Alyssa Fowers is a Ph.D. student at the University of Miami where she studies research methods, applied statistics, and data visualization. She writes about graphs (and sometimes swords) at Data & Dragons. Before returning to graduate school, she worked in data analysis and management for five years. Her professional career focused on making data more accessible, reliable, and interesting to businesses and nonprofits in Washington, D.C. She received a B.A. in psychology from Duke University. When she isn’t in the data mines, she enjoys hiking, crafting, and tabletop games.
Alyssa and I sat down at the Tapestry Conference back in December after Alberto Cairo, one of her professors at Miami, pointed me to one of her blog posts on making comparisons to individual values. We talked about her interests in school, how to visually communicate small and big numbers, and how to work with data.
Alyssa’s post, Time and Space
Washington Post, America is more diverse than ever — but still segregated
Guardian, Mekong: a river rising
Welcome back to the PolicyViz podcast. I’m your host Jon Schwabish. Welcome back to the show everyone. I’m really excited for this week’s guest, Alyssa Fowers, who is a PhD student at the University of Miami where she studies research methods, applied statistics and data visualization with our good friend, Alberto Cairo. Alyssa and I sat down at the Tapestry Conference back in December to talk about her interests in school and her work in the research methods department and in the communications department, and talk about how she visually communicates both large and small numbers and her blog, Data & Dragons, which I’ve been really enjoying over the last few months.
Just a couple of quick announcements before I get to the interview. The first thing, if you are interested in getting some free books, go check out a blogpost I wrote a few weeks ago. I’ve link to it in the episode notes. I’m giving away some extra books I found in my office while I was cleaning out some bookshelves. I have a couple of extra copies of Cole Nussbaumer Knaflic’s book, Storytelling with Data. I have an extra copy of Andy Kirk’s book, extra copy of The Big Book of Dashboards, extra copy of John Tukey’s book. So if you want to try to win one of those free books, go over to the blogpost that I’ve link to in the show notes and see what you need to do to win those books. It’s been a couple of months, I’m just going to keep extending that contest until we reached the threshold that I can start mailing those books around the world.
Second announcement is again another blogpost I wrote a couple of weeks ago on data visualization inventors, founders, and developers. I’ve been doing some writing lately about different types of graphs and I got the thinking about who are the people who created and invented and developed some of the graphs that we use all the time. Not just the line graphs and the pie charts and the area charts, but who created the Venn diagram, for example, that has V in it or who created the first Sankey diagram or the Gantt Chart. So I wrote a blogpost and opened up a Google sheet. What I’m asking people to put in some names and links and original articles from people who they think may be the original inventor or creator of some of our favourite data visualization that we use day-to-day. So that blogpost is also linked on the show notes page. So go in over there and check it out and see if you can contribute to this open project and maybe we can have a nice catalogue of people who invented some of our favourite graphs.
So anyway, on this week’s show, I’m really excited to chat with Alyssa Fowers and here is that interview.
Jon Schwabish: So do you want to start by talking a little bit about your background and how you’ve got to the University of Miami?
Alyssa Fowers: Sure.
AF: So I sort of arrived through a circuitous route which I think a lot of people do. There’s a lot of it. I felt I got into this by falling backwards through a door and sliding into a new dimension. It seems to be a common story.
JS: Yeah, a common thread.
AF: Yeah. So my undergraduate degree is actually in social psychology and I was a little bit of a lab rat. I did a lot of research, which meant I had data, which meant I had to analyze it. I actually applied to and was accepted into and was considering going to some PhD programs in social psychology research. I was studying implicit bias. And what wound up happening was that I realized that what had me leaping out of bed at two in the morning and running across campus was not that I had a new idea about how implicit bias worked. It was that I had a new idea about a potential interaction effect in my data.
AF: So that got me thinking that maybe I wanted to work in data and analysis, maybe more than I wanted to be in psychology research.
JS: So you are really more like excited about working and getting your hands dirty in the data.
AF: I just wanted to see what was going on. I like exploring, I like making things. So I decided to sort of lean into that curiosity.
AF: So from there, I actually graduated and moved to Washington D.C. and worked in a mix of data analysis management; a little bit of teaching, a little bit of database design for about five years. So I worked for a consulting company as many people who graduate in DC do. And then I worked for a non-profit as sort of their — my title was a manager. Anything that could wind up in a graph was my job.
