization, but I tend to split that into different buckets, partially so it's easier to say, partially so it's easier to think about. One bucket is what I call data sculptures, and that's where the data is encoded into some physical object that is outside of yourself. So the naive or the simple version is like,
00:07:40
Speaker
uh, say a 3d bar chart made out of boxes. Right. And I can speak to, there's more creative versions, like, you know, the table that, um, that the welded table made out of 1700 pieces of cutlery, which by the way, my wife did the majority of because my welding sucks. All the pieces that fell off. Those are yours. Just to be clear. All right. So sculptures is one bucket.
00:08:05
Speaker
Another bucket is embodiment. So that's where you're using your body to represent data in some way in cooperation or collaboration with other bodies. And that's where I'm doing a bunch of work on data theater and using participatory practices from the long, rich history of theater as a way to bring people together to talk about civic issues and imagine alternatives.
Theatrical Data Presentations
00:08:28
Speaker
So I like to split that bucket up, but that's just kind of like
00:08:31
Speaker
how I start to think about that longer term physicalization. And then the goals, of course, are for me, are really focused on who gets that seat at the table and how can we build new ways to offer it to them.
00:08:43
Speaker
Right. I'm curious about the, the embodiment. So is that, can you give an example? Is this like taking a group of people and, you know, having them stand in certain areas is there, are they raising signs? What does that look like? Well, the simplest version, I mean, people were doing this stuff in the seventies. They were doing like a living histograms, they call them. So imagine you're standing up on a ladder and you're asked a group of people to line up based on height and you say, use like, use like three inch buckets or something.
00:09:11
Speaker
And you can just imagine them lining up in rows based on height. And they, they called that a living histogram. And again, that's like a very simple way to imagine people using their bodies. Cause if you imagine doing that, you're going to remember where you are and the people around you far more strongly than if you saw a picture of it, right? Because there's all this research into how we learn with our bodies. There's words like embodied learning, somatic learning, there's
00:09:40
Speaker
lots of different research that gets into why and how that works. But the simplest is just to think about any sport you play, like muscle memory, it just works differently than the rationalized or abstracted way of knowing things. Right. So it builds on that kind of embodied understanding. Right. And so you're doing this in, uh, in civic meetings with, with community groups and non-profits and CBO's. That's where we're getting to. I'm partnering with a team for about
00:10:09
Speaker
three years now, I've been working on the idea of what would it mean to use the lever of theatrical performance, right? So pushing beyond that idea of just lining people up to say, Hey, what if we actually give a set of people in this case, actors, or dramaturgs, people who work with source material, and bring it into some scene, give them information about some issue, some civic issue. In the current case, it's about
00:10:35
Speaker
green space planning and displacement in Boston. So very like relevant civic issue for lots of places. How do we give them something and then have them synthesize and come up with a performance of that data, which they use all of their dramatic skills for, and then have a debrief conversation afterwards that responds to that.
00:10:59
Speaker
So just take a step back from that description. Usually in the US, in my area, certainly the way civic meetings happen is there's like an evening meeting that you get invited to that, you know, it's probably hard to attend if you have two jobs or kids, but then there's a, if you go, there's like a big room with chairs and there's a, there's a projector and there's like, you know, usually like some city official and wearing nice clothes, that's like sharing things. And maybe there's like one or two things where you can like,
00:11:28
Speaker
put up sticky notes or like they ask some questions. And then there's like a people, then there's like the three people who always ask tons of questions. Like that's what a civic meeting looks like in our area. Now, like I already mentioned, there's lots of people left out of that, not just because of the logistics, but also the format. So we're saying, all right, contrast that to the idea of a 20 minute theatrical performance that maybe you know the people in it.
00:11:58
Speaker
and then breakout conversations that react to the performance for 20 minutes afterwards. It's just a very different invitation. It's probably in a different place, a place maybe you're more familiar with, like a library or something like that, or a theater. It's probably easier to get people to show up because they don't want to see a play, not a meeting. So this is the hypothesis. So we're working towards that.
00:12:28
Speaker
And we just did, actually just this week, we did our third round with a wonderful group of youth here at the Hyde Square Task Force. So a group of youth in a lower socioeconomic status part of the city, we do a ton of activism, and they brought their theater experience and wisdom to working on some data about a local park that is a green space in that part of the city.
Empowerment through Data Literacy
00:12:51
Speaker
and how and why it's used and what it's for. It is mostly youth of color. And they were responding and working with survey data, which had been mostly filled out by people that were white and affluent in the area. So it's a really interesting mix. And we're working towards creating that alternate model to then do a comparison. That's a long way of saying it.
