The problem with developing election data expertise is that they make you cover elections.
Even as they go, this was a rough one, and I'm just now starting to feel like I've recovered. After getting through a grueling primary season, we ended up rewriting our entire general election rig — we've got a post on the NPR News Apps blog about it, which is a pretty good overview.
This wasn't my first election night. But it was one of the biggest development efforts and teams I've led in journalism, and obviously the stakes were high. In the end, I'm mostly satisfied with the work we did — there's always room for improvement. I think 2020 is a year I'll look back on in terms of the simple (our technical choices) and the complex (weighing our responsibility for the results).
For the primaries, I wrote our results displays as a set of custom elements. That worked well: it was fast, expressive, and with a little work to smooth over the API (primarily adding support for automatic templating and attribute/property mirroring), it was a pretty pleasant framework. However, for the general election, I wanted to build out a single-page application that shared state across multiple views. I didn't have a ton of experience with React, and it seemed like a worthwhile experiment (technically, we used Preact, but it's the same thing in practice).
Turns out I didn't love the experience, but it did have advantages. HTML custom elements do not come with a templating solution built-in, and they don't have a good way to pass non-string data across element boundaries. I wrote a miniature "data to element mapping" function to make that process easier, but it was never as straightforward as JSX templates were. In general, React's render cycle is reasonably pleasant to use, and easy to train people on. It's clear that this is what the library was primarily designed to do.
Unfortunately, it often feels like React doesn't really know how to organize the non-rendering parts of a web application, and when you're building live election results those parts matter. Unlike Vue or custom elements, which separate updates to display and data, changes to a React component properties and state all get piped through the render cycle. This is a poor match for operations that don't easily map to an idempotent template value, like triggering a server fetch or handling focus for accessibility purposes.
So, long story short, it works, and I think it's probably good enough to re-use for another four to eight years. But I went into the experience hoping to see if there was some hidden virtues of React that I just hadn't found, and largely I was disappointed.
It was obvious to anyone paying the slightest bit of attention that Trump was not going to accept the results of this election if he didn't win, and that his supporters would absolutely be flooding the country with misinformation about it. He'd done it before: in 2016, among other lies, he handed out misleading maps to try to persuade visitors that he had actually won the popular vote and the majority of the country.
But five days before that report surfaced, as usual having utterly failed to read the room, the New York Times had published a largely identical map, effectively pre-validating Trump's choice. Much like Trump's 2020 strategy, this shouldn't have been hard to predict: a map of vote margin by precinct is considered wildly misleading by data visualization experts, and right-wing operatives have consistently overemphasized the mostly-empty land mass that makes up "real America" when they're working the refs. If newsrooms themselves don't understand (or care) how their visual reporting contributes to misinformation, how can we expect voters to differentiate between journalism and lies?
My worst nightmare, going into 2020, was that Trump or someone in his orbit would use our reporting to try to illegally retain power. So we planned for uncertainty. We had a big note at the top of the page to warn readers that mail-in voting could delay results. We didn't mark races as "leading" on any displays until they hit 50% of votes in, to reduce the well-known red-to-blue shift from vote tabulation. We built new displays to emphasize electoral weight over geographic size. And behind the scenes, I built additional overrides into the pipeline just to be safe: although we never used them, there was a whole facility for flagging races with arbitrary metadata that could have been used for legal challenges or voting irregularities.
(In fact, if I can offer one bit of advice to anyone who's new to election results, it's to be extremely pessimistic about the things you might need to override. Since this wasn't my first rodeo, I had control sheets already set up that would let us reset candidate metadata, ballot rosters, and race calls. Sure enough, when Maine's public radio stations wanted long-shot independent candidates included to highlight their impact on ranked-choice voting for Susan Collins' seat, despite not clearing our vote threshold for display, it was just a matter of updating a few cells. Same for when data irregularities muddled the district results coming from those states that proportionally allocate their electoral votes.)
I honestly wanted to go further with our precautions, but I'm satisfied with what we got approved in a fairly traditional newsroom. And in the end, we got lucky: it wasn't actually that close. Biden won by the same electoral margin that Trump had won in 2016, and across enough states by a high enough margin that it couldn't be flipped with a single lawsuit (even assuming Trump's lawyers were competent which... was not the case).
So the good news was that we didn't have to worry about NPR results being posted to Trump's Twitter feed. The bad news, as you know, is that the losers of the election spent the next two months undermining those results, lying about illegal voting, and eventually inciting a riot by white supremacists and QAnon conspiracy theorists at the Capitol during final certification of the electoral college. Kind of a mixed bag.
