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Mapping the Invisible: Inside the Atlas of Macroscopes
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Welcome back to the show! This week, I sit down with three co-authors of the Atlas of Macroscopes—Katy Borner, Elizabeth Record, and Todd Theriault from the Cyberinfrastructure for Network Science Center at Indiana University—to explore what a macroscope actually is and how it differs from a standard interactive visualization. We trace the 20-year journey of the Places and Spaces: Mapping Science exhibit, from two-dimensional wall maps to the 40 richly interactive pieces featured in this stunning 11×14-inch MIT Press book. Along the way, we talk about design strategies for making complex systems legible to general audiences, the role of AI in data visualization, and what it takes to grab and hold attention on a museum floor. Each guest shares a personal favorite from the book—ranging from Smelly Maps to an Appalachian opioid overdose tool to a skills-landscape explorer—and we close with a look at the exhibit’s exciting third decade, focused on visualizing intelligences.
Keywords
data visualization, macroscope, atlas of macroscopes, interactive visualization, Katy Borner, Indiana University, Places and Spaces, complex systems, information visualization, scrollytelling, AI and data visualization, opioid epidemic mapping, data communication, science exhibit, data science podcast
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Find the Atlas of Macroscopes and explore the Places and Spaces exhibit at scimaps.org. Follow Katy Borner, Elizabeth Record, and Todd Theriault through Indiana University’s CNS Center.
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Email: jon@policyviz.com
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Transcript
Podcast Introduction and Website Updates
00:00:12
Speaker
Welcome back to the PolicyViz Podcast. I'm your host, John Schwabisch. I hope you and your friends and family are well these days. Things have been busy over here. On my end, I relaunched the policyviz.com website, new theme, new security features, streamlined it, cleaned up a lot of the pages and the site so it's a little bit easier to navigate and to use.
00:00:34
Speaker
That was a major undertaking, but thanks to our new friends and these AI tools, it made my life a little bit easier to clean all this stuff up. Fortunately, I also have a bunch of friends who are web developers, so asking them some questions was also super helpful. So thanks, they know who they are.
New Tools on PolicyViz Website
00:00:48
Speaker
I've also been launching a bunch of new tools on my website, ah many of them built with the help of Claude. I've really been getting into the Claude code these days. I've built a little budget simulator. I've built a social security benefit calculator. And probably the one that I'm most excited about is the data visualization style guide builder, which will enable you and your teams to import or input your colors, your fonts, and then select from a menu of sections of your style guide, even rearrange them and then export everything as a Word doc, as a text file, as an HTML document. It was really a lot of fun to build it, and it's something that I've wanted to get off the ground for a long time. And now with Cloud Code, I was really able to do that because I had all the content in my head, just didn't know how to pull it all together into a web app. So there's that over at the PolicyViz website if you want to check it out. I hope you'll put that to good work to help you create your own data visualization style guide, either for yourself or for your organization and your teams.
Introducing 'Atlas of Macroscopes' Authors
00:01:44
Speaker
As for the podcast, on this week's episode of the show, I am joined by the three authors of the new big book, physically a big book, and you'll hear us talk about that in the episode. I'm joined by Cotty Borner, Elizabeth Record, and Todd Turow, the authors of the new book,
00:02:00
Speaker
Atlas of macroscopes. um If you don't know what that is, this is
Understanding Macroscopes and AI's Role
00:02:04
Speaker
the episode for you. You want to check this out. You want to learn more about what macroscopes are, the differences between macroscopes and interactive visualizations, where this whole project started from and its evolution over time. And of course, no conversation would be complete these days without talking about AI and its potential impacts on the field. So we obviously spent some time talking about that.
00:02:24
Speaker
So I hope you will enjoy the show. Hope you learn a lot. I hope you'll take a look at the new physically large, but also impressively good book, Atlas of Macroscopes, content information interviews. You can only get here on the policy biz podcast.
00:02:40
Speaker
Okay, I've got the full team. This is very exciting. i mean, not often do I get like a big crowd for a podcast. Elizabeth, Cotty, Todd, thanks so much for coming on the show. I'm excited to talk about ah your book and your work, um but let's start with let's start with introductions. So folks know who they're listening to or watching if they're on YouTube, but you know probably listening. um Why don't we start with Elizabeth and we can go around the room.
00:03:07
Speaker
So hi, I'm Elizabeth Record. I have worked with my colleagues here, Cottey Borner and Todd Theriel, for over 10 years now curating the Places and Spaces Mapping Science exhibit, um which is an outreach activity of the Cyber Infrastructure for Network Science Center here at Indiana University.