00:13:11
Speaker
Yeah, well, it's interesting too, because there's kind of two different approaches in some ways, right? Or two different means, because on the theater one, if I'm the attendee, I view this performance and then I get to interact with people. But in the second one, I'm really part of the data
00:13:32
Speaker
visualization for lack of a better word, right? There's more participation in there. So how do you think about the difference between these two of the sort of more of the exhibit versus the participation? Yeah, that's a great question.
00:13:44
Speaker
I tend to argue that if you think about a data pipeline, right, people draw them in different ways, triangles, hexagons, lines, circles, whatever. They often move from like asking some questions to producing or gathering some data to finding a story to like creating that story and then sharing it with an audience. I think each of those is a point for participation, whether it's participatory data collection,
00:14:10
Speaker
or participatory analysis, brainstorming on stories, doing collective creation of an artifact, or having like a hosting, like a launch party. And that isn't to say that every project needs to engage each of those. But to do what, there was a training I took years ago from a local group called the Interaction Institute for Social Change. And they had this wonderful concept of a maximal appropriate level of participation. So just to pull that apart,
00:14:40
Speaker
as much engagement with the group of people you're working with as is right for the project. What's a simple way to think about that? Well, not every decision needs to be made with consensus, but some should. So that's a simple way to think about that idea. I think about data projects very similarly. What's the right level of participation at which point
00:15:01
Speaker
for this particular project with these particular partners. And it looks very different in different ways. So some, it's about getting them to be at the data sharing events. And that's where you engage them. For others, the whole output, the whole desired outcomes are about the process, not what you make at the end. So it's about engaging people in the process.
00:15:22
Speaker
Those are some first thoughts about like that difference. Yeah. Yeah. And when you are working with your students in the lab, what, what, what does that process look like? What are the different, um, I mean, you're working with students, so you're not necessarily hiring people, but if you know, what are the sort of different skill sets that you're looking for? Um, when you are trying to build some of these things or pull it together, you know,
00:15:49
Speaker
The main thing I pull from is from my experience after I was sort of ran away from computer science a little bit. I did, well, I graduated basically with robotics experience. And at the time, I think if you remember back 25 years, the only robotics jobs you could get were robots that blew things up. And I was like, okay, I don't really want to blow things up. So what can I do with these skills? And so I went into robotics and education.
00:16:15
Speaker
So I lean on my education experience and I would say the most useful skill set is around activity design. So if you have a set of goals, how do you create an experience and activity that invites people in with appropriate media, like a thing to work with to help achieve that goal with that group of people? So that's a skill set I look for really, like that's a really, it takes a thoughtful person that can be in an experience and step out of it.
00:16:44
Speaker
And think about the settings and the context. Those are the skills that I look for and that I try to practice with myself to get better at the most. So that's one thing that I think is different with this kind of work because usually it's not like, like, yes, you need to be nimble and facile with data and information. And you need to have like cultural knowledge about how to move in different settings or be aware of your own limitations of the person. But when you're doing participatory work,
00:17:11
Speaker
with communities, all of the work I do is with community groups, then that's, I think, the main one that I focus on, actually. Yeah. So when you are working on a project, you're working, let's just say, with a community group, and you're going to create, let's just say, let's just take this community meeting, I think is a good way for me to think about it. At this community meeting, you're going to have them work with some data. We'll just keep it simple. They're going to work with some data to create something.
00:17:42
Speaker
In your mind, how do you think about that? What are your steps? Are you thinking about here's this community, here's what we need to know about the people in the room? Do you think about what graph we want to end up with, what data, what tactile pens or paper or Legos they're going to have? Walk us through your thought process.
00:18:03
Speaker
Yeah, all of those. So almost always I'm invited in or like I have a collaboration with some key stakeholder, right? So there's a set of, there's a small set of people that are of or have trust in the community that I'm working with.
00:18:18
Speaker
And with them, we come up with a plan about what are your goals? Like, what is the point of this thing we're doing? And they might say like, Hey, we got funded to do this data thing. And now we need to like disseminate it. And we want to do that in a way that matches our nonprofit way of working. That's about empowerment and justice. So help us catch up. And I'm like, Okay, cool. Or it might be to say, Oh, you know, we're, we're engaging a group of people that we want to survey to produce some data about the community. How can we do that in a way that doesn't feel extractive? Yeah.
00:18:48
Speaker
So that's when we started to say, all right, what can make people feel comfortable? Is it about having the craft materials that make them feel comfortable, right? Is it about having someone that looks like them be the one leading the session? So then that's when you match the toolbox with the goals, and then we come up with a plan.
