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Episode # 199: Miriah Meyer
237 Plays4 years ago
Associate professor in the School of Computing at the University of Utah and a faculty member in the Scientific Computing and Imaging Institute visits the PolicyViz Podcast to talk about the Visualization Design Lab.
The post Episode # 199: Miriah Meyer appeared first on PolicyViz.
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Transcript
Introduction & Upcoming Episode
00:00:13
Speaker
Welcome to the 199th episode of the Policy Viz Podcast. I'm your host, John Schwabish. I hope you are well and healthy and safe, and I hope you are going to continue to listen to the podcast as we near the end of this season, rounding out in just a couple of weeks with the 200th episode. I have something very special planned. I hope you will tune in, but I hope you will also listen to this week's episode of the show because I have a very special guest with me.
Meet Mariah Meyer
00:00:40
Speaker
I chat with Mariah Meyer, who is an associate professor in the School of Computing at the University of Utah. Mariah and I got to know each other earlier in the year a little bit better after having run into each other a couple of times in the past. But we got to work together on a panel helping a US federal government agency improve how they were visualizing their data. So we had a lot of
00:00:59
Speaker
fun discussions about techniques and approaches and processes and workflows and everything that an organization or an agency would need to have a better data communication setup.
Participatory Design & Oregraph
00:01:11
Speaker
In today's interview in my discussion with Mariah, we talk about her research
00:01:16
Speaker
She's done a lot of research on participatory design workshops, and we talk about how data visualization instructors can utilize some of those methods. We talk about data visualization tools. She did some work a couple of years ago on a tool called Oregraph, so we talk about her work on that project and the data visualization tool field sort of generally.
00:01:36
Speaker
And then we also talk about the current state of data visualization research. We talk about what we think or what she thinks the field should move and what directions are different to mention. There's also a special reveal coming up in this episode right towards the end of our interview.
Special Reveal Tease
00:01:53
Speaker
So make sure you listen to the whole thing. It's a very special reveal. I'm not going to tell you what it is right now. You're going to have to listen to the entire episode.
00:01:59
Speaker
And you will, I think, find it to be quite some interesting news, something that I think we will all be looking forward to to see what happens over the next couple of years. So I'm not going to delay any longer. I'm going to get right into my conversation with Mariah Meyer. I hope you will enjoy this week's episode of the Policy Vids podcast. Hi, Mariah. Great to see you. How are you? Welcome to the show. I'm great. How are you, John? Thanks for having me here.
00:02:27
Speaker
Of course, I'm doing well. It's great to see you. Now we actually get to see each other. It's been a few months since we actually chatted last time. We were doing this really cool. What advisory board panel thing. Yes. Where we basically just got to.
00:02:43
Speaker
talk at length about the things we feel very strongly about when it comes to visualization recommendations. It's lovely. It was. It was. It was just like a freeform discussion about everything we love and hate about database. That was pretty good. So we've got a lot, I think, to talk about all of your awesome work and maybe some thoughts you have on the future. I thought maybe we'd start with
Impact of Design Workshops
00:03:07
Speaker
the work you've done in the past on participatory design workshops. So you've written, I think, a couple of papers on that. And so I was hoping maybe you could just talk about that work a little bit and then maybe we'll just make it personal. Like, how do you implement those ideas into your own teaching? Yeah, so I'm glad you asked about those. So a number of years ago, one of my students, Ethan Kurzner,
00:03:31
Speaker
He was working on a project with some neuroscientists and that's sort of the basis of the work that we do in my lab. It's about working with people in the world and designing tools for them and using those experiences as ways for us as researchers to ask questions. And so my student Ethan was working with this group of neuroscientists and he was doing the sort of initial before we designed trying to understand what it is that they're having challenges with, what are visualization opportunities for us.
00:04:01
Speaker
he would go and interview one lab member and they'd say, this is the really important thing. And then he'd go and interview another lab member and they'd say, no, no, no, it's this thing. And then the PI for the lab would be like, really? None of those things are important. And so he was getting really frustrated. And, um, so about that time he saw a paper that was published by a group in London, they were working with these energy modelers, very different group, but they were talking about these workshops that they had been running.
00:04:27
Speaker
with this group to better understand, to sort of build consensus and understand what the opportunities were. And so Ethan decided to try this. So he designed and ran one of these, at the time we were calling them creativity workshops with our neuroscience colleagues. And it was just incredibly successful. It's a participatory style workshop where you bring people together.