00:03:29
Speaker
Yeah, and I'm Todd Theriault, and I, course, work with Elizabeth and Cotty at the CNS Center. I do a number of things there, but for the purpose of this podcast, I'm co-curator of the Places and Spaces exhibit and the new Envisioning Intelligences exhibit.
00:03:45
Speaker
I am Cotty Borna, Indiana University. I've been with Indiana University for 29 years now. True pleasure to be here. And as part of this all, we have been building data visualization tools and also advancing theory so that it's easier for anyone to ah render data into actionable knowledge. For the last two decades, I have been really fascinated by macroscopes as they give us illuminating and holistic views of our ever-changing world. And it's really a pleasure to be here with my co-authors to introduce the Atlas of Macroscopes to you.
00:04:23
Speaker
Very excited to have you all on the show, especially because you've got the Atlas of macroscopes t-shirts on, which, you know, I'm sure there's a, I'm sure there's a website out there somewhere where someone can get their own copy, but um so I want to start by getting into this term macroscope. So, so how do you define a macroscope and what separates it from just like a really good interactive visualization online?
00:04:47
Speaker
That's a good question. You're right. There's a lot of overlap. Um, macroscopes are interactive visualizations that serve as interfaces to data. Um, so in many cases we're dealing with large data sets. So more so than maybe just any interactive visualization would.
00:05:02
Speaker
Um, so yeah, you're right. There's a lot of overlap between the two, but where we see the difference is, um, that macroscopes help us view complex systems.
Development and Applications of Macroscopes
00:05:11
Speaker
at multiple scales. So they're in a sense, another type of a scope, like a microscope would help you look at things that are really small. A telescope would help you look at things that are far away. And a macroscope will help us see things that are complex, like a systems view that will let you get sort of a view of things that are maybe too large or too complex or too slow to look at with the naked eye.
00:05:36
Speaker
So that's um that's kind of how we differentiate the two. The term comes from Joel de Rosne, although it's been used in other areas for since the 1950s and different uses, but we we were inspired by Joel de Rosnay's definition who envisioned their use as tools for observing what is at once too great, too slow, and too complex for our eyes.
00:06:00
Speaker
They tend to focus on multi-factor and multi-modal data sets, um and they can be really powerful tools for looking at complex topics.
00:06:12
Speaker
So does it by definition require big data and or complex data or do you need both? ah That's a good question. I don't know how you would feel about that, Kati and Todd. I think we can I think it looks at all scales and levels. Microscopes also are oftentimes modular, so you can plug and play in new algorithms, new data sets, because new data sets and algorithms become available every single week.
00:06:37
Speaker
So that it needs to be extendable in order to be useful and actionable. And I think to answer that question, I think there can be a very small data set that's hugely important for making good decisions.
00:06:50
Speaker
And then there can be a really big complex data set that needs to be wrangled and ultimately made so that human beings can gain insights from it. I think we are typically made for local and rather short-term decision making. And what we want here is to empower many human beings to make more long-term and global decisions because that's what we are doing.
00:07:18
Speaker
Mm-hmm. Yeah. And I want to come to and a bit, come to where these pieces, at least the ones that that that are featured in the book, obviously there's many more, but the ones that are featured in the book, where they are situated because this sort of hyper-local, but this sort of big macro perspective is, I think, an interesting sort of dichotomy of how people interact with them versus what they are showing. But before we get to that, um You kind of can't have any conversation these days without, ah without talking about AI. So I wanted to talk about AI. So does AI change what a macroscope is? um Does it change how people build them? Does it change how people interact with them? Like it's exhausting to talk about AI all the time, but I think kind of feel like, you know, with these sorts of pieces, we kind of need to.
00:08:06
Speaker
Yeah, absolutely. And I think many of the macroscopes have been using advanced machine learning techniques to lay out the data, to wrangle the data, to have people interact with data in new ways, also need natural language interfaces. And then coming over to tool development, which the exhibit is also aiming to inspire and tool usage by anyone, anyone can map, we believe. um I think it's fascinating to see how you can use large language models today to create a data frame, to analyze and visualize that data, to optimize these data visualizations
00:08:42
Speaker
in a very natural non-programming based way. And so in the and data visualization course we teach here at IU to um about 100, 130 students every spring, we have a setup where students are actually asked to use large language models to do temporal, tropical, geospatial, and network analysis and visualizations, and to use natural language interfaces, prompt engineering,
00:09:07
Speaker
to optimize these data visualizations. And I think that's also what they will do when they go out in industry or if they continue research in academia or government. And so I think we absolutely need to embrace that new tool and technology and ultimately make it useful so that we still gain also our own knowledge and wisdom and make our own good decisions.
