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Episode #182: Aaron Williams
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On this week's show, I talk with Aaron Williams about his work as a data journalist, scientist and visualization expert tackling inequity in data and design at scale.
The post Episode #182: Aaron Williams appeared first on PolicyViz.
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Transcript
Introduction to PolicyBiz Podcast
00:00:15
Speaker
Welcome back to the PolicyBiz Podcast. I'm your host, John Schwabisch. Now, for the last few weeks on the show, we've focused on data and technology and the intersection of those two and how that intersection can punish and profile people of color and low-income people and families. And in some ways, that's been a move away from the core content and the discussions I usually have on the show.
Guest Introduction: Aaron Williams
00:00:38
Speaker
But today, we're going to get back to some core data visualization work.
00:00:42
Speaker
And I'm going to talk with Aaron Williams, who is formerly a data journalist at the Washington Post, and that's where he was when we spoke in late August. He is now at Netflix, doing some great work there, I'm sure. And while at the Post, Aaron produced some amazing visual journalism in 2018, probably my favorite piece of his.
Project on Segregation in America
00:01:03
Speaker
Aaron and his colleague Armand wrote a story about segregation in America, and they created this series of dot density maps that I still think may be some of the best I've ever seen. Just a combination of the text, the colors, even the background of the entire piece is really just amazing. More recently, this was over the summer, Aaron published the code for that project to an observable notebook so that others can use it and model their work after it.
00:01:29
Speaker
We talk a bit about that of the observable notebook and the code, and some of that is a little over my head. But if you're into JavaScript and you're into observable notebooks, you're going to love that part of the discussion.
Challenges for Journalists of Color
00:01:41
Speaker
So we talk about Aaron's work at The Post. We talk about what's coming up for him at Netflix. And we also talk about conversations he's had about challenges journalists of color face in the newsroom, which is an interesting part of our discussion. So I'm sure you're going to like this talk. I'm sure if you've seen his work, you'd love it. So here's my discussion with Aaron Williams.
00:02:02
Speaker
Hey, Aaron, welcome to the show. How are you doing? I'm doing well, John. Thanks for having me. Hanging in there in these, I think, strange times is an understatement. Yeah, no doubt.
00:02:16
Speaker
Well, thanks for coming on the show. I'm excited. You've got a number of great projects, and there's a particular project that I want to dive into in some detail. And you've got a new exciting opportunity coming up on the West Coast, and I want to talk about that.
Aaron's Journalism Background
00:02:30
Speaker
But before we get into all that, maybe be helpful for you to talk a little bit about yourself, your background, and how you ended up at the post on the graphics desk.
00:02:40
Speaker
Yeah. So, uh, again, I'm, uh, and Williams. Um, I'm currently a data reporter at the Washington post. Uh, I'm originally from California. Um, my background is in journalism. I have been working as a journalist for, you know, almost a decade now. And I initially, when I started out in this work, uh, was very interested in focusing on, uh, sort of office, like a neighborhood reporter, like
00:03:06
Speaker
you know, neighborhood reporter in San Francisco. And, uh, I remember like there was one story in particular that I did where the neighborhood I lived in was just Excelsior neighborhood in San Francisco.
Discovering Data Journalism
00:03:19
Speaker
The city had commissioned kind of like a report or like a survey asking people in the neighborhood, you know, how safe they felt in their neighborhood. And so, you know, they had all these tables and graphics they used to like, not only do the survey, but then kind of report what they found.
00:03:34
Speaker
And it was the first time I had kind of discovered the idea of data journalism, right, which was like using data that's being published by the city or by some kind of government, whether local, state or federal and trying to find stories out of that versus like how I had traditionally at the time been reporting, which was like basically.
00:03:52
Speaker
cold calling people and knocking on doors and people being like, who the hell are you? You know, like, which was just not a style I liked. So like, I was like, Oh, if I could work with like data from the city, you know, not only does that or I have like more targeted questions to ask that will hopefully mean I'm doing less of the kind of cold knocking on doors of strangers who are like, I don't trust you.