00:04:52
Speaker
The ones we do are based upon creativity theory. So it's about how do you get groups of people to be creative together in the brainstorm? But how do we do that in a structured way to get towards very tangible visualization opportunities? And so this was really, it was great. Not only did we learn a lot and Ethan was able to, he developed a really nice tool that he deployed to the lab based upon this, but all kinds of other like really squishy things came out of it.
00:05:21
Speaker
There was this one member of the lab who Ethan just couldn't get any time with. And after this workshop, this person was then willing to meet with us very regularly. We heard back from the PI that as a lab, they had made a lot of progress on their own sorts of needs for the next couple of months through these conversations. So there's all this agency within the group that these workshops developed, and we also were able to build trust among the VIS folks and the neuroscientists. So it was great.
00:05:50
Speaker
After that, we even went and talked with the London group about their own experiences. And then we spent the next two and a half years reflecting as a group on our experiences of running many of these workshops. And we were able to formalize that into a framework, a sort of structured framework for how you design these workshops, run them, and then analyze them. And so after we did that, we started using these workshops in every single project that we do.
00:06:20
Speaker
And I really think it saves months and months of time of that. Otherwise you would need to spend talking to one person and then another. And so it's a way to bring people together and get consensus on what some opportunities are for visualization design. And I think these are, you know, there's probably, you know, many people who listen to your podcast, like practitioners and, and other scholars who do similar kinds of workshops. These, you know, it's very, I think it's a, it's a very common and productive method to bring people together.
00:06:49
Speaker
But I think what's useful about the workshops that we developed is that they are super structured and in that we have certain activities that we like to do. There's a sort of ebb and flow of making people very much diverge in their ideas, but then bringing them back together and doing that over and over and how that can lead towards some sort of consensus building on critical ideas.
Qualitative vs Quantitative Research
00:07:15
Speaker
So that's I guess a long-winded answer to your question, but they're awesome and they're very much tailored for trying to find out visualization needs.
00:07:26
Speaker
So I want to ask you because the way the workshop was developed was through this sort of qualitative approach. So talking to people having these conversations. And I know you do other research that's, that has more of a quantitative bent. And so I'm curious about your take on the blending of the two, specific to obviously to, to vis research, but, you know, do you have.
00:07:49
Speaker
I don't really know, I don't honestly really know what the question is, but like, do you feel like it's important to merge the qualitative and quantitative? Should they be separate? Like, were you trained? I assume you were trained as a quantitative researcher, not as like a qualitative researcher. So like, what is that sort of switch in your, or not switch, but like added skillset to your approach been like? Um, yeah, I, I love that question. And in fact, you know, in thinking about these workshops,
00:08:15
Speaker
Well, let me stop. So yeah, my own background, my undergrad degree is in astrophysics, and then my PhD is in computer science. My PhD work was very quantitative in nature. But if you look at the kind of work I've been doing the last couple of years, it's almost exclusively qualitative. There was a real inflection point. And I think that our developing of the sort of workshop framework is sort of a nice case study of how that switch went.
00:08:45
Speaker
Again, my student Ethan that was leading this work, he was also a very, he's a very quantitative person. And when he had to tackle the problem of trying to analyze all the stuff that we, you know, all the sort of data and evidence that we collected during the workshop, he started by making these spreadsheets and counting things. Like how many insights did people have? And can we like rank those on a variety? Like, can we assign numbers to them for a variety of attributes?
00:09:12
Speaker
And at the end, he kind of threw all that out because it's like, it doesn't matter how many times someone said this word, like that doesn't matter. And what really mattered more was his own sort of reflection on what he learned, sort of observing and facilitating this group of people that he was going to work with. And so that was like, that was like a really difficult shift for him to let go of. And I have to say, like, I sort of went along with him in that journey.
00:09:42
Speaker
But I think sort of more generally, my own feeling is when we're looking at visualization in the world, I just don't think putting numbers on things really tells us the kinds of things that matter to someone who's going off and designing for the messy complexity of people and the world and relationships and all that stuff that gets entangled together. And so my own work has very much shifted towards qualitative work. And in fact, I have a
00:10:11
Speaker
whole project led by another student that is all about it's a longitudinal study where we're doing lots of interviews along the way and even there we've we sort of abandoned the idea of of open coding which is I think the sort of quantitative person's way of approaching qualitative work like let's put some codes and then count them and like count how many codes match between this this coder and this coder and
00:10:36
Speaker
At the end, I just feel like it's really hard for us sometimes to value what we just intuitively know and learn. But that is where I think the knowledge resides. It resides in my interpretation and my students' interpretation and then us talking. And so that's actually led to a whole other stream of research I've gotten interested in, which is how do you document this sort of reflective, collaborative process?