00:19:18
Speaker
And that might look like, you know, using those craft materials or, you know, or Legos to make a data sculpture. Who knows, you know, or it might look like, um, it might look like brainstorming questions together in reaction to like a one page data handout. You know, it has some, some, a couple of tiny tables and a bar chart. Who knows? Um, so that's some of the concreteness of what it can look like in a room. Yeah. Yeah.
00:19:48
Speaker
It very seldom, it might be a giant paper spreadsheet on the wall that they're writing information on. Very seldom looks like giving them like a tablet computer and like having them look at data on a device. That seldom aligns with the type of work I'm doing. I'm not saying that's necessarily bad. It's just not where I point my energies. Other people are doing stuff like that. Other people are working on technical skill development. Sometimes the goal of my work is data literacy building.
00:20:18
Speaker
But it's not literacy in that way. It's not computational literacy because computers don't help with things like asking a good question, right? Like that's not something they're good at. So that's something that people are good at. So I tend to focus on those things to complement other work.
00:20:35
Speaker
Right. You mentioned at the very start how site is kind of limiting in lots of different ways and excludes the broad swath of people. When you are starting out your exploration with a community group,
00:20:51
Speaker
What are your conversations like? You've already mentioned different communities of color and levels of affluence, but when you start talking about sight versus certain types of disabilities, what are those conversations like? I assume you can't have this big poster board on the wall if you're working with people who can't move around as nimbly as my 14-year-old son.
00:21:16
Speaker
Yeah. And I'll say straight out of the bat, that's actually not a focus of my work. So I haven't been leaning in those directions intentionally. It comes up because I work with regular communities, and communities are made up of people with different abilities. So I don't proclaim to have expertise there. The thing I will say is that fun is a major design principle for me. It's a very important one. And one way to get people to have fun
00:21:42
Speaker
is to do something together. And maybe it's cooking, like the wonderful data cuisine work. Maybe it's drawing a picture, sketching. Maybe it's building with craft materials. Maybe it's taking a walk. Those are all ways to have fun. And each of those can engage with data. And why? Not because data is like this amazing thing. Data is useful. But it's because data is like a key to power and empowerment.
00:22:10
Speaker
In these sorts of settings where now data is being used to bring people together to make decisions, if you don't speak the language of data, where it's not presented in a language you understand, in a way you understand, then you are actively disengaged and disempowered. And that's troubling for me. So what I want to do is help say, hey, look, there's a language of power.
00:22:32
Speaker
Right? And you could make the argument about reading literacy is the same thing, right? You need this literacy to access the doorways of power, which are, by the way, like reading literacy, typically closed on purpose, right? Like, oh, no, you know, women aren't allowed to read, you know, formerly enslaved people in the US cannot be taught to read. So there's a history of things that we call literacies being used to shut the door to access to power. So
00:23:03
Speaker
I see that and I say, Oh, well, data is, is like a new tool that has carries a rhetorical power of truth. Right. It looks important and truthy. And there's a lot of history around that. But, um, so how do we help more people access that?
00:23:18
Speaker
So we can open that doorway. That's a really big deal to me. So that's a long way of getting back to your question. I'm like, okay, I want to have that, that gets us back to questions of access and accessibility. So, so then like, then we say, all right, well, what are the capabilities and assets in this community? Right. Not the, not that deficit framework, but like, what can we build on in this community? And man, if it's empanadas, let's go with empanadas, you know, like, what can we do with this? So like, you just got to work with what you have.
00:23:48
Speaker
And I usually, when I'm working internationally, I usually like, I'll get to the place a day early and like go around the market with people and just try to learn what I can about the media that makes sense to people there. And whether it's smell or touch or materials, I try to sort of at least be cognizant that it exists, aware that I won't become an expert in it in a day. But like, I try to, that's something that's deeply important to me. Yeah.
Cultural Humility in International Projects
00:24:14
Speaker
Have you ever had an experience where you've gone internationally or wherever to a place outside New England with an idea in your mind of what an activity might look like and then you get there and have this experience in a day and you're like, no, that's not going to work at all. I have to totally redesign what you had in mind. That's like every single occasion.
00:24:39
Speaker
To a certain extent, that's why I have, I always make sure that I'm not parachuting somewhere. That there is a local person that I at least know we're going to introduce to that can pull me to the side, smack me upside the head and say, hey, look, this is what that means here. So in those settings, I have to carry a deep cultural humility to understand. There are certain set of cultures I feel comfortable in and know well.