AI in Data Visualization Education
00:09:31
Speaker
but also ah have a better tool that One thing that's been um sort of knocking around in my brain lately is, you know, a few years ago, maybe like pre-pandemic, we had a lot of these big bespoke ah data visualizations, right? Interactive pieces. I think a lot that sort of like, you know, sort of similar to some of the pieces that are in in the macroscope, in the Atlas book. um And then it seemed that people moved, ah you know, clients and companies moved away from that and moved to more, you know, single graphs, maybe scrolly telling things were little bit more contained. um
00:10:06
Speaker
I wonder whether you think AI brings us back to more of these bigger, bespoke, creative visualizations, because the AI tools can whip up a dashboard,
00:10:20
Speaker
really quickly now. And, you know, maybe they're not as fancy or in-depth as someone, you know, someone who knows what they're doing in Tableau or Power BI. But, you know, for a basic dashboard, it could spin it up very quickly. And I'm i'm curious whether you whether you all think that we're maybe going to return to that kind of bespoke era of custom visualizations that have like a really creative bent to them.
00:10:40
Speaker
I know Todd has a lot to to say about scrolly stories. if We all three are fans of those. And I think it is a great way to introduce a data set of problem and then walk people through the different steps that it takes from the initial data, which oftentimes needs cleaning and analysis and modeling and more cleaning and more analysis and then having a final visualization that's actionable. So I think I'll leave that to Todd. But on the... um question can they help with complex data i think so we recently did a number of analysis of our website traffic and it was amazing to see how quickly dashboards come about how easily they can be at optimized and how actionable ultimately these dashboards are for optimizing website traffic for instance and so um yes absolutely again we should embrace that because then human usage
00:11:31
Speaker
and human intelligence can be used to spend time on how what traffic do you really want on a website, um especially an academic one where it's not just about getting to the entering your credit card information site.
00:11:45
Speaker
um And also in a learning environment, if you can now run learning analytics using large language models, that's huge because you can identify different learning cohorts. You can try to serve the needs of these different cohorts. and ultimately optimize um learning content on Canvas, for instance, or any other course development site to the needs of these different learners. So I'm super excited about the possibilities that now I'm seeing.
00:12:14
Speaker
Todd, do you have you have strong feelings on ScroolyTelly?
Designing Effective Macroscopes
00:12:17
Speaker
I like scrolly tally. I mean, um you know, I would say that one of the things that I like about the, that I think are really strong about the macroscopes is their commitment to openness and documenting. um And, and there's a pedagogical bent to them. Um,
00:12:34
Speaker
that encourages interaction and play and openness. And there's a link to get to the data set. There's a link to get to the a published paper. There's a link to get to the people made it if you want to contact them. um What I'm concerned about with AI is does that exist? Right. Can you find out where thats the information is being scraped from? um And ah I would i don't know, I like the ones that we have. I know that I will you know improve the process and and maybe improve the quality, but I would hope that it would also retain the transparency um that's part of this. Right.
00:13:15
Speaker
Yeah. So that leads to my next question, which is on where these many of these pieces were situated. So you just mentioned that you know for many of them, you can contact the author. You can download the data. There's a GitHub site. um But many of them seem to be designed for you know or placed in museums where people can actually physically stand there or play with them or do something with them and people who aren't necessarily data scientists, you know, computer scientists. um
00:13:45
Speaker
From your all's perspective, what's the what's the key design insight for making these complex systems, you know, legible, understandable to these more general audiences?
00:13:56
Speaker
Well, first, you know I think that it's important to recognize that the macroscopes that are in the book come out of the 10-year long exhibit, um which procured macroscopes and had ah a call for macroscopes every year. And they were submitted and we took them to our International Advisory Board, Exhibit Advisory Board, and we looked at them. And so there was a band to like which ones will make sense to the most people, you know, and which ones are intuitive and and highly interactive and will matter to people and not to just to select a select few. And so there is some selection bias to the microscopes that appear in the book because they are for that for that setting, although maybe not originally designed for that. um But, ah you know, I think there's a there's a scene in um from Friends. I don't know if you I haven't watched it, but I know this scene, so I know it must be somewhat famous where two of the characters are in London.
00:14:56
Speaker
and ah they want to get to Westminster Abbey and they can't find their way. So they pull out a paper map that shows how old friends is um and they're like ah and they look at it and they can't get orientation. One of them says, Oh, I know what I need to do. I need to get in the map.