00:11:01
Speaker
So how do you document it? How do you provide evidence? How do you convince people that the outcomes you came to are reasonable and plausible? And then also, how do you even allow people to understand how you got there? And so that, I think, is a whole other sort of interesting, deep bit of work. We're starting to think about, but it's very much in support of people going off and doing messy things in the world, but valuing what we as individuals learn from that.
00:11:31
Speaker
Right. So looking at the dataviz research field sort of more broadly, do you think that the field has that right balance or not? I can tell you how I feel about the econ field, but not about dataviz. So like, what's the, what's the balance like in dataviz?
00:11:47
Speaker
Um, it's like you're trying to poke me. Well, cause some trouble, cause some trouble in the early summertime here. Well, I have to say no, we don't have the right balance. And I think on the whole, the visualization research community knows that like, yeah, every year there is a recognition that we need more qualitative work. We need more examples of qualitative work. Um, you know, I, I've,
00:12:14
Speaker
I've been in the community and reviewing for many years. I've been a paper chair the last couple of years and sort of seeing, seeing the kinds of standards that are applied to qualitative work is a little bit heartbreaking because it's really hard. But, and it's also, it's also a bunch of methods and skills that your typical computer science, like graduate or undergraduate student doesn't have any training at all. Yeah. And so, so many of us are sort of like winging it and making up and doing our best.
00:12:42
Speaker
And it's really hard when you position that kind of work against what has become a very well-established quantitative bent in our community, like controlled studies and statistical analysis. Like there's people in our community now who are clearly experts and leading in that. But that is so well established and you can like name the kinds of statistical tests to do. You look at qualitative work and really it's
00:13:11
Speaker
So individual and it's so specific to the project and to the needs and to the skills and that's okay. Um, that it makes it really hard to sort of, you can't have a checklist of things and say, this is good work because it meets a checklist that doesn't work for qualitative in the same way. I think it works for quantitative. So as a community, I think we're really struggling with that. And, um, I, I feel like we've gotten really good at the sort of more positivist objective loving
00:13:41
Speaker
controlled science side of things. The community has, I think, really come a long way there. But because of that now, it's making it in some ways, I think, more difficult for design-oriented and qualitative work to get the same kind of recognition and acceptance because they're just fundamentally two different approaches to research. And yet as a community, we tend to just apply one sort of epistemology or perspective on how to value it.
00:14:09
Speaker
I think the same is generally true for economics. You know, there's been over the past couple of decades the growth of the behavioral economics field, which is more qualitative, but it's also sort of a slice or a sub, you know, it's a kind of a sub sector of economics where I think to your point,
00:14:29
Speaker
I would guess that CS grad students and economics grad students are the same thing that there's little to no training on qualitative methods. Whereas if you go to like a sociology department, it's
00:14:42
Speaker
And not that I've ever, I've only gone to sociology webinars, to be clear, to only sociology webinars to get free lunch. That was the plan in graduate schools where you can get free lunch. But like sociology graduate students, the ones that I know, they mostly qualitative skills, but they also had some quantitative skills. Like they knew how to like clean data and run a basic regression. I mean, if you, you know, you tried to throw higher level econometrics at it, that's not something that they were, they would know, but like,
00:15:10
Speaker
They had all these qualitative skill sets that like, I didn't even have to read about it. So I wonder like you're a faculty member. Like how is the evolution of the field? How does it move forward given the current sort of syllabus as it were for graduate students? Like is that, is that where things start to change or does this start to change with the more senior level faculty members, researchers who start to embrace these methods and then it trickles into their grad programs?
00:15:40
Speaker
Um, that is such an interesting question. I, I, unfortunately, it's probably not what I've thought about deeply. Yeah. You know, one thing that strikes me is the social science departments that I know of, for example, here at the university of Utah, they have methods classes that span quant and qual. They have methods that teach them. There are different epistemologies out there. There are different views on what knowledge is and how we come to know it.