00:25:08
Speaker
And there's a large that I do not. So how do you work in those environments? I think you, I specifically go in with a plan and then I'm just, I'm constantly thinking on my feet, which again, training as a teacher or facilitator helps when you're bringing people together around data. You can't just roll in with your data science muscle. You have to roll in with your on bringing people together muscle. Right. And, and that to me is, is, is.
00:25:37
Speaker
super critical and reading the room is a big part of that. Yeah. I want to ask on the side, I was really fascinated by this idea of the participation at each stage of the data workflow, whatever, however we want to call it. But I'm curious on the, on the data, I guess, collection or data sharing piece of that workflow, if you're working with a group and they are providing their own data to participate in this group, how did you think about
00:26:07
Speaker
You know, data, I'm going to put that in quotes because it's kind of loose and that maybe that sense but like data privacy data security when you're in this in this group. Do you think of it like oh we're in this closed group this is a closed room this lives here only. Yes, so how do you how do you wrestle with that. Yeah, that's a really good question.
00:26:27
Speaker
So the first step is just to recognize the historical norms that people are used to in the room. Data is usually extracted from a set of people by someone else who has more power. And by power, I mean the ability to make decisions about yourself and people around you. It's usually extracted from someone that
00:26:52
Speaker
to buy someone else and then seldom looped back to engage them on it. So just by bringing data that was produced with a community back to the community, you're already resetting a power balance. And I think that's really important because then they're put in control of decisions about privacy and impact.
00:27:13
Speaker
And that, I think there's great examples of that in, you know, in the development world where people are rethinking that like colonialist extraction for like working in, you know, places that you would imagine, like Sub-Saharan Africa and things like that. We have nice examples of switching that script, but it's still the norm. And it's not always bad to like survey someone and then analyze it, but like there's just a reduct, there's like a, there's a disempowering history there. So coming back to your question, uh,
00:27:41
Speaker
I try to engage that with the room because culturally people have very different expectations around privacy. And I come at it from like a Western context that is sometimes totally different than, and I run into that in the US as well, right? When I go into different cultures in the US, whether it be like an organization that works with
00:28:04
Speaker
with Latino populations across, you know, some city or like a room of that's all like black organizations. The norms are different. So it's a similar thing about like, how do we negotiate this together? Right. And like working with communities and people that aren't computers takes longer.
00:28:26
Speaker
Yeah, it just takes longer. So you have to accommodate that. And we, we tend to focus on speed as a metric of success. And that is a very poor metric of success when you're doing participatory work. Right.
00:28:43
Speaker
It's actually often a metric of failure. If you did something too fast, you probably didn't build the relationship that you need. So I know that sometimes I veer away from talking about data in the sense that data visualization thinks about it. These are the things that matter when you're working with information in real settings. So it's easier to put the blinders on and just stare at the computer. But once you take them off, you've got to be like, oh, I have to wrestle with all these things.
00:29:13
Speaker
Right. I want to just finish up with asking you about some of your favorite projects. If you have a few that sit in your head, either because they were meaningful to you or they're meaningful to the community, or they're just hella a lot of fun to make. What are some of the ones that for you are like your most fun ones? Well, I'll use one that has brought me a lot of joy in different ways. So the table that we discussed a little bit, just to briefly describe
00:29:43
Speaker
My wife and I coming out or in the pandemic decided to, we work a lot on food security, like you do. And we decided to make a piece that spoke to the number of people that were fighting against it in Massachusetts. So we found this number that almost, it was 1659, almost 1,700 people, households every day were applying for SNAP benefits in Massachusetts during the peak of the pandemic. So they were saying, hey, I need help with food.
00:30:13
Speaker
My household needs help with food. Every day, 1700 blew me away because it's a conceivable number, but it's still too big. So we collected from our friends and community, 1700, almost pieces of cutlery and welded them into this full-sized table. And the reason I want to talk about it is because then we took it places.
00:30:38
Speaker
We took it to a farmer's market where, you know, a guy, we would talk to somebody like, what's up with this table? And we'd be like, oh, this is what it is. And he would say, oh, can I apply for that? And then we would send them over to the table where he could apply. We took it to an art gallery where they said, oh, this is a real problem in other places. And we said, no, no, it's a problem here. Here's a QR code that links you to organizations where you can help. Those experiences to me speak to the power of
00:31:06
Speaker
alternate forms of data, because the arts sort of helps you ask questions. And this, this big physical table, you can touch and feel, and as a form of a table, it uses all these metaphors, material metaphors, right, about the things that they're made out of.