00:04:15
Speaker
And I could actually report on stories that weren't just floating there happening in the real time. It required knowing the city's publishing schedule around data, getting to know the public information officers and the data folks in the city halls of San Francisco. And so that's what set me on this journey. And that was roughly in 2009, I want to say. And so that was my start. And in the meantime, while I was working on my
00:04:45
Speaker
my journalism career, you know, I was living in San Francisco. You know, it just seems like if you weren't coding, like you weren't cool. And I don't know if like maybe it was just because it was like, you know, like, I was just, you know, this was like at the height of like Zynga and like
00:05:01
Speaker
of like the behemoth tech company, just like Twitter had just moved to Selma. And so this was like when all of that stuff was happening. And I remember thinking like, Oh, I need to like, I actually learned some of the skills because, you know, just in case, you know, I want to change careers or just in case I want to try something new,
Learning to Code in Journalism
00:05:20
Speaker
I'll do it. So I actually remember I went to San Francisco State and I applied to be to do a take a minor in computer science. And my journalism advisor at the time was like,
00:05:29
Speaker
Why would you do that? These are totally separate career choices. She's like, why don't you minor in history or philosophy or something? So I actually ended up minoring in philosophy, which has some parallels to programming, but I didn't talk it upon myself to teach myself how to code. So during that time of going to school and
00:05:49
Speaker
reporting. I was also on the side just like Code Academy and those kinds of things that really exist at the time. But I was like, they were like really like new, but I was like, you know, just getting books off Amazon or like out of the studio library. And I taught myself Ruby, which was also the pop in programming language at the time, you know, so
00:06:08
Speaker
But that was kind of my start into getting into this whole world. And then, you know, you flash forward. After a brief stint at the LA Times, I ended up getting the job at Center for Investigative Reporting, which is now called Reveal. And that's where I really kind of started to build my skill set as a data journalist. That's where I learned Python. That's where I did like my first like real analysis and that really deep into D3 and data visualization and using all of those tools to do investigative reporting.
00:06:35
Speaker
And so, you know, I did that for a couple of years. I didn't work at the Texas Chronicle. And then about five years ago, the Post reached out to me being like, hey, we see what you're doing over in the West Coast. Would you like to bring your skills to Washington, D.C.?
00:06:51
Speaker
And at the time, you know, I was like, you know, I'm never leaving the West Coast, you know, you're gonna have to like drag me kicking and screaming out of here. But yeah, you know, the post had, you know, this was like maybe a year after the police shootings database had just been published, you know, by John Miskins, Wes Lowry, and all the folks who worked on that project at the post, and I was just like enamored by
00:07:13
Speaker
the post's ability to take data visualization and deep investigative reporting to talk about something really crucial in society. And so I was like, I would be stupid not to try to go over there and try to do some of this work and work with those people. So I packed up all my stuff, moved 3,000 miles across the country, and I've been at the post since. So that's kind of the long haul trajectory that got me here. There's a lot of amazing parts of that story, but the one that strikes me is
00:07:42
Speaker
your journalism professor sort of said don't why would you worry about coding and that was like only a decade ago and how much things have changed yeah oh that's amazing i think about that all the time because uh you know and i've talked to that that advisor saying it's like we're friendly but um i've told them that i'm like yeah you were like so off the mark because it's not just that learning how to code for journalism is like
00:08:07
Speaker
like now seems like a no-brainer like software just generally is like a huge facet of like all parts of society at this point right so like you could learn how to code and work in business you could be a journalist you could be you know even a carpenter you know you can like an architect it doesn't matter what skills that you use knowing a little bit of software engineering or coding will get you really far in all of those disciplines and so yeah i think about this all the time now because i'm like
00:08:34
Speaker
And I'm glad I taught myself how to code because it's like an alternate world where I minored in, I don't know, history. And then I just never went that route. My career, my life would be totally different because of that.
Skepticism in Data Journalism
00:08:49
Speaker
Right. What's also interesting about your origin story, as it were, is that you came from a sort of true traditional journalism background, whereas a lot of people who, and now doing DataViz, they come from all sorts of different walks of life.
00:09:05
Speaker
right? They're astronomers, they're economists, they're, you know, whatever. And I'm curious when you are, one thing I've been talking about with people on the show and elsewhere is the difficulty that people who are, their background is more technical. So they're, you know, maybe they're a social scientist or a mathematician or a statistician.
00:09:24
Speaker
And then talking to people is very foreign to them. It's not something that they're used to doing, even though we all acknowledge that that's useful to the skill of being able to talk to the people that we are studying or communicating with. And I just wonder, do you find that you have this additional skill set where talking to the people that you are collecting data about or downloading from some other place is just more natural for you? And does that make your job both easier and the final product better?
00:09:53
Speaker
that part of your you know you're able to just do that and have the experience doing that reporting.
00:09:58
Speaker
Yeah, I mean, I think that to some level, my background as a quote unquote traditional journalist certainly helps. But I think the really the big skill that I think as a journalist that helps us do like when applied to like data journalism and data visualization that is really crucial is skepticism. And I say that because I think if you like watch just even this year kind of, you know,
00:10:26
Speaker
at the kind of onset of the COVID-19 epidemic, you saw kind of like every like armchair economist and like data science student do like a COVID data project. Like, I don't know if you remember, you know, like March through June of this year, it just seemed like everyone suddenly became an epidemiologist. Right? Yeah.
00:10:49
Speaker
And I found that infuriating for a lot of different reasons. One, because it just seemed like there was this mad rush to be like, I have to make the definitive COVID dashboard. And a lot of it was, I think, in good faith. There wasn't a lot of reporting being put out by the federal government. There was a lot of just uncertainty about what was happening. And so I think folks were trying, were actually eager to try to be like, I want to do something about this. So I think a lot of it was in good faith. But I think often what I saw people report,
00:11:17
Speaker
like, you know, whether like their their denominator was really bad, or it just like the data sources they relied on were like super wonky. You know, I just thought, you know, like a lot of the things I saw kind of get propped up in those early days, like as a journalist, the first question you're asked is like, is this data even legit? Who produced it? Right.