00:15:11
Speaker
Right. And so he sets it down on the ground and stands in it and sort of like you can see i'm sort of being a little person in this map. This may may not be the tightest analogy, but I do think that um this is what I look for in a macroscope. Does it allow you to get in the map?
00:15:28
Speaker
Does it allow you to um access the things that concern you? Does it allow you to manipulate it so that it's useful to you? um And I think that the ones that we have in the book definitely do that and were chosen because of that. um As far as like making complex systems, I for me.
00:15:50
Speaker
I think I have to see where I am if if the complex systems are these these big gnarly networks that have intertwining lines and connections. I want to see where they overlap me first and the people that I care about in my community. And from there, then I can extrapolate from that and ever widening circles to sort of understand. um the the big picture of it. And so the other people may have a different approach that seems to work for me. I don't think I'm particularly selfish, but I think we're all a little bit, you know, self-concerned.
00:16:23
Speaker
We want to see where we fit into that. So um in terms of design features, I think, Things that encourage exploration. So if it takes having sort of curated interactivity at the beginning, sort of get you going or very extensive demo section or instructions to let you know um how to how to navigate your way through that. I think those are wonderful things like, you know, you scroll across it and it highlights the links and and especially like something that rewards exploration and doesn't penalize exploration. So not all the paths you go down are going to be interesting or enlightening to you. So can you reach that dead end and be like, okay, I want to go back and get back to where you started from?
00:17:09
Speaker
Is that easy to do? And so I think all those things to me make for kind of a successful way to explore these very you know complex systems to sort of take bites out of them. and And then pretty soon you start to see the interconnectedness of things building up from there.
00:17:26
Speaker
Right. So now I kind of feel like the little Google orange person icon in Google Maps should just be Joey Tribbiani. And that's what we should have all this time, right? Like, just have him run. Yeah. So let me ask about this community piece, because I think it is really important.
00:17:45
Speaker
Todd, you just kind of described it as making this big complex system very personal to people. um And do you think that that approach generally is something that visualization developers and designers should, I don't want to say strive for, because that's not really the cause it's not going work in all cases, but should think about you know situating the user within the data, whether it's a macroscope or or something you know maybe a little bit more contained?
00:18:14
Speaker
um whether they do that or not, I think that's how people will, especially if you're not an expert in it, that's where you're going to start, I think. um So yeah, I think that that's something that that should be, there at least has to be some some connection. um yeah The nice thing about the macroscopes is there's, no one's an expert in any of these things on this team. We have ah we have an exhibit advisory board, but it's great because it's wide ranging topics, but you need us to start somewhere. And we talked a lot about when we were ah writing the book about um the emotional pull, the affective nature.
00:18:48
Speaker
yeah And I think that kind of came out of the COVID-19 dashboards. you know that That was very experiences that were overloaded with emotions, you know, and probably a lot of people's first experiences with a macroscope, you know, ah with macroscopes. And so we thought about how that how that matters um to the making and the exhibiting of those things.
00:19:15
Speaker
so So maybe I should have started here, but let me pull back a
Atlas of Macroscopes Project Overview
00:19:19
Speaker
second. Can one oft you talk about this 10-year project and then how it, you know, you sort of win, what was the process of winnowing down to actually, I'm going to hold it up because I just feel like I have to.
00:19:33
Speaker
For folks who haven't held it, it's a very big book. it's This is not a seven by nine inch book. This this is this sits on the top of the bookshelf. um But what was the, can you talk a little bit about the this 10 year project and then how you sort of went through, the three of you presumably went through and sort of winnowed down?
00:19:54
Speaker
Well, I can say that it actually started as a 20-year project. really okay um Really, Cotty's brainchild. Which is weird, because we're all just like 25 years old. so We started when we were babies. Right, yeah. It's really strange. yeah But it does, it, it's this idea of wanting to present information visualizations and to showcase best examples and techniques that, um that are, you know, innovative or useful, or that are something that we might want to see happen further in information visualization. And so it started with 10 years of each year, a new iteration focused, you know, often on a different audience or a different question um and a call for submissions, much like um like a juried art exhibit.
00:20:45
Speaker
And then we went through pieces with um with a our advisory board, who are all information visualization specialists of one of one stripe or another, many different kinds, and then window those down to the selections for each year. So each year there's a new group of pieces added to the exhibit.
00:21:03
Speaker
After the first 10 years, we kind of looked at what we were doing and found that the pieces, the two dimensional pieces we were hanging on the wall with an object label kept saying, and for the rest of the story, go online. so, you know, it it became clear that this was happening in it and we needed interactive visualizations to be able to showcase what was happening in the field and where it was going and what was what the new innovations looked like. And so so at that point, we moved to 10 years of interactive visualizations. So the pieces that you see in this book are the ones that have been selected from that 10-year span as you know kind of the best of the best. So that's how we put them together.