AF: So a very wide range of things. What I realized, I realized sort of two things. One was that people kept coming up to me and asking me questions about, “Is this legitimate? Can I say this? How far can we push this? What does this mean? Is this a real difference?” And I wound up saying “I don’t know” a lot. And the second thing was that when I gave people data, people treat it as the hard and fast truth. So it was this, I think Mona talked about this a little bit earlier, the terror of your word being gospel and not really having those checks and balances. And I realized that I just didn’t know enough to do the job that I was doing in a responsible way.
So I think about going back to graduate school and actually I, as fate would have it, saw Alberto Cairo speak at a conference I was at for work and thought, what if I did that also because…
JS: Sorry to interrupt.
JS: So the people at the non-profit who were coming to you, those are like senior researchers.
AF: I wouldn’t say there was a research team, but it could be like the CEO would come to me.
JS: I got you. Okay. So they didn’t have like a data background.
AF: They were very smart, very analytic people who did their own analysis and would go into the data and say, “Can I actually do this?”
JS: Right, right, right.
AF: That kind of thing.
JS: So they weren’t asking you to double check, fact check, but sounds like asking more of the harder analytical questions.
AF: Yeah. We also worked with a research company at one point where they did a little bit more an in-depth analysis for us and I wound up doing a lot of the like translation back and forth and that kind of work also.
JS: Got you. Okay, so you’re doing the data analytic stuff, you’re doing some of the viz work, you’re doing the communication, bridging sort of thing that’s amorphous. And then you come down here to Miami, but you’re not in the communications department.
AF: Right. I studied in the program of research, measurement and evaluation in the School of Education. So that’s research methods, that’s some applied statistics. So it’s both finding things out and then thinking really hard about how we find things. And I wanted to add in that third piece, which was telling people about the things that we found out which seems like very natural fit to me.
JS: Yeah, absolutely. And so just quickly on how that program works. So you have, I would guess, your core part of the program in the school and then you also have the classes in the School of Communication?
AF: So I have classes in the school and then I have elective slots that I use.
JS: Got you. Okay. And so the reason that I wanted to chat with you is because you wrote one particular post that caught my eye, but also sort of a back series about getting people to connect with data. Do you want to maybe walk people through the core argument that you’re making here?
AF: Sure, yeah. So the blogpost is called Time and Space. It’s on my blog, Data & Dragons. The core of the argument is that the space that is spent on figures in a chart, the time that is spent in sort of scrolling through something that’s interactive and digital is also a form of encoding that we use these things to communicate priorities as well as communicating just straight information.
JS: Oh, that’s interesting.
AF: So I have some examples in the blogpost where there’s just a blank page.
AF: So I think it’s like the Wall Street CEOs from the Great Recession, it’s just a blank page. So if you think about what the New York Times can put on a page in print, it’s an enormous quantity of information. And to them having this blank page, having that shock value of they’re nobody, there’s nothing was as important as everything else that they could have put on that page. There are some other examples too where it’s sort of the opposite where rather than having a lot of blank space, you take something and you give it a lot of space. So there is part of the post that includes a visualization about the victims of the Las Vegas shooting. What that does is it takes, I think, it’s three numbers and it expands that into an individual image of each person. So you could say that in three numbers. It’s very, very small; people injured, people shot, people killed. That could be in a single sentence, it could be a very, very tiny table, and instead what they’ve done is they have sort of blown that up into an individual outline, a sort of silhouette for each person. And it’s not like the kind of thing that you walk and see where there’s a single very uniform like bathroom sign.
JS: Right, right or like a bar chart or line.
AF: Yeah, it looks like people are milling around, talking to each other, like they would have been just before the massacre. So I think that that’s giving space to each of those individual people and making them into people again in the next step,
JS: So what it is, it’s also interesting because you talked about, there’s sort of two parts of it; so there’s the print part and then there’s the digital part. So I want to come to the digital part because you’ve made an interesting comment how our perception, I guess, of the story is in some ways driven by the order in which things are. In the print side, when it comes to the CEO example you talked about earlier is a good one. So we’re looking at it here. But like it’s basically, it’s just says in the Times I think, this is like ten of the CEOs of Wall Street have to go to jail. It’s just a blank page. Now that seems to work from my perspective, that works in print, but I don’t think it would work on the website only because there’s like, there’s stuff on the top and there is stuff off to the side and then there’s ads popping up. So I wonder if you’ve thought about like this distinction between this approach print versus online.