00:16:09
Speaker
And they teach these things in the framing of there isn't just a singular worldview about what truth and knowledge is. There are several. And you make decisions about which one is the best fit for the type of inquiry that you're doing. And you look at computer science programs, and it sounds like probably similar for economics. And we just don't have that training. And I think one of the problems then is that
00:16:36
Speaker
There is an approach to how we think about knowledge and truth, but we don't even know it. We don't even recognize that we have a singular perspective. We don't even know that it's a perspective.
Educational Changes in Research Methods
00:16:49
Speaker
And so I think, you know, I've looked to one of the places that I find a lot of inspiration in are in iSchools, you know, and there's a number of very technical iSchools now that I just find the work coming out of there so exciting. And you look at their curriculums and the students have to take classes that teach you about different
00:17:06
Speaker
philosophies and these kinds of things. And, and that's the kind of thing that I would love to see sort of coming into more computer science programs. I think the fact that as a field, we suddenly suddenly realize, Hey, ethics is like a thing. And there's like a problem here. And we don't really know how to handle that. And that's because, right, we don't have the training yet. And so a lot of programs, including like, you know, my own department, we've been working on building ethics curriculum into our degree programs.
00:17:36
Speaker
And I think that that's a step. But I think that ethics also comes from recognizing that there's different types of thinking besides math and logic. And so where am I going with all this? I think in computer science, I think getting training beyond basic
00:17:57
Speaker
programming and object-oriented programming and operating systems, there's all these other things that I think are increasingly important. They have been important for a long time, but only recently, I think, has my community, my CS community recognized that, oh, yeah, maybe we need to do something different because we seem to be causing harm and we don't know how to solve it. So I think it's kind of a great time to make these kinds of changes. And I think there's, as you're sort of, I think,
00:18:24
Speaker
sort of implying there's a lot of opportunities to train this next generation differently and to understand that there's many different ways to think about the world.
00:18:32
Speaker
Um, and I think if the younger kids all do it, then the senior people have no choice but to go along with it. Right. Yeah. I mean, it is, it is interesting, right? Cause it's always a trade-off. So if you have to take X number of classes and we're going to say you have to take ethics and you have to take philosophy of thought. Well, there's two classes that you have to, you have to take out somewhere else. Um, but I've seen lots of, uh, information schools is a good example. I've also seen a lot of public policy schools trying some different ways to provide.
00:19:02
Speaker
Well, in DC, it tends to be very like professional development in the schools that I work with. So it's like, how to write for a policy audience, how to do GIS software. And so it's not like a full semester class, but it gives you that, at least that introduction to those skills that are not typical in these different fields or different departments. Totally. Yeah. And it's funny, like I struggle, we
00:19:27
Speaker
struggle a lot in our own department. Exactly what you're saying is like, well, these important, these classes seem important, but like, what about all this other important stuff? And at least for computer science, it seems like there's this really, I think exciting trend of recognizing that computer science is broad and there may not be just one flavor of what CS means and explore, you know, like the person who's going to be doing a lot of front end development work.
00:19:54
Speaker
um, may not need to know about, you know, networking or how a compiler works, right? Like at some point we can't know at all. And I suspect, you know, if you look at fields that perhaps are much more fluent and deeply embedded in methods, a lot of them are, are sort of being asked now to be, um, computational as well. And so I know that they're struggling in similar ways too.
00:20:18
Speaker
Yeah, I bet that's true.
Future of Visualization Tools
00:20:20
Speaker
Um, I want to turn back to another strand of your research, which is on, um, on data visualization tools. So you did this paper. I think it was fairly recently, right on, on or a graph. Um, and so I was wondering if you could tell folks a little bit about that project and then we can talk sort of generally maybe about data as tools and where you think tools are going over the next several years.
00:20:45
Speaker
Yes. Um, so that project or graph, this was a project that was led by, uh, another student of mine, Alex Bigelow. And the idea behind it was really about, uh, graph wrangling. And so what we, what we wanted to do was think about, you know, you know, graphs or networks as a representation are a really interesting representation for thinking about relationships between things in your data.
00:21:11
Speaker
And a lot of the things that we think of as networks or as a graph aren't necessarily like physically connected things in the world. So it's more of an abstract sort of thing. You think about a social network, like I'm not physically connected to you, John, but like because I follow you on Twitter and like I have a connection to you. So we abstract that into this sort of graph relationship.