00:21:45
Speaker
And did you all, plus the advisory board, was your when you started thinking about how do we winnow down 20 years of work, were you looking for a mix of content, a mix of modalities?
00:21:59
Speaker
Was it just like, these are the ones that we think are the best? like how how are you I mean, that's ah that's a big process to winnow down from a lot i mean a lot of work. Yeah. Yeah. um we We worked with with criteria as we as we judged submissions as they came in. Some were nominated, some were submitted by the macroscope maker teams. So we looked at, you know, how scientifically valid are they?
00:22:25
Speaker
We looked at how how applicable are they for public audiences? like Would they translate well to this to this mode of communication? um Let's see what What were other two? I'll have to pull them up. Do you remember what our other two criteria are, Cati and Todd? Yeah, it's definitely a scientific rigor, impact, and being actionable, and then also being relevant for a large audience.
00:22:51
Speaker
which not every single um um map or macroscope is. And then, right just to answer your question, so the initial Atlas trilogy has all 100 maps covered, which are all in the first decade of the exhibit. And then the book of macroscopes that you just held up, um that has all the 40 macroscopes that came together in the second decade of the exhibit. And if you do have time, there is a third decade also in the making, so we're happy to talk about that. But um for the macroscopes, they come in all forms and shapes and sizes, and they had to be harmonized so that they all can be explored and enjoyed on a kiosk. And then still, some of them are videos, some of them are scrolly stories, and others are interactive data visualizations. And um they are now all in a framework so that you can browse through all 40 and get sufficient information that you can have a good time with them and get actionable knowledge from them.
00:23:54
Speaker
Yeah. Now in now I just I just pulled out a ruler here because I i hadn't done this. So the book is 11 inches by 14 inches. So it's a it's a big book.
00:24:06
Speaker
And I'm guessing that that was a conscious decision on your part. ah Their macroscopes, which I think just the word to most people probably just evokes big and large. um Was that where your heads were at the beginning? were like, we're going to make this book, but it's got to be big.
00:24:20
Speaker
um Like just, it's got to be something that has some heft to it when you hold it It's just for workout, you know, especially if you have now all four address books. That's a really cool. Right.
00:24:36
Speaker
um So there are realities with MIT Press. um It's important that they can manufacture it, that they can ship it, that they can bring it into stores, that the stores actually can hold it in their bookshelves. And so there are serious realities there also that the book is affordable with about $30. It's a really nice Christmas present or other present.
00:24:57
Speaker
And ultimately we wanted to have these double page spreads, which just shows the beauty and richness and complexity of science and other data sets and, and really draw people in and for the, um, Atlas of macroscopes, it's not easy to pick static screenshots, right? All of these are interactive.
00:25:17
Speaker
And as we know, a picture is worth a thousand words. Now you have an interactive data visualization where you can identify what adventure you're going to take through that visualization. So right now we have to pick one of those many different adventures and make it work in a 2D environment and Todd did an amazing job there, and we also have videos which in many cases Todd created in close collaboration with the macroscope makers so that we can see what they pre prefer as an adventurite.
00:25:51
Speaker
Oh, right. Okay. I think the size also comes in handy just because um so trying to convey a sense sense of interactivity takes a lot of space. If you want to do it we worked with my wife is designed the book and these shirts, Tracy. And um so it was, I know she spent a lot of time thinking about how do you convey that through different scales of of images to sort of come out as though you were clicking on something. How do you, how do you convey going into and exploring more. So do you do like a series of images of what you would see? So there are a lot of challenges. And I think the size really helps in that because you can do a lot and then you don't sacrifice detail or legibility when you do that. So that that did that was a plus.
00:26:40
Speaker
Yeah. I mean, it's not like you pull out the little map and stand on it, right? You need the big map to the stand on it, right? Exactly. Exactly. Yeah. Yeah. um ah um Okay, so um so so back to the pieces. So do you find that there's a common mistake people make when they're trying to build macroscope, a big picture view? i mean, we talked a little bit about making it personal, making it the community, making it out.
00:27:08
Speaker
I'll say, i have no shame here, Todd. I'll say making about me first before I could learn about you. um but ah But, you know, maybe a more positive way to think about this question is why these 40 and why not some other ones? Like what makes these great?
00:27:27
Speaker
So every year there are many different macroscopes submitted to the exhibit, and then the exhibit advisory board judges them based on the criteria which are on the website.