AF: Yeah, I think that’s where the sort of the Times piece comes in the title. So this particular example, where it’s just a blank sheet might not work, but there was one another gun violence post that was extremely striking to me. I believe it was also in the New York Times. It’s a graphic of legislation, guns legislation that’s been passed over, I believe, between the Sandy Hook shooting in the Parkland shooting. And if it’s a series of tiny calendars and each day is shown in grey, if it’s month by month, there’s the number of mass shootings above each one. There’s little notes about like what might have been in legislation. And I just remember scrolling and scrolling and scrolling and it’s all blank. Every single day it’s grey because nothing was ever passed. And so it’s this really remarkable experience because you keep expecting something to happen and nothing happens and it’s sort of violating the expectation that you have of this calendar. And it really, you know, it’s one thing to say no gun control legislation was actually passed or very minor gun control legislation was actually passed. And another thing to just see where something should be and have nothing be there at all. So you’re sort of taking the viewer’s time to communicate it in absence.
JS: Right. Yeah, I guess it is also true that there are pieces like there’s this piece from the Washington Post where it’s the maps of segregation which is sort of now famous, but like it is its own thing. It’s like a black background and there are no ads. So I guess there are ways in which they do that.
AF: There was actually another sort of scrolling effect from that post about the Las Vegas massacre or the article about the Las Vegas massacre. There was an online version of it as well, and they didn’t just reproduce the same graphic, which was part of what was so striking about it to me. When you scroll through, at some point, you get to where I think this graphic was in the print version and the screen just goes white and then a sentence pops up that says “Here’s the number of people that were injured” and you just keep scrolling. Here’s the number of people that were, you know. So it forces you to sort of sit with each of those ideas while you’re scrolling because there’s this really, really compelling personal narrative going on either side. To see what happened, you have to put your attention on these things.
JS: Yeah, right. It takes your attention and then you’re forced to wait for the next piece as opposed to that bar chart where it’s like, oh, there’re three bars.
AF: Right. And that’s what I think is so striking to me about it because in print the limitation is really, I believe, its space whereas online, the limit is really attention. You have potentially unlimited space. You can make your website or your article or your post or whatever as big as you want, but you’re really limited by how long people want to look at it. And so it sort of comes back to that thing about values and priorities where whoever was designing that visualization or that scrolling experience decided that it was worth potentially losing people. It was worth spending the time on it because this is very important. So that was a lot of what was very powerful to me about it.
JS: Yeah. Have you thought about the change in direction as it were? So most of these sort of scrolling down of things are all vertical and we used to have a lot of stuffs that were horizontal and there only seems to be like the rare occasion where they’re kind of blended. Like I remember the Guardian did this story on the Mekong River a few years ago now probably where at least in the mobile version, there was a combination of you would scroll down and then you could scroll across and then it would bring you back. I don’t want to put you on the spotlight. Have you come across any of those projects? Or just generally the differences in direction in that way. And maybe it’s something that’s just different between a mobile platform and a desktop or a laptop where especially we don’t have a touch screen. It’s just a different interaction, right?
AF: Yeah. I think that part of my guess would be that the move towards having things be vertical scrolling is because people are looking things on mobile. From my own experience, working with spreadsheets, I don’t like going sideways. So I just have an instinctive desire to not go sideways. If I think is something is stepping across a screen and I don’t have to actually scroll to see it…
JS: It’s a physical step or the chapter or the page turn.
AF: It’s a lot of work to me than having to scroll right, I am just like, oh my God, why isn’t this in a database, why is it that long, which lets how my brain works a little bit. I think also that, I I’m going to take back what I said, there is a little bit of a more space limitation when you look side to side.
JS: Yeah, absolutely.
AF: Just because of how people are used to consuming information digitally. If you’re reading a document, you’re scrolling down, scrolling across, unless like you said you were in like an e-reader or something where you have this vision of turning pages.
JS: A map, for example, like I mean we obviously look north south, but also east west. And so if the story was to go from west to east, like the Washington Post did that story on the eclipse. And I’m trying to remember how they set it up. I think it was his own sort of thing, but I don’t remember it going like where it was showing the path of the eclipse across the country, but I don’t remember like swiping or something like left and right.