00:21:35
Speaker
Anyway, so we got really interested in how people use graphs as a sort of thinking tool. And we started thinking about like, well, what if, you know, what would it mean to, um, to allow people to change that graph model? What, what would it mean to allow people to define on the fly, what it means for someone to have a connection? Like, you know, who am I connected to that also does data visualization and lives on the East coast? Like, and now I have a connection to you.
00:22:01
Speaker
And as we looked around, like, you know, there's a lot of data wrangling for, you know, tabular data and things like that. There's, there's tons of exciting and great tools out there for that, but graphs still hadn't been done. So the whole idea of Orograph was to provide a tool that allowed people to, you know, define a model for a graph and then be able to visualize it.
00:22:22
Speaker
And why I'm excited that of all the tools I've created or been part of that you picked this one. One of the reasons I like that is because to me that also sort of embodies what I see is sort of a major future thrust of visualization, which is thinking less about the specific visual encoding challenges and more about the visualization as a tool.
00:22:47
Speaker
And so, you know, I think the heyday of the, you know, the Viz community, at least just the research community is all I can speak for, coming up with like really exciting new techniques that actually like matter is largely gone. But instead it's about experimenting and thinking about what visual representations and interactive representations allow us to do. And of course, like with data science being so important,
00:23:15
Speaker
I think visualizations are a really critical way of helping people reason about data. So that's one of the things I see as being increasingly important for visualization is less of a focus on the specifics of how I encoded something and more about thoughtful design work that gives people access to data and opportunities to explore it. And this tool or a graph was, I think, an example of that.
00:23:43
Speaker
You know, it's really interesting because of the projects that we've talked about, like the through line of your work seems to be, how do we bring people together to solve problems or, or be creative within a group in the, in the design workshops, like in person. And then through this, through a data visualization tool where it's being creative or connecting people within the tool itself. Yeah. I love that perspective. I have to say like my work is less and less about
00:24:13
Speaker
tools and learn more about what we learn through the act of either designing visualizations or people using them. And so in that, I think of it very much as sort of the human side of data science and visualization is, that is our tool, our probe, our technique for exploring that.
00:24:31
Speaker
Right. So you have, I'm sure, a lot of projects starting up ongoing. Do you have anything you want to talk about briefly? And then I know you have a big move coming up, starting up a whole new project. So we can talk about all that stuff.
Mariah's Move to Sweden
00:24:50
Speaker
So maybe we can talk about the big move and then what you hope to accomplish after you've packed up all the stuff in your house.
00:24:58
Speaker
Well, since you asked, John, yes, I do have a big move. Yeah, I'm really, really excited. I've accepted a new position at Lindshoping University, which is in Sweden. There's a big visualization group that's been there for quite some time now that's headed up by Anders Jättermann. And they do some just amazing work, particularly with public outreach. They have a big museum space and think about
00:25:25
Speaker
how visualization and data can be used in these sort of communicative outreach settings. So yeah, so I'm really, I'm excited to go and try some new things and build some new collaborations. And so yeah, so I'm gonna be moving there this fall. And one of the things I'm really excited about moving to particularly like Scandinavia is the sort of, it's kind of the birthplace of participatory design and thinking a lot about democratization
00:25:55
Speaker
technology and data and all these things. And so I think there's, I'm excited to sort of be embedded in, in a different way of thinking about how we make visualization and data more inclusive and more diverse. And, and, and that, so that, that is going to be a, that is a theme of mine. I think going forward, both from the thinking about it theoretically, from an epistemological perspective, we've been doing a lot of work with applying feminist theory to visualization recently.
00:26:23
Speaker
Um, but then also sort of questioning like how we, um, who we're designing tools for and is designing tools always the thing we should be doing? And I have a project with putting air quality sensors in the homes of, um, people with asthmatic kids. This is a project led by another student of mine, Jimmy Moore. Um, and after having spent several years with these families and interviewing them.
00:26:48
Speaker
Um, and ultimately we had hoped to design a visual analytics tool to help them explore their data. Our end conclusion is actually, no, the most effective thing that we did was to bring an analyst into their homes and let them ask questions and on in real time show them stuff. And that's really led us to question like, would it have been worth all the resources and, and, and, and hours of designing the tool that maybe they would use. And maybe instead we should think about like systems. How could we scale up that idea?