00:11:36
Speaker
like, how are they tabulating this? Is it consistent over the time frame that I'm trying to report or trying to visualize? These are questions that I think any economist, social scientist, anybody with any kind of data background wants to ask. But I think skepticism is the biggest trait a journalist has. And usually with a skepticism, that then means she has to go talk to somebody who can either back your skepticism or say, oh, actually,
00:12:00
Speaker
here's something else you should have like you should look at. So I mean, I think that my background in terms of like being able to talk to people certainly helps. But I really do think it's the skepticism and the the idea of not taking data on its face as like legit, that's the most key aspect of the role, I think. Do you think I mean, aside from the COVID dashboards that were littering the internet a few months ago, do you think in general, people are not as skeptical with their data as they should be?
00:12:29
Speaker
Oh, absolutely. I mean, I think just generally, not just data journalists, but I think just when people think of data, there's just this built in assumption that it's without bias, right? That the data whoever collected it was kind of done in this laboratory setting. And it's machine run, and there's no kind of built in assumptions when I mean, I think
00:12:47
Speaker
you know, as you likely know, as probably listeners of this podcast know, you know, data is produced by people. So people's assumptions get put into that data, the data you don't collect is just as crucial as the data you do collect, right. And so I do think that because data science and data visualization is kind of an interesting discipline right now, because if you have both like folks like journalists, and academics are deep in it, but then you also have artists and people who more want to kind of do more
00:13:17
Speaker
not like that they're not like back to the science, but I think there's also like kind of this artistic angle to it. And then you also have folks who are using data and visualization to just like, you know, focus more like analytics metrics in like business building. And so all of these, everyone kind of has built in assumptions and all these different places and things they they're willing to not break on because they are willing to break on. And I think that like all of that together just kind of creates an interesting kind of like conundrum of like,
00:13:45
Speaker
when should you be skeptical of the data? As a journalist, my assumption is always be skeptical. All data you collect has inherent problems, inherent bias, so it's your job to figure out what those are and how much of that then impacts the thing you're trying to visualize or the statement you're trying to make. And just because the bias there doesn't mean that the data is bad, but you just need to know what that bias is before you move forward. Otherwise, you could end up publishing something that's misleading. But I think that there's a total
00:14:14
Speaker
Assumption that all data is good. I think it's changing. I think that with the rise of like Social media and kind of just like we've seen like some bad actors with the use of data I think there is kind of now a more general consensus that not all data is good But I still think that there is this underlying assumption that you know If someone hands you a data set that is perfect. You should just you know Rock with it, which I don't think is true at all
00:14:38
Speaker
Have you had an experience lately where you've been working with a dataset and you've been skeptical about it and you've gotten down some length of the project and said, I can't use these data because of whatever reason, and you've just abandoned a project?
Challenges with COVID-19 Data
00:14:55
Speaker
Uh, I mean, I think the closest example of that in recent memory would be the kind of early COVID data that was being reported by a city, state, and county health departments. And it's not that the data was like misleading. It's just that, um, you know, really early on, you know, like anybody you wanted to know, you want to know like in my state, how many COVID cases are there? Um, how many tests have been done?
00:15:22
Speaker
just some really kind of high level questions. And really early on, me and the folks working on these projects realized that data was just not there, or if it was there in some places, but not in other places. And sometimes they included how many total tests they gave, and sometimes they didn't, or they did briefly, and they removed the data, because they made them look bad. There was just all of this craziness happening with those early dashboard days of COVID.
00:15:46
Speaker
that made it insanely difficult to actually say with like some kind of authority or like you know clarity what was happening and so I think um
00:15:55
Speaker
you know, a lot of the early meetings I was in at the post where we were talking about what was happening, like, like, like the date we were collecting data around this, you know, a lot of the talks were just like me and Steven rich, who's also a data, a data reporter at the post and others and Angie Tran is also a data reporter. Like it was like the three of us basically be like, we can't use this. Like this is bad because like we couldn't figure out how it's like, we knew that just looking through the data that like,
00:16:18
Speaker
It wasn't collected in a consistent enough fashion for us to make a full, you know, full-throated claim of what's happening. Obviously, that changed. We then brought in more folks who helped us collect this, like, scrape and collect data from different health departments, and then that's what now ends up powering the dashboard on the Washington Post.
00:16:38
Speaker
those early though, like that March, April, May zone was just a lot of us trying to figure out like, well, what can we say? Because we, you know, it just felt like the data in some places would be, was being reported, but it was only like a handful of jurisdictions or they had only tested like once or twice, like, you know, so it, so that was more just having to do with how the data was collected less about the data just being like outright bad or wrong. Right, right, right.