00:27:38
Speaker
So scientific rigor or relevance, and also just aesthetic appeal ultimately also, because oftentimes the exhibit map and macroscopes, they are dis displayed on museum floors and libraries where there's a lot of other are things going on from rockets taking off or cute animals. um looking at you and why would you go to a data visualization instead of these two? So it's it's interesting how to attract and keep attention to these data visualizations. But then to your question of how to ah make people um not make mistakes when they're trying to understand complex long term trends and models.
00:28:19
Speaker
I think what you really need to keep in mind is how you get people in And then again, also how to keep their attention. And oftentimes it takes a bird's eye view of just understanding the data and what is it all about, then drawing them into the sprawley story or otherwise of how you take a subset of the data and
Features and Engagement in Macroscopes
00:28:39
Speaker
you create a reference map. And then you design data overlays, oftentimes many different data overlays that you can turn on and off and compare and correlate to each other.
00:28:49
Speaker
And then oftentimes you also want details on demand and you can because it's an interactive touch panel displays, or you can actually click on a data entry and get more information, which you couldn't do with the static maps. And so it's a deep understanding of what people really need to make better decisions in their life, and also an understanding of what algorithms now exist and what visual metaphors exist to draw people in and keep their interest.
00:29:19
Speaker
What is the fundamental difference, do you think, between grabbing and keeping people's attention in that museum space or that library space, the physical space versus online. i mean, it's the same.
00:29:33
Speaker
i mean, we're, we're all trying to do the same thing, right? Get attention and keep it. But, but is it the fact that people are are walking rather than sitting in front of their computer? Like what is that difference between the two experiences?
00:29:45
Speaker
I, it was interesting. I was putting together some photos for, of the first 10 years of people interacting with the science, the static science maps. And this is probably selection bias because they make good pictures, but a lot of them was someone showing someone else a point on the science map. Like, look at this detail. Sometimes they were touching it there was no prohibition against touching it, but I, I'm always like hesitant to tell it, but it's like, look, there it is. Um, and I don't think we were even conscious of it, but you know, like maybe in some sense we were, but this was a fundamental like move that people make in community, in a community of learners um that translates very well to, to macroscopes, interactive macroscopes. And so we had a 55 inch multi-touch screen initially where people could stand around it in groups and say, well well, I want to see this. I want to see this. And if you get stuck, maybe someone else can, can lead the way. And so,
00:30:44
Speaker
having that aspect of it, then COVID came and no one wanted to touch the same thing or be, so there were challenges, right? But um so I think that that understanding um the way that people interact with with each other as they interact with the works. And that's something that we can observe um by just standing by and watching how people work with these.
00:31:08
Speaker
Right, right. um I mean, it's just an interesting... I don't know, the just the modality is just different, right? the the Being able to be there, touch ah a screen. I mean, there's here in DC, then naa NASA has this huge wall screen downtown that you can go and watch and touch. And it's just, it is interesting for me to think about especially in this new era of AI, where I think lots of people are worried about what are computer science new graduates, I'm sure you're all all feeling this fear from your students. um what What are they going to be doing? um By the way, I think it's underwater welding. I think that's the that's the the key to the future. top the um
00:31:55
Speaker
But you know, is the more bespoke, is the more installation in a place where we're going to see changes in information visualization. I'll actually put that out there to you, see if you have any thoughts on on that. Or maybe generally, if you have thoughts on like what your students are seeing right now.
00:32:12
Speaker
um You know, I know that it's a different era suddenly. I do think it's worth saying that as we have looked at visualizations over the last 10, 15 years, and one thing that we have seen is that the groups of people who are building one of these macroscopes is larger now than it was initially. So these are team effort as opposed to an individual effort. And so I think having both the subject matter expertise of the content, I think that's critical to understand the data and to help navigate and find those insights that are, you know, really interesting or new or whatever to help guide you through the insights is a very important aspect.
00:32:57
Speaker
It's also really, it's not by accident that many of these macroscopes are easy to understand at the top level for someone who's a generalist. and also that they provide that depth of detail and information that holds the engagement of a specialist who's looking for more specific information. I think that that comes about as as a result of the expertise of people like UX designers and people who really understand interaction and are experts in that. And so we see teams that are including both
00:33:32
Speaker
both kinds of expertise to really get to something that is engaging, that works for a broad range of levels of expertise in terms of the user and, um, and that's engaging and that works well, it doesn't break, you don't get lost, um, all those kinds of things. So I think, I think acknowledging like the range of people in a team that make these macroscopes, um, really good is, is still important.