AF: My guess if I remember is that you just zoom. It’s not like you have to go in a specific order.
JS: Yeah. Well, anyway, so that’s sort of unsung because that’s interesting. But I want to get back to this idea of using the silhouettes of the people, because in this particular example of the Vegas shooting, it’s still lots of people. It’s not dots, it’s not bars, it’s people. So I guess that’s just a broader question about using icons in data visualization and your thoughts on how that helps a reader or hinders the reader and how it’s useful for data visualization creators and also potentially a challenge for creators.
AF: Yeah, I think that an icon communicates, it communicates a lot. And one of the things that is really interesting to me about icons is how important the level of detail is. So in this Las Vegas massacre image, there are clearly people, they’re sort of leaning there, their grouping in kind of organic ways. So they are very clearly people, but it’s really hard to tell what gender they might be there. You can’t tell what race anybody is, you can’t. You really can’t tell anything about them except that they’re probably all adult people. So there are sort of different levels of detail that communicate different things. So like I mentioned the sort of bathroom sign icon, all that says is person. It’s pretty generic, it’s instantly recognizable. People aren’t going to spend a lot of time looking at it and going, what is this, or absorbing the details.
But as soon as you put more detail into that, the more specific you make an image, the more specifically people are going to read it. So if you take that sort of generic human outline and you make it more gendered or you make it into a child or something like that, people are going to assume that what you’re showing is sort of relevant to everything included in your visualization. And as you sort of add more detail, the message that you’re sending gets more and more specific because you can get, of course, all the way down to a photograph of someone. But then it’s extremely specifically that person.
So I think that it’s something to be aware of where the less you tell you have the faster is to process but the less specific it’s going to be and the less sort of immediate resonance it’s going to have with the people who are looking at it.
JS: So the gender icons, for example, like the bathroom icons, there’s like basically the icon, which we all sort of recognize as male only because I think the female icon is sort of standard one has like a triangle dressed sort of thing. So that directs us in the two directions, but we also have like gender as a spectrum. So that adds an additional and races, people can have various backgrounds. So how are we as designers or maybe a better question actually as you as a designer like communicating this, like how do you start thinking about this? I mean I don’t think there’s an answer to this really, but you’re clearly thinking about it. So I’m curious, you know, if someone were to bring a graph to you and to say, “You know, I’ve got income for men and income for women and I want to use icons.” How do you start thinking about the limitations of that?
AF: So I think that in a case where you really have this categorical information in your data and it’s really important to the analysis, it makes sense to show it as these two categories. But I think it’s also important to recognize and acknowledge that you are going to have non-binary people in a lot of datasets and that not all women look a certain way, not all men look a certain way, not all non-binary people look a certain way. I don’t have a great answer for do we have imagery that is instantly recognizable that appreciates that gender is not necessarily this hard and fast binary. But I think it’s really important to keep it in mind and to just be mindful about what you’re doing while you’re doing this work. And if there are non-binary people in your dataset, don’t forget about them. If they’re there, make sure they’re sort of visible.
JS: It’s difficult because they’re certainly, almost certainly are non-binary people in a lot of the datasets, but they’re either for whatever reason they’re not identified.
JS: And so I think the general message right is to just be aware or be aware which is what your implicit bias, it all comes back where try to be aware of these things even though it doesn’t show up in the data necessarily in the explicit tag.
AF: Yeah. So I should probably clarify a little bit. I do mean that one should always be aware of it, but if in your dataset, you have this explicit tag, I do sometimes see people saying, well, we have non-binary people in this dataset, but it’s like 1% of the data. So we’re just going to exclude them from everything. So that’s the kind of thing that is important to me to not just like leave out or exclude.
And yeah, that, that question of how do we do iconography when we don’t have discrete groups is really hard. It’s really hard. I don’t have a perfect answer for it.
JS: Can you tell us a little bit about your research? So you’ve got this interesting mix which is like almost like the renaissance person. I don’t know, like it seems like it’s the evaluation side, but also then sort of the communications and database side. So can you talk a little about, I mean PhD is a long thing. So what is your research about and how are you blending all this together?