00:17:03
Speaker
This is interesting because it's actually a good segue to the project I wanted to spend a little time talking about, which is your segregation diversity maps that you published at the post a couple of years ago. These dot density maps that are just I just think spectacular. And then a few weeks ago you posted the code to an observable notebook and
00:17:25
Speaker
So I want to give you a little bit of space to just talk about the observable notebook and how folks can use it. But maybe before we get into that, you can talk a little bit about the project itself. And then maybe we could also talk about the missing data because the project has, as I recall, six different race and ethnic categories. And of course, there's a lot of diversity across and within those racial groups that are not
00:17:49
Speaker
captured by our standard federal data sets. And, you know, how did you think about having a category, you know, for black people and a category for Asian and Pacific Islander people? Like, how did you think about these these different groups that are, you know, they are missing pieces, they are missing people who report their race in maybe, you know, different ways and capturing and not capturing the diversity of the country?
Mapping Racial Diversity and Segregation
00:18:13
Speaker
Yeah, absolutely. So that was a long question. And I sort of like, it's like, here you go. Just talk about it. Yeah, no, no, no, I just great. No, I think I got what you were kind of setting up. I think I got it. All right. So
00:18:29
Speaker
Yeah, so the project, the story is called America's More Diverse than Ever, but Still Segregated. It published May 2018. It was me, as well as my colleague, Armand Imamjime, who's currently an assignment editor for the graphics desk, as opposed. We've been longtime friends and colleagues for years now.
00:18:48
Speaker
And so this project was born out of my desire to bring some data, some clarity to the question of what's happening in the country. I live in Washington, DC. Before that, I was in Oakland, California. And in both instances, these are cities that have seen rapid change, both in the demographics of the cities, the racial demographics, but as well as the class demographics of those cities.
00:19:16
Speaker
having moved from the Bay Area, which it is still the most expensive place on the planet, but it was near the most expensive place on the planet. They had rapid gentrification. Today, moving to DC, we're kind of the same thing was happening. I had all these kind of conversations with friends of mine who were just kind of like, oh, yeah, I remember when this neighborhood used to look like this, but now it looks like that. And while the Black population of this part of the city is going to be pushed out,
00:19:43
Speaker
And now this is the demographic group that's taking that over. So my question was, OK, well, could we actually use data to show that? So that was the genesis of the project.
00:19:55
Speaker
I didn't want it to also measure the idea of segregation, which, you know, when you look at these dot density maps are really great, but we're just we're showing where people in theory live, but we're not actually measuring segregation. Right. Segregation is like, you know, the actual separation of people along racial lines. Right. And, you know, so what I wanted to also figure out is that can we can we not only show where different demographics have been and are over time, but
00:20:20
Speaker
can we then add a score that says, hey, this block of the city is actually, if you look at the distribution of people in it, is fairly isolated by one racial group. And because of that, that is in some way showing segregation in a way that's just showing where Black and white and Latinx and Asian folks live. That's part of it, but we're not actually scoring the blocks that those people live in. So that was the second aspect of the project.
00:20:50
Speaker
So yeah, and so, you know, I use census data from the 1990 census 2000 census in 2010. So the decennial census years, as well as the latest American Community Server data, which at the time was the 2012 the 2016 five year release.
00:21:05
Speaker
And my colleagues, Dan Keating and Ted Melnick helped me out a lot because census geography changes over time. So they helped me standardize to the 2010 census geography. So that way we could have, you know, apples to apples comparison of the same blocks. Yeah. So what we did there was, you know,
00:21:25
Speaker
I looked at a couple of different places. I wanted to kind of understand the history of segregation in America and I spoke with a researcher at the American University whose name is Michael Bader who gave me like a helpful way to think about it which was kind of like you kind of have three ideas of
00:21:44
Speaker
of segregation, you kind of have the legacy of historical segregation that was set up right after the Civil War during the Reconstruction era. So you have white flight that happens in the 50s, 60s, 70s. And then you kind of have the new, since the 70s and beyond, you now also have kind of these rapidly diversifying suburbs. So a great example of that is Northern Virginia,
00:22:12
Speaker
in kind of how communities like Annandale and Fairfax and Falls Church and those communities have rapidly become more like Latinx and Asian American over the last 20, 30 years. And then you have places like Houston, Texas that to some level has level of segregation, but because of just the fact that the city was kind of developed later compared to like a Chicago or New York or DC, because of that, Houston has also its own unique mix of
00:22:38
Speaker
racial integration and segregation. So I tackled Chicago, D.C., Houston as kind of three examples of how segregation and racial integration works in the U.S. And so I did maps around those three cities. And then finally, we just published all the data for the entire U.S. in a map box, vector map that allows you to type anywhere in the U.S., see where you grew up, see how either integrated your neighborhood is or how segregated your neighborhood
Design Choices in Segregation Maps
00:23:09
Speaker
And hopefully that then led you to either have conversations with your local politicians or your family or spark something in how you felt. But yeah, that was the genesis of the project. Out of all the work I've done as a journalist, I think I'm most proud of. It's the one that got me to this podcast. It's the one that folks often ask me about. And so I'm really proud of it. And I think it really shows the power of what you can do with data and visualization.