No, this election wasn't like any other. However, I hope it serves as a chance for all of us who do this work to think carefully about what our role is, and how we use the power we've been given. I have long argued that real-time election results are a civic disaster — they're stressful, misleading, and largely pointless — but that ship has sailed, and like a hypocrite I cash the checks for building them regardless. In the era of The Needle, news organizations aren't going to give up the guaranteed traffic boost no matter how unhealthy it is for the country. At the very least we can choose to design responsibly, and prioritize more than "faster results" and "stickier pages." We're not big on long-term lessons in this industry, but we have to start somewhere.
A proposal for responsible and ethical publication of personally-identifiable information in data journalism
Thanks to Helga Salinas, Kazi Awal, and Audrey Carlsen for their feedback.
Over the last decade, one of the goals of data journalism has been to increase accountability and transparency through the release of raw data. Admonitions of "show your work" have become common enough that academics judge our work by the datasets we link to. These goals were admirable, and (in the context of legitimizing data teams within legacy organizations) even necessary at the time. But in an age of 8chan, Gamergate, and the rise of violent white nationalism, it may be time to add nuance to our approach.
This document is concerned primarily with the publication of personal data (also known as personally-identifiable information, or PII). In other words, we're talking about names, addresses or contact info, lat/long coordinates and other geodata, ID numbers (including license plates or other government ID), and other data points that can be traced back to a single individual. Much of this is available already under the public record, but that's no excuse: as the NYT Editorial Board wrote in 2018, "just because information is public doesn't mean it has to be so easy for so many people to get." It is irresponsible to amplify information without thinking about what we're amplifying and why.
Moreover, this is not a theoretical discussion: many newsroom projects start with large-scale FOIA dumps or public databases, which may include exactly this personal data. There have been movements in recent years to monetize these databases--creating a queryable database of government salaries, for example, and offering it via a subscription. Even random public records requests may disclose personal data. Intentionally or not, we're swimming in this stuff, and have become jaded as to its prevalence. I simply ask: is it right for us to simply push it out, without re-examining the implications of doing so?
I would stress that I'm not the only person who has thought about these things, and there are a few signs that we as an industry are beginning to formalize our thought process in the same way that we have standards around traditional reporting:
In her landmark 2015 book The Internet of Garbage, Sarah Jeong sets aside an entire chapter just for harassment. And with good reason: the Internet has enabled new innovations for old prejudices, including SWATting, doxing, and targeted threats at a new kind of scale. Writing about Gamergate, she notes that the action of its instigator, Eron Gjoni, "was both complicated and simple, old and new. He had managed to crowdsource domestic abuse."
I choose to talk about harassment here because I think it provides an easy touchstone for the potential dangers of publishing personal information. Since Latanya Sweeney's initial work on de-anonymizing data, an entire industry has grown up around taking disparate pieces of information, both public and private, and matching them against each other to create alarmingly-detailed profiles of individual people. It's the foundation of the business model for Facebook, as well as a broad swathe of other technology companies. This information includes your location over time. And it's available for purchase, relatively cheaply, by anyone who wants to target you or your family. Should we contribute, even in a minor way, to that ecosystem?
These may seem like distant or abstract risks, but that may be because for many of us, this harassment is more distant or abstract than it is for others. A survey of "news nerds" in 2017 found that more than half are male, and three-quarters are white (a demographic that includes myself). As a result of this background, many newsrooms have a serious blind spot when it comes to understanding how their work may be seen (or used against) underrepresented populations.
As numerous examples have shown, we are very bad as an industry at thinking about how our power to amplify and focus attention is used. Even if harassment is not the ultimate result, publishing personal data may be seen by our audience as creepy or intrusive. At a time when we are concerned with trust in media, and when that trust is under attack from the top levels of government, perhaps we should be more careful in what data we publish, and how.
Finally, I think it is useful to consider our twin relationship to power and shame. Although we don't often think of it this way, the latter is often a powerful tool in our investigative reporting. After all, as the fourth estate, we do not have the power to prosecute or create legislation. What we can do is highlight the contrast between the world as we want it to be and as it actually is, and that gulf is expressed through shame.
The difference between tabloid reporting and "legitimate"journalism is the direction that shame is directed. The latter targets its shame toward the powerful, while the former is as likely to shame the powerless. In terms of accountability, it orients our power against the system, not toward individual people. It's the difference between reporting on welfare recipients buying marijuana, as opposed to looking at how marijuana licensing perpetuates historical inequalities from the drug war.