AF: So right now I’m working with my advisor, Dr. Soan [phonetic] on second-order meta-analysis which is an abstraction of an abstraction basically. So a meta-analysis is a synthesis of lots of different effect sizes about the same phenomenon synthesized into one. So it’s sort of averaging across a bunch of different studies to say, we have looked at a lot of research about this issue. If we combine everything together, here is our estimate of how big this is or what this relationship is. So first-order meta-analysis which is what most people think of is summarizing primary research. So you have an individual study and then where you compare one group of people to another group of people and then you get a bunch of those, and that’s the first-order meta-analysis. I’m studying the second-order meta-analysis at the moment which is you’re doing a meta-analysis of meta-analyses.
JS: Got you. So you have 100 studies, 100 point estimates of some relationship between x and y and 10 people have done meta-analyses of those 100 studies. So you are doing the meta-analysis of the 10 studies.
JS: Okay. In a particular area?
AF: So, so I’m studying it from a methodological perspective. So right now what I’m doing is, is a simulation study. So it’s all I make up data to study an abstraction of an abstraction which is the most academia thing I think I’d ever said. I go through all that to say that, this has been a case study and how hard it is to talk to people about statistics where I’m trying to write a grant application or something, an article about this and it takes eight words just to say the two things that I’m talking about and people’s eyes are at the third word. So that’s some of the research on the statistics side that I’m doing. I have an interest though sort of more generally in this translation piece. I think that data can seem like a foreign country full of incredibly perfect hard truths that if you throw a lot of money at the end trying to get at it, you can come back with an iron clad pack of action that will undoubtedly result in success. You can come back with your hard truth.
And having worked with data and a lot of different ways, sort of having my hands in databases, seeing how it’s collected, seeing how it’s analyzed, that that’s not how it works.
JS: Yeah, right.
AF: Data is intensely human. And so, to me being able to communicate, being able to sort of accurately communicate what the data is actually saying, being able to sort of translate it for people is immensely important.
JS: So you’ve got this methodological piece of your work here that is doing the second order meta-analysis. But really at the core, it sounds like what the core of what really drives you in some ways is trying to take these 100 estimates and trying to communicate that to people even though there’s various levels of uncertainty and distributions around within each study and then across all of these studies. So is the sort of bottom line goal, I guess, to try to figure out a way to communicate relationships between variable A and variable B to policymakers and stakeholders and decisionmakers, is that like, where you’re trying to get to?
AF: I’m going to give you again, in the most academic way possible, which is…
JS: Oh, never sitting at the university bench. We should have done this like at a coffee shop, I would’ve gotten like.
AF: So that’s one of the things that I really want to do and that’s very important to me. I think that with my work here at the university, I’m very much a both end person. I want to be able to help people do this research in sort of the best way possible while also having this, being able to communicate it out. I am also really interested in applied problems in sort of getting into more actual data and doing analysis on stuff that actually sort of comes from people and is applicable to sort of more hands-on situations. I do think that part of doing a good analysis is being able to tell people about it because if you do the most brilliant and cutting and an insightful analysis in the world and nobody knows what you’re talking about, it’s the tree falling in the forest.
AF: If your analysis falls in a desk drawer, did you ever really do it?
JS: Absolutely. Hallelujah. Yeah.
AF: Yeah. So for me, it’s this combination of wanting to make sure that data is handled in a responsible way and also making sure that it’s understood and these things very much go hand-in-hand for me, and what’s also very important, and I’m glad you mentioned it, is making sure that it’s understood by the people who are going to use it. Not just having other academics or other visualization people understand what I’m doing, but making it more open to people.
JS: Yeah, absolutely.
AF: Which is one of the really big challenges I think, and as someone who really likes making things and experimenting and sort of throwing things at the wall and seeing what sticks and falling, you know, not falling, leaping down rabbit holes, it can be hard to sort of reel it back to what actually makes sense to other people.
JS: Yeah. Well, I think you’ve cut out a pretty big slice of your graduate work pie.
JS: Well, Alyssa, this is great. Thanks and good luck.
AF: Thank you.
JS: And I’ll put the posts on the show notes so people can check them out and I’ll put some pictures on the site. So Great! Thanks a lot.
AF: Thank you.
So thanks so much for listening to this week’s episode. I hope you learned a lot. I really do hope you’ll check out Alyssa’s blog, Data & Dragons. She’s writing about some really interesting things and some interesting observations, especially at the crossroads of data visualization and research methods and statistics. So please do check out the show notes; there are some good resources there, some old blog posts, and some other things that I think you’ll find interesting. So until next time, this has been the PolicyViz podcast. Thanks so much for listening.