00:23:35
Speaker
to tackle something as insidious but crucial as racial segregation. Well, I'll just say this. So I saw you talk about this project at OpenVizConf, I think, in Paris a few years ago. And I remember asking, you had chosen this sort of dark gray background for the whole thing. And I was like, why this dark gray background? And
00:23:57
Speaker
And I still remember your response was just because it looks whack, like it just it just looks good. But there is a there is a design element to this piece that I think is really striking. You want to talk maybe a little bit about that and also maybe folks should definitely look at this project maybe before they maybe they pause here and take take a look at the project if they haven't seen it. But just also like the choices of color that you use throughout, I think just are striking in the way they pop off the pop off the page.
00:24:28
Speaker
Absolutely. All right. So yeah, the color choices I picked for the maps were we deliberately chose a dark background. And the reason for that was just the fact that we use like what a lot of social sciences use when they use census data to break up race into six categories. So we chose, you know, black, white, Hispanic,
00:24:54
Speaker
Asian, Pacific Islander, Native American, and then kind of everybody else. And so everybody else includes any one of two or more races, someone who chose a race that did not fall into any of those categories, anything like that. And so we had to pick six distinct colors. And so Armani Mamjume, we're playing with several different color palettes. And so we decided on these six multi-hued colors that we felt like were a good representation of
00:25:19
Speaker
the data that weren't obviously racist in their color choices, and that were to some level colorblind safe as well, which was like no easy task. It was insanely difficult to choose six colors at that threshold. And so when we initially did the data,
00:25:39
Speaker
or visualize the data, we had it on a white background. And it looks fine. It looks very cool. And if you go to the observable notebook, you'll see it's also on a white background, just because that's how observable is designed. But we then were just messing around one day, and we decided to place the data on a soft matte black background, if you will. And the colors just really just pucked, just because of the sheer contrast between light and dark.
00:26:09
Speaker
And I think that we just decided that's kind of the aesthetic we should go with for this project because, you know, and also like, you know, you look at a lot of Washington Post projects and just a lot of news site projects in general. It's just a lot of serif type on black type on white backgrounds. You know, most news websites to some level fit the same kind of like genre palette of like what you see on the news article website. So by going with white type on black, we just felt like it
00:26:38
Speaker
it kind of bucked the trend of what you would normally see on a news website. And to your point when you asked her the question at OpenViz, why did I choose it? I just thought it looked really dope. At the end of the day, I'm a hip hop kid at heart. And so I was like, it just looked really cool. I would put this in my studio or in my apartment or whatever. But yeah, but color is a huge part of this project, I think, too.
00:27:05
Speaker
And then even when we created the diversity layer to actually measure the level of segregation in every census block in the data, we chose a very traditional green to purple diverging color scheme. And that was much easier to do because you're only grading on a 0 to 1 diverging scale, which allowed us to keep the colors fairly simple.
00:27:30
Speaker
We really tried to create palettes that were really striking and kind of popped off this black background and that would hopefully keep you scrolling and clicking on the project. No, I agree. I mean, I think the pop is what does it for me, right? Like just like when I look at this map of the entire United States,
00:27:49
Speaker
It's, you know, the red really pops off, the yellow pops and also the areas where there are not a lot of people living, right? Like the, you know, the Rocky Mountains, there's just this sort of like emptiness in the country, which I think also like the lack of data, as you sort of talked about alluded to earlier, the lack of data is part of this story.
00:28:07
Speaker
Absolutely. Yeah, it was actually really funny. I remember one of the comments after we published this story, it's like someone was like, you really want me to believe that all those dark empty spots, like people don't live there? And I was like, yeah, like those are like mountain ranges. Like, I mean, there might be some people like, you know, like it would somewhere, but not to the point where if you zoom out to the entire, you know, American continent that you're going to see, your North American continent that you're going to see a dot pop up.
Reactions to Racial Distribution Project
00:28:33
Speaker
It was just so funny because yeah, because I think it also, you know, if you look at this map,
00:28:36
Speaker
The quote unquote black belt through the South really comes through really strikingly. So, you know, the kind of huge Hispanic population that populates the, you know, American Southwest and obviously California really come.
00:28:52
Speaker
really emerges and then another kind of feature of this project I really enjoy is that if you look around the Phoenix, Arizona area and kind of going north into the Dakotas, you can actually see where the large you know Native American populations are so you see Navajo nations like really big.
00:29:10
Speaker
uh concentration right there um you know in phoenix and then kind of going up to the codense they assorted uh you know native american first station groups that are there so i mean i think you know again it really kind of shows the the power of this data i think you know
00:29:25
Speaker
if you were to ask anybody in the U.S. kind of which racial groups populate which parts of the country, we kind of know this to some level because of just being American and kind of knowing kind of the setup of the nation, but to see it actually put on a map where you can actually identify, oh, I've been there. Oh, this matches my experience. Or, you know, maybe you're like, hey, I never actually thought about the concentration of this people group in that area. What's the history behind that? You know, I think that, you know, that's where this project I think really kind of elevates the discourse around, you know,
00:29:54
Speaker
where people live in America. So right, right. Can you talk a little bit about the observable notebook and I guess why you decided to put the code up in observable and how people can use it for their own dot density maps?