00:31:23
Speaker
to get you into what is really a data story, right? It's about numbers. And then we have a videos that engage with it and like a data report that looks more traditional. All those things are linked to. So that piece I think is a, for me, it has had more legs than I would have thought. We were, it was touring for like two years. And I did not think it had that many legs. And that to me is interesting. It pushed me to think, it continues to push me to think harder, plus it was,
00:31:53
Speaker
fun to make, fun to show and like brings me a lot of joy to feel like, hey, we're engaging people on something that's hard to talk about because you talk to people and it's hard to talk about some of their backgrounds. They're like, oh yeah, we had times when my mother where we didn't have enough food, you know, and like they get into it.
00:32:11
Speaker
So anyway, that's one piece. Um, another example is a lot of the work that my students do in class. So in various settings, when I, when I'm in like a data storytelling class that I'm teaching, they expect to be producing charts and graphs and maps. Yeah. And then I say things like, Oh, you need to make something edible. And they're like, what?
00:32:34
Speaker
And then they go and produce this group of students to this amazing piece where they made smoothies. I made a video of themselves with these smoothies where the, it was like a breakfast smoothie, right? And the amount of kale, they started with CO2 emissions. The amount of kale in each smoothie was based on the amount of carbon dioxide emissions in each country. So they had four smoothies and they had like, you know, each one had increasing amount of kale.
00:33:04
Speaker
Okay. Kale smoothie. It's good for you. Yeah. Like first one, I don't remember what it was. It was something like European country and they're like, Ooh, this is a good smoothie. And they get to the last one and they're drinking. It's like, Oh my God, this is terrible. Yeah. And like, you can, you can feel, you feel it. Yeah. Yeah. You experience it. Right. Yeah. Right. And that's exactly
Innovative Student Projects in Data Presentation
00:33:27
Speaker
it. That way is no, that's a different way of knowing the data. You watch someone perform it.
00:33:32
Speaker
And you viewed that as a viewer of it. And I think I love those examples where like they come up with some, I never would have, and they never would have just cause I pushed them a little bit and they come up with this other way to experience the data. And I think we just need more examples of that. So I'm trying to like, you know, this, this year I'm the data storytelling class I'm, I'm teaching, they're going to produce a video piece at the end. That's like a TV news bit. That's about bringing people together around data.
00:33:59
Speaker
I found one or two examples from like morning shows in the US. I think we need more examples that like just show that there's a bigger toolbox. Yeah. Yeah. Then just your line charts, bar charts, whatever's on the screen on your phone all the time. Yeah. Yeah. And especially because there's all this great research in psychology that starts to say that when we use those forms, people think we're right. Right. When we show people a bar chart, people are like, Oh yeah. Okay. That makes a lot of sense.
00:34:29
Speaker
But they don't ask as many questions as when, even if you just draw that same bar chart by hand, then they ask more questions. And in so many settings, you want people to ask questions and engage with you, not have them nod your head. If you're in front of your board meeting and you want more money, go ahead, show them a bar chart. You want to let them nod their head. But if you're in any other community setting, you want people to ask questions and engage. So we have to use media forms and processes that are appropriate.
00:34:59
Speaker
Yeah. Well, it's great work. I'm super excited about it. I just enjoy the website for your lab. And yeah, thanks so much for coming on the show. I hope you have a great semester and I'll look forward to watching those projects come out. Thank you for having me. It's so fun to talk about this stuff.
00:35:18
Speaker
Thanks for tuning in to this week's episode of the show. I hope you enjoyed that. I hope you'll check out the website for Reheals Lab. I hope you'll check out the PolicyViz site where I have lots of other resources and a couple of blog posts on my own about this process of working with data in the physical world.
00:35:33
Speaker
While you're at it, take a moment, if you wouldn't mind, to leave a rating or a review for this podcast on your favorite podcast provider. Be it iTunes, Spotify, Google Music, wherever you listen to this podcast, if you could leave a rating, leave a review. I'd really appreciate it. It helps me reach out to your guests and helps more people learn about the exciting world of data visualization. So until next time, this has been the policy of this podcast. Thanks so much for listening.
00:36:01
Speaker
A number of people help bring you the PolicyViz podcast. Music is provided by the NRIs. Audio editing is provided by Ken Skaggs. Design and promotion is created with assistance from Sharon Satsuki-Ramirez. And each episode is transcribed by Jenny Transcription Services. If you'd like to help support the podcast, please share it and review it on iTunes, Stitcher, Spotify, YouTube, or wherever you get your podcasts.
00:36:22
Speaker
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