Collaborative Creation of Macroscopes
00:33:56
Speaker
And it's, I think that's a, that's a pretty big team, um,
00:34:01
Speaker
with a lot of different types of expertise. And those are all important and in building a really good macroscope. Right. And bringing their own um areas approach to these things. I mean, just thinking about publishing in different, you know, publishing in economics is very different than in computer science. I mean, it's always been extraordinary to me. And so everybody sort of brings their own professional, like, i don't want to call it baggage because that makes it negative, but what we call it baggage. But it is, it's hard to get outside of your yeah your jargon or your approach to a particular topic to imagine how someone who's new to that topic, where might they start and how how can you help them, give them the scaffolding they need to understand your topic. some It's hard to be a beginner in something that you're deeply immersed in.
00:34:47
Speaker
Right, right, for sure. um Okay, so I don't want to throw shade at any of the 40 in the book, but I did want to ask if each of you have a favorite or maybe two ah in in what was winnowed down from 20 years of work into into this book.
00:35:04
Speaker
So know I don't know, Elizabeth, maybe you want to start? you did you have a favorite? Sure. i am I will say i don't have a favorite. I love them all. So it's it's difficult, but I did want to highlight um that these are also fun. yeah And so one of one of the ones I wanted to highlight is um is one called Smelly Maps. which um which provides a smellscape, if you will, or a map of what's what different large urban cities smell like. um And one of the things that I think is really interesting about this one, besides the fact that it's fun to look at London and you can see, oh, here's, ah you know, what's all this animal smell? What's this about? Oh, that's the zoo, you know, or, oh, that's where they give carriage rides, you know, horse-drawn carriage rides. But,
00:35:52
Speaker
ah More than that, I think it's just it's a creative way of thinking about how can you take an experience that's not at its core digital, like smelling is not something that's necessarily digital, but how can you um do that in a digital way and map it? Like, how can you translate those sensory experiences into something that's quantitative?
00:36:12
Speaker
I think that was an interesting approach to that. Yeah, I love that. Todd, you have a favorite? Yeah, i've ah I think one of the ones that I really like, especially when um if people ask, you know, what impact do these macroscopes have? I think this one has an impact, and that's the Appalachian overdose mapping tool that came out during the the height of the opioid epidemic um and gives you... ah the Appalachian region, and then county by county mortality rates, but then overlaid on that are demographic data, economic data, um employment.
00:36:53
Speaker
ah So things that, and then some things that you might not expect that give you a sense of maybe how this problem, some of the the not so obvious causes of it, like, you know, is there internet access? um is Are there food deserts? Are there... um ah How are the highways? um You know, other things like that. How many people are involved in accident prone occupations? And I i really like that because I think it's it's got it is what like ah Elizabeth said earlier, just something that the generalists and the specialists can both get enormous amounts from. And I think it is something that would be invaluable for journalists seeking to to to write on the problem, ah of community organizers to the families at the front lines of the epidemic to wanting to understand. you know like
00:37:42
Speaker
So I think it's a very sobering and and it gives you just an overwhelming and an awful sense of the the scale of this of this problem and just the many challenges.
00:37:53
Speaker
in inter um interlacing causes for it, you know, the things that contribute to it. So I really, really like that one. It's not the most fun one, but I think it's very impactful to me. Yeah.
00:38:04
Speaker
Yeah. Yeah. No, that's great. Cati. Yeah, I find it super that we have such a diversity among the body. I think you get a really good overview of what works and what doesn't work and what you could potentially use for your own data. So we are very happy to connect um those which have data and questions to those which love to make macroscopes, the teams that are, for instance, included in the exhibit or also other teams. So feel free to contact us. um One map that you might like to start with or might like to explore if you're listening in
00:38:35
Speaker
is um the making sense of skills map. ah So here AI is used to create a skills taxonomy and then to plot the landscape of those skills that are growing in demand or are shrinking in demand for human labor. because of AI, robotics, automation moving in, for instance. But then also it's a two axis plot, right? So one is demand and the other one is salary.
00:38:59
Speaker
And it's not always the case that you want the job that's in high demand and has the highest salary. I think many of those jobs are very stressful and maybe you can't do them for long. But there are some interesting insights from that map. And of course, you could animate this over time and see it trends in the jobs landscape. And as a teacher, I need to understand and that landscape because I have to um train the next generation of students in engineering, data science, computer science, information science. And it's not easy to figure out what kind of forever skills I can give them and what kind of short-term skills are most useful for them to get the first job in the job market. And so these maps really can make a difference.
00:39:42
Speaker
Yeah. Wow. Three very different visualizations. um Okay, so ah so I think I have maybe maybe three questions kind of looking forward.