Introduction to Observable Tool
00:30:13
Speaker
Absolutely. So for the listeners of this podcast are not familiar observable is a
00:30:21
Speaker
online web-based notebook environment that uses its kind of its own, it uses JavaScript, but it's kind of like its own flavor of JavaScript. It's like, if you know JavaScript, you can mostly work in it, but it kind of has like its own kind of set of keywords and syntax it also provides. But it's a really great way to explore JavaScript code without setting up a
00:30:47
Speaker
web server and kind of like your own coding environment. You know, you just, all you have to do is go to observablehq.com and start coding, you know, and it works entirely in your browser, which is really nice. And so again, when I published this project two years ago, I think probably the first question I got was like, how the hell did you do this? Right.
00:31:06
Speaker
And I think a lot of people thought I used traditional techniques. So a lot of folks who have built dot density maps in the past have used tools like QGIS or QGIS, or have used R or Python, which have really mature statistical and GIS tools for doing this exact kind of work.
00:31:28
Speaker
But a lot of folks were surprised that I did it entirely in JavaScript. And so, which I think really kind of for me was also I wanted to in some way you know I use all those languages I use Python I use R I use QGIS I love all those tools but at the time I was just really deep in the JavaScript and
00:31:44
Speaker
I wanted to see if there was a way to do the level of sophistication in terms of GIS analysis that you would do with Python and R, but in a language like JavaScript that also, because it's in the browser, offers a lot of advantages that Python and R do not. I told people I did it in Node.js or JavaScript, and they were like, well, can you write a blog on how to do this?
00:32:08
Speaker
you know, as you probably recall, I had not only had I just published a project, I was getting ready to talk about it at OpenVizConf. So I was not really in a position to like also do like a code deep dive. Also, if for anyone who's ever published a behemoth of either like an analysis or a project, you're usually not
00:32:28
Speaker
eager to go right back into that code. You kind of want to like, never think about it again. All this time thinking about this one thing. So I was also kind of eager to like not look at the code anymore. Also, I mean, I wrote this code, you know, for a while, I think this project started it, I was kind of doing it in some ways as a passion project. So I was kind of doing it in my off time between stories at the post. And it wasn't until you know, February 2018 that my editor at the time Katie Hink
00:32:56
Speaker
kind of gave me the full, she was like, I'm gonna take everything off your plate, you can just go deep on this. And so that's when, and then I brought Armand in and he kind of had to read my tea leaves to figure out what I was actually trying to do with the code and then together we finished the project. But like that code was not written for like public consumption at all. Like it was written in kind of my,
00:33:17
Speaker
And I think I'm a pretty competent software developer, but I was not writing this code to be used by anybody else but myself. So there's just all these things that were going to have to happen in order for me to even explain this code. Also, I have tried and failed at least, I want to say, 15 times to start a blog about my work. And I have just come to the conclusion that I don't think I want to blog. I think I'm just bad.
00:33:45
Speaker
I don't want to maintain a blog. I don't want to promote it on Hacker News or on Twitter. I just can't. I don't want to do it. Me working at Google, I was like, no. So I was like, OK. So if you go to my website, my personal website now, it's just a really brief bio and a link to my Twitter, my GitHub, and I think my LinkedIn. I think that's it.
00:34:10
Speaker
So I was like, now I got to turn this into a blog. That's like more coding. I don't want to code anymore. I was so tired of wanting to code. So basically, I kept telling people I'll do it, but then I didn't do it. But basically, once observable came out, there now came this huge opportunity
00:34:29
Speaker
because I wrote the code in JavaScript to actually begin porting it over to a web environment. So observable, I mean, it got announced a while back by Mike Bostock, who's also the creator of D3. But I think it kind of
00:34:44
Speaker
the beta came out maybe I wanted just over a year ago or a year and a half ago, something like that. But I remember I started using the observable for just even my own production work and it kind of has and it still is in a lot of ways the main way I kind of explore data using JavaScript. I just find it incredibly comfortable to use and I love that you can use kind of the full ecosystem of
00:35:05
Speaker
NPM, which is Node Package Manager. It allows you to use all these tools that normally you need to run a server and have JavaScript set up on your computer, which requires you setting up a development environment, working in terminal. With Observable, I can just literally open up a tab in Firefox or Chrome and just start doing it. The upfront cost of setup is so much lower now.
00:35:30
Speaker
And so I was already using Observable for some of my own work. And then finally, it kind of came to a point where I was like, you know what, you know, Observable had matured a bit. I'd since moved from the graphics team to the investigative desk at the Washington Post, which kind of meant I wasn't doing the kind of day to day reporting that I had to do, that I was doing as a graphics reporter.