Our audiences may not consciously understand the role that shame plays in our journalism, but they know it's a part of the work. They know we don't do investigations in order to hand out compliments and community service awards. When we choose to put the names of individuals next to our reporting, we may be doing it for a variety of good reasons (perhaps we worked hard for that data, or sued to get it) but we should be aware that it is often seen as an implication of guilt on the part of the people within.
I want to be very clear that I am only talking about the public release of data in this document. I am not arguing that we should not submit FOIA or public records requests for personal data, or that it can't be useful for reporting. I'm also not arguing that we should not distribute this data at all, in aggregated form, on request, or through inter-organizational channels. It is important for us to show our work, and to provide transparency. I'm simply arguing that we don't always need to release raw data containing personal information directly to the public.
In the spirit of Maciej Ceglowski's Haunted by Data, I'd like to propose we think of personal data in three escalating levels of caution:
When creating our own datasets, it may be best to avoid personal data in the first place. Remember, you don't have to think about the implications of the GDPR or data leaks if you never have that information. When designing forms for story call-outs, try to find ways to automatically aggregate or avoid collecting information that you're not going to use during reporting anyway.
If you have the raw data, don't just throw it out into the public eye because you can. In general, we don't work with raw data for reporting anyway: we work with aggregates or subsets, because that's where the best stories live. What's the difference in policy effects between population groups? What department has the widest salary range in a city government? Where did a disaster cause the most damage? Releasing data in an aggregate form still allows end-users to check your work or perform follow-ups. And you can make the full dataset available if people reach out to you specifically over e-mail or secure channels (but you'll be surprised how few actually do).
In cases where distributing individual rows of data is something you're committed to doing, consider ways to protect the people inside the data by anonymizing it, without removing its potential usefulness. For example, one approach that I love from ProPublica Illinois' parking ticket data is the use of one-way hash functions to create consistent (but anonymous) identifiers from license plates: the input always creates the same output, so you can still aggregate by a particular car, but you can't turn that random-looking string of numbers and letters back into an actual license plate. As opposed to "cooking" the data, we can think of this as "seasoning" it, much as we would "salt" a hash function. A similar approach was used in the infosec community in 2016 to identify and confirm sexual abusers in public without actually posting their names (and thus opening the victims up to retaliation).
Once upon a time, this industry thought of computer-assisted reporting as a new kind of neutral standard: "precision" or "scientific" journalism. Yet as Catherine D'Ignazio and Lauren Klein point out in Data Feminism, CAR is not neutral, and neither is the way that the underlying data is collected, visualized, and distributed. Instead, like all journalism, it is affected by concerns of race, gender, sexual identity, class, and justice.
It's my hope that this proposal can be a small step to raise the profile of these questions, particularly in legacy newsrooms and journalism schools. In working on several projects at The Seattle Times and NPR, I was surprised to find that although there are guidelines on how to ethically source and process data, it was difficult to find formal advice on ethical publishing of that same data. Other journalists have certainly dealt with this, and yet there are relatively few documents that lay out concrete guidelines on the matter. We can, and should, change that.
This post was originally written as a lightning talk for SRCCON:Power. And then I looked at the schedule, and realized they weren't hosting lightning talks, but I'd already written it and I like it. So here it is.
I want to talk to you today about election results and power.
In the last ten years, I've helped cover the results for three newsrooms at very different scales: CQ (high-profile subscribers), Seattle Times (local), and NPR (shout out to Miles and Aly). I say this not because I'm trying to show off or claim some kind of authority. I'm saying it because it means I'm culpable. I have sinned, and I will sin again, may God have mercy on my soul.
I used to enjoy elections a lot more. These days, I don't really look forward to them as a journalist. This is partly because the novelty has worn off. It's partly because I am now old, and 3am is way past my bedtime. But it is also in no small part because I'm really uncomfortable with the work itself.
Just before the midterms this year, Tom Scocca wrote a piece about the rise of tautocracy — meaning, rule by mulish adherence to the rules. Government for its own sake, not for a higher purpose. When a judge in Nebraska rules that disenfranchising Native American voters is clearly illegal, but will be permitted under regulations forbidding last-minute election changes — even though the purpose of that regulation is literally to prevent voter disenfranchisement — that's tautocracy. Having an easy election is more important than a fair one.