00:39:53
Speaker
um Is there a data set out there that you, or maybe multiple data sets that you wish someone would pull together into a macroscope? I will go first, by the way. um I would love someone to do something on the Artemis.
00:40:09
Speaker
And I know lots of people have done things, you know, showing the space flight, but but there's more there. And I would love to see like a really big story on whatever it is around the Artemis, maybe how we all came together for a hot second in time. um But are are there data sets or topics that you would love someone to, to this is this is kind of an ad for someone to like...
00:40:32
Speaker
really find a good topic, but are there things that you wish people would do? Well, I'm very curious to map and model the human body across scales. And there are many spatial scales and many temporal scales, 10 raised to the power of 10 for each of those. And it's super complicated to visualize this because because it's mostly coupled feedback cycles across these different scales.
00:40:55
Speaker
And I think that's just a super cool visualization challenge. And our team is working on it. There's many other teams that are also working on it. And I think you will see some of this in the future. Okay.
00:41:09
Speaker
Elizabeth, Todd, any datasets come to mind? Not so much forward-looking, but it does bring to mind one macroscope that's in it currently where someone said exactly that. They said, I'm so glad someone finally pulled these data sets together to use in this way. And that was the the New York City, the spatial New York City one, where New York City has a huge amount of open source data.
00:41:34
Speaker
and someone finally pulled it together and presented it in an accessible way. And so I think, um, there is the opportunity for that in almost any topic, you know, if you, if you dive into any topic deep enough, you find really interesting insights that, um, so I guess I would leave it wide open. I'm really excited to see what kinds of things come out in the next, you know, five years, 10 years, whatever. I think, I think there's no limit on what could provide interesting macroscope.
00:42:02
Speaker
If someone has data, and some interesting questions for it. I think you anything could be interesting given right the right focus. Yeah. Yeah. Todd, anything come to mind for you?
00:42:14
Speaker
I'm with ah Elizabeth on this. ah Yeah. yeah i um i'm I'm there for it. if If someone has something, I mean, that's been the lesson of of the of the macroscopes as he kind of ah became, a you know, encountered a new topic.
00:42:30
Speaker
ah you know, every time you turned around and it it that's that process has been great. um And so I don't feel like there's a particular, you know, data set that I've been yearning to have, but I'm i'm there and I just hope that it's presented in a way that that people can easily make sense of it and have it matter to them. Yeah.
00:42:49
Speaker
Yeah. Great. um I'm going to combine my last two questions. Kata, you mentioned earlier, we've got the third decade now as we enter that. um So I'll just plant that flag and just ask, um how can people submit or get in touch with you all or learn more if they're like, I want to have one of my macroscopes or one of my projects, I want to create a project. like what what What are the steps people need to take to sort of be involved in this next decade?
Episode Conclusion and Call to Action
00:43:17
Speaker
You go to scimaps.org, and we can maybe also feature that website link in the metadata for the video here. And you get to see an exhibit menu item, and then you go to call for submissions, and that gets you more information on that third decade, which is all about visualizing intelligences.
00:43:36
Speaker
And so we believe we have to better understand how our microbiome interacts with our bodily cells or how fungi and trees interact with each other and create life-sustaining function, but also, of course, how different organic intelligences interact with silicon and silicon intelligences such as large language models. So this next decade is really inspiring, hopefully many, to submit and work on that.
00:44:09
Speaker
And we then get the pleasure to pick the best and to ah display them in public places. And so we have the first iteration in hand, and that will be on display very soon. So check back on the website to see the initial um visualizations of intelligences.
00:44:25
Speaker
But then also try to um see if your work is relevant and please do submit. Terrific. Elizabeth, Todd, Katie, thanks so much for coming on the show. been great chatting with you.
00:44:37
Speaker
Thanks for the book. It's got to find a good big space. But thank you all for coming on the show. This has been a lot of fun. Thank you. Yeah, thank you. Yeah, thank you.
00:44:49
Speaker
Thanks for tuning in everyone. I hope you enjoyed that episode. I hope you will check out the new book, Atlas of Macroscopes. Lots of great stuff in there, some beautiful photographs and some great content to help inspire and inform you as you create your data visualizations.
00:45:02
Speaker
What episode wouldn't be complete without me asking you to please take a look and rate or review this show wherever you get your podcast. You can also subscribe to my newsletter at Substack. You can also subscribe to the website at policyvis.com where I've got lots of great tools, resources, and calculators for you to now play around with. So until next time, this has been the Policyvis Podcast. Thanks so much for listening.