Porting Code to Observable for Accessibility
00:35:49
Speaker
I kind of had a little more time on my hands as I was kind of investigating and doing more. And just also that job required less kind of front end development. I was doing a lot more
00:35:59
Speaker
you know, analysis and stats and Python and R. So, you know, I wasn't running code as much. And so, you know, I was like, you know what, I'm going to take some time and actually, you know, break down exactly how I did this. And it took me a while to do it, because again, I hate blogging. So, you know, I was effectively creating a blog. But like the one thing that observable allowed, which is something I wanted to show people with this project, was like, it's one thing for me to just like copy and paste code to like a code block on like a blog.
00:36:28
Speaker
And it'd be like, hey, if you can read this code, this is how it works, right? But by using Observable, you could actually run the code in the browser, which I think kind of totally shifted the power of actually writing about it. Because now not only am I explaining how I did it, you can literally in real time see the code running. You can go in there and change the code. If you want to change the colors I use, if you want to change the scale of the map, that's all available to you directly in the browser.
00:36:54
Speaker
And you can manipulate the code in real time. And that didn't exist, project published. So I think the power of moving it to observable is that it allowed me to not only explain the code, but it allows for anyone who's interested in it to literally go and poke around the code and manipulate it so they can hopefully get a better understanding of how it works.
00:37:15
Speaker
So yeah, I think without observable, you likely would not still have seen this code because it would have required me to set up a blog, which again, as I've said, I'm really bad at doing it.
00:37:29
Speaker
Do you have a good name in your head for a blog? You just didn't get that far. I don't know. My website is ACWX.net. I guess it would be like slash blog. I don't know. I could probably think of some really, I don't know, nerdy, coming on. I don't know, man.
00:37:52
Speaker
that only certain people would understand would get the, there's like an inside joke in the name of the blog. Yeah, exactly. Yeah. And I don't know, man. Like, I mean, it was funny is my Twitter kind of handle about Aaron that I use like basically everywhere. I actually came up with that because in Firefox, if you type about colon config, you can like adjust the browser.
00:38:17
Speaker
like the browser settings underneath the hood of Firefox. So that's where my username actually comes from. It's like about Colin Aaron, as if you're keeping under the hood of my life. But I came up with that handle when I was like 19, 20, something like that. And dear listeners, if you hear, I am not 19 or 20 anymore, that was the one coming up. So I'm kind of done with the overwrought, look at me. I understand how to, clearly you know I know how to code,
00:38:47
Speaker
I'm going to hammer that over in the name of my blog or my handle. I just haven't changed my handle because it now just kind of like stay. So I'm just like, I'm doing that's right. It becomes this thing that you can't get rid of, right? Yeah, basically what I'm trying to explain to everyone listening is that I'm just incredibly lazy. Like I don't want to do any more work that I absolutely have to do. And the only reason why
00:39:08
Speaker
you know, I was able to publish this code for your consumption is because a bunch of other very smart people built an entire notebook for me to do that play. That is great. That's great. Okay, so we've learned we've learned a few things about you today.
00:39:25
Speaker
Um, so before we wrap up, I want to just talk for a minute about, uh, you're leaving the post, um, and at least in the short term, virtually heading back to the West coast.
Transition from Journalism to Netflix
00:39:34
Speaker
Uh, do you want to talk about the new gig you have coming up? Yeah, absolutely. So, um, I've been at the post for about five years and, um, I decided I wanted to try to, you know,
00:39:43
Speaker
explore how to use data in just a different environment and also think about, you know, a lot of my work has focused around racial equity and data equity. And so I wanted to see what it's like to tackle those problems in just an entirely different domain. And so I recently accepted a job at Netflix, where I'll be working on the content science team as a senior visualization engineer.
00:40:05
Speaker
I can't really go into too many details about what the job entails because I have not started the job yet. And hopefully when this podcast comes out, I am still employed with that job. I have no reason to think otherwise, but 2020 is crazy like that. But yeah, I think it's just a huge opportunity.
00:40:25
Speaker
When you work in the data science and data journalism space, it sometimes felt like to me that a lot of us who was particularly as data journalists, we were kind of all playing a really high-level version of musical chairs where we were all just getting each other's jobs.
00:40:44
Speaker
Um, where like, you know, one person might work at the post and then they might work at the New York Times and then they might go to the Chicago Tribune or an LA Times. And then you might go back to the Washington Post or back in the New York Times. We might go to like a nonprofit like ProPublica or the Marshall Project. And like, you know, and I have nothing but respect and admiration for all of my peers at those organizations.
00:41:05
Speaker
journalism that comes out of them. But I think, you know, as I was thinking about my career, I wanted to try to just see like, well, what else is out there? What else could I do? And for me, getting a similar style job at another news entity wasn't really what was calling me. I wanted to try to see, well, could I bring journalistic thinking and thinking around equity to a space that maybe isn't inherently journalistic?