00:27:19
Speaker
of having access to, you know, to train data analysts, um, in some sort of like in like a data clinic, what would it look like to have data clinics and to build a volunteer core of people like us that would be interested in working with others and what are the incentives there. And so, so those are the kinds of things that I'm excited to work on. And I feel like, you know, a place like Sweden and Europe in general.
00:27:42
Speaker
There's a lot of history of those kinds of ideas coming out of those countries. Right. That's very exciting. Congrats, first of all. Thanks. That's exciting. I want to ask before we round up, you've mentioned that there's a big museum and planetarium, I think, there.
00:28:00
Speaker
Do you see yourself, and it sounds like you do, see yourself working with the folks who are in those museum spaces?
Data Visualization in Museums
00:28:08
Speaker
Because I feel like Dataviz in museum spaces is an underappreciated part of the world of Dataviz. Here in DC, when you go to the Air and Space Museum in downtown DC, they have these little posts that represent with a little sphere on top for each planet, and they're sized and spaced out accordingly. So Jupiter is like,
00:28:30
Speaker
whatever it is five blocks away or something like that. And I just feel like that's such a great way to visualize data. And we don't, I don't think it's a field really talk about that much. So I'm curious, do you see yourself working with that team? Um, I would, I would love to, I think one of the things that I'm excited about this, this, this group I'm going to be joining as they also have, um, learning science. Uh, they have a learning science, uh, faculty there who are, you know, part of the people that think about how people learn in the museum setting.
00:28:59
Speaker
And so it's sort of bringing together and there's a design group and stuff. And yeah, and I think as a communities, the this community, some people have tried to touch on that, but we get so wrapped up in like the represent the sort of accurate, I say in air quotes, accurate representation of values that we forget about the sort of embodied nature that like what you're describing this sort of very visceral reaction of having to be like Jupiter is five blocks away. Oh my goodness.
00:29:26
Speaker
And I think there's so much that we could learn about how people, the experience of representations and what that might mean for how we think about visualization. You know, there's this whole emerging thing with data physicalization and what does that bring? And like, it's easy to say, oh, data physicalization is great for teaching people about this or for getting people excited about their personal data.
00:29:53
Speaker
But why aren't we doing data physicalization for bioinformaticians? Why don't they get to play and enjoy too? So I think, I think like you're saying that there's a lot of crossover. I would think from what we as a community can learn from museum settings and other sorts of engagement approaches that I think would be super exciting to apply to our like, you know, you know, working with scientists, because I think we're kidding ourselves if we think that like,
00:30:21
Speaker
You know, a scientist is just this like dry person who looks dryly and very like they're like a, they're, they're just this perceptual machine looking at their data, but no, these people have hopes and dreams and feelings too.
00:30:33
Speaker
Like, I want them to have to look five blocks away and be like, wow. Yeah, right, exactly. Yeah, I think that is exciting. I mean, I definitely am excited to see what comes out of your new position, new experience, because I feel like what we've been talking about, this through line of this getting people together and sort of this cross-cutting thing that seems to go through all of your research,
00:30:57
Speaker
really can just blossom and find some really interesting patterns and combinations and teams and where you have access to a whole other team that's doing this sort of interesting type of work that maybe we don't interact with enough. No pressure, right? No pressure at all. And I'll just keep refreshing your website like every month, like starting in September.
00:31:20
Speaker
Mariah, thanks so much for coming on the show. This has been great. Congrats again on the new position. I'm sure everybody who's listening is gonna be excited to- Thanks, Ben. This was such a lovely conversation. Great to see you again. All right, thanks so much. Bye. And thanks to everyone for tuning into this week's episode of the podcast. I hope you enjoyed listening to that conversation with Mariah Meyer. I hope you'll go check out her work. I've put all the links to the things that we've talked about, including her research in the show notes for this week's episode of the show.
00:31:49
Speaker
So we get down to the last episode of the Policy Miss Podcast for this season in two weeks for episode number 200. Make sure you come back, check it out, something very special coming your way. So until next time, this has been the Policy Miss Podcast. Thanks so much for listening.
00:32:06
Speaker
A number of people help bring you the PolicyViz podcast. Music is provided by the NRIs, audio editing is provided by Ken Skaggs, and each episode is transcribed by Jenny Transcription Services. If you would like to help support the podcast, please share it and review it on iTunes, Stitcher, Spotify, or wherever you get your podcasts. The PolicyViz podcast is ad-free and supported by listeners. If you'd like to help support the show financially, please visit our Patreon page at patreon.com slash PolicyViz.