00:41:29
Speaker
Like it isn't like a news business by design, right? And so, you know, Netflix, you know, it's like this behemoth of a company that, you know, is having like an insane year because of, you know, COVID, you know, and they just, they have a lot of data and I'm, you know, and so when they first reached out to me, you know, I had actually had no idea that they were even doing database work. Like, you know, I knew that Elijah Meeks used to work there. He's a very well-known D3 and database person.
00:41:57
Speaker
And I knew a couple of other people like Susie Liu, who's also a really fantastic database developer. But I didn't really know that they were looking for people who had my skill set to apply to some of their work around content specifically.
00:42:15
Speaker
And so, yeah, so, you know, we talked about it and it's been, it was kind of like an ongoing process for a while, but they finally came back to me and say, like, we're really eager to like explore the ideas you're in. And I felt like it was just like a really great time for me to, you know, just try my hand at something different. So, so yeah, so, you know, it's still very new, you know, I'm still finishing up my tenure at the post. Um, and then I'll be starting there in a few weeks, but yeah, but you know, it's going to be interesting to see what it's like to apply these kinds of skills in a different domain.
00:42:45
Speaker
Right. Absolutely. Yeah, it sounds great. And I trust you'll get a free Netflix subscription too. I will not hope so. I mean, yeah, like, you know, I've been watching a lot of Netflix as is everyone right now. So, you know, you know, I'm definitely you know, that's like another thing I'll take on. But yeah, but you know, and I think like,
00:43:05
Speaker
I kind of, I alluded to this earlier in the podcast, but like data is everywhere and working with data is everywhere. And I think like, um, you know, in the same way that my advisor, you know, it was like, why would you be a data scientist? Uh, you know, or why would you have a code if you want to be a journalist, which now seems crazy, you know, uh, to say the way I'm kind of thinking about this is that like it's a lot of people are not even asking me like, why would you as a journalist go work at entertainment company?
00:43:31
Speaker
But I feel like my entire career has always been about taking skills that don't seem like they match and then finding a way to make them match. Right. And I think, you know, certainly what you like, as I also talked about in packets, bringing skepticism, bringing, trying to bring justice and equity to work. I mean, I think being able to apply that in a space like Netflix in a place that reaches millions of people and a company that is, you know, deciding the kinds of content that we watch in our homes, you know, there's a lot of power in that.
00:44:01
Speaker
And so, you know, for me, I think it's kind of interesting. I think on paper, it might seem like I'm doing like an entirely different job. But I think in some ways to me, it feels like I'm actually this is like a natural conclusion of the work I do. Like it's not actually like a totally different role. In many ways, it's taking the kind of same concepts and ideas I've played with over my career and just kind of taking it to a different direction. But it's not different in that it's unrelated. It's just like not in the same industry.
00:44:29
Speaker
But the work is very much Congress. So I think that that's the way I've been thinking about it. Interesting.
00:44:36
Speaker
Well, it sounds like a great gig and hopefully you'll be able to share some of the work that that you do and comes out of it or at least share the improvements in Netflix that come out and we'll have a chat again in a little while and figure out. Yeah, totally. And hopefully, you know, like I said, I get my little Twitter threads that like, I don't think I'm ever going to stop being a journalist. You know, so it remains to be seen what my work was going to be like. But, you know, I do hope
00:44:59
Speaker
you know, I can get a little bit of freelance work here and there, kind of, you know, publishing stuff. Because, you know, like, I don't think, you know, as you can imagine, just because I stopped working full time in media doesn't mean I don't stop thinking about stories. And, you know, you know, thankfully, there's stuff like observable that allows me to kind of play with code and publish stuff pretty easily. So, you know, certainly I think I'm going to try to get more stuff out on that and observable specifically, and then hopefully
00:45:28
Speaker
know, we'll put up in places. So yeah, so we'll see. But yeah, I'm excited. It'll be a nice change of pace. So hopefully, when I come back on the podcast, I'll have more to talk to you about. That's great. That's great. Well, I'm looking forward to it. I'll book you right now. We'll do it again. Yeah. Thanks so much, man. This was great. It was great to chat with you. Yeah, absolutely. Thanks for having me. I can't wait to hear the chat live. Alright, thanks, man. Thanks, man.
00:46:00
Speaker
And thanks for everyone to turning into this week's episode of the show. I hope you learned a lot. I hope you will check out some of Aaron's work from the Washington Post. And if you are able and interested, you go over to his observable notebook and take use of the fact that he's opened up this code for you to use. It's an amazing resource.
00:46:18
Speaker
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00:46:42
Speaker
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00:47:17
Speaker
A number of people help bring you the PolicyDiz podcast. Music is provided by the NRIs, audio editing is provided by Ken Skaggs, and each episode is transcribed by Jenny Transcription Services. The PolicyDiz website is hosted by WP Engine and is published on WordPress. If you would like to help support the podcast, please visit our Patreon page.
00:47:45
Speaker
Not too much noise