For those of you who have worked in diversity and inclusion, this may feel a little like the "civility" debate. That's not a coincidence.
I am concerned that when we cover elections with results pages and breaking alerts, we're more interested in the rules than we are in the intended purpose. It reduces the election to the barest essence — the score, like a football game — divorced from context or meaning. And we spend a tremendous amount of redundant resources across the industry trying to get those scores faster or flashier. We've actually optimized for tautocracy, because that's what we can measure, and you always optimize for your metrics.
But as the old saying goes, elections have consequences. Post-2016, even the most privileged and most jaded of us have to look around at a rising tide of white nationalism and ask, did we do anything to stop this? Worse, did we help? That's an uncomfortable question, particularly for those of us who have long believed (incorrectly, in my opinion) that "we just report the news."
Take another topic, one that you will be able to sell more easily to your mostly white, mostly male senior editors when you get back: Every story you run these days is a climate change story. Immigration, finance, business, politics both internal and domestic, health, weather: climate isn't just going to kill us all, it also affects nearly everything we report on. It's not just for the science stories in the B section anymore. Every beat is now the climate beat.
Where was climate in our election dashboard? Did anyone do a "balance of climate?"
Isn't that an election result?
What would it look like if we took the tremendous amount of duplicated effort spent on individual results pages, distributed across data teams and lonely coders around the country, and spent it on those kinds of questions instead?
The nice thing about a lightning talk is that I don't have time to give you any answers. Which is good, because I'm not smart enough to have any. All I know is that the way we're doing it isn't good enough. Let's do better.
[SPARSE, SKEPTICAL APPLAUSE]
About two months ago, just before sneaking out the back door so that nobody in the newsroom would try to do one of those mortifying "everyone clap for the departing colleague" routines, I sent a good-bye e-mail to the Seattle Times newsroom. It read, in part:
I'm deeply grateful to Kathy Best, who made the Interactives team possible in 2014. Kathy doesn't, I think, get enough credit for our digital operation. She was always the first to downplay her expertise in that sphere, not entirely without reason. Yet it is hard to imagine The Seattle Times taking a risk like that anymore: hiring two expensive troublemakers with incomprehensible, oddball resumes for a brand-new team and letting them run wild over the web site.
It was a gamble, but one with a real vision, and in this case it paid off. I'm proud of what we managed to accomplish in my four years here on the Interactives team. I'm proud of the people that the team trained and sent out as ambassadors to other newsrooms, so that our name rings out across the country. And I'm proud of the tools we built and the stories we told.
When I first really got serious about data journalism, the team to beat (ironically enough, now that I've moved to the Windy City) was the Chicago Tribune. It wasn't just that they did good work, and formalized a lot of the practices that I drew on at the Times. It was also that they made good people: ex-Trib folks are all over this industry now, not to mention a similar impact from the NPR visuals team that formed around many of the same leaders a few years later. I wanted to do something similar in Seattle.
That's why there was no better compliment, to my ears, than when I would talk to colleagues at other newsrooms or organizations and hear things like "you've built a pretty impressive alumni network" or "the interns you've had are really something." There's no doubt we could have always done better, but in only four years we managed to build a reputation as a place that developed diverse, talented journalists. People who were on or affiliated with the team ended up at the LA Times, San Francisco Chronicle, Philadelphia Inquirer, New York Times, and Pro Publica. We punched above our weight.
I never made a secret of what I was trying to do, but I don't think it ever really took hold in the broader organizational culture. That's a shame: turnover was high at the Seattle Times in my last couple of years there, especially after the large batch of buyouts in early 2017. I still believe that a newsroom that sees departures as an essential tool for recruiting and enriching the industry talent pool would see real returns with just a few simple adjustments.
My principles on the team were not revolutionary, but I think they were effective. Here are a few of the lessons I learned:
In retrospect, all of these practices seem common-sense to me — but based on the evidence, they're not. Or perhaps they are, but they're not prioritized: a newspaper in 2018 has tremendous inertia, and is under substantial pressure from inside and out. Transparent management can be difficult — to actively celebrate the people who leave and give away your hard work to the community is even harder. But it's the difference between being the kind of team that grinds people down, or polishes them to a shine. I hope we were the latter.
We posted Quantity of Care, our investigation into operations at Seattle's Swedish-Cherry Hill hospital, on Friday. Unfortunately, I was sick most of the weekend, so I didn't get a chance to mention it before now.
I did the development for this piece, and also helped the reporters filter down the statewide data early on in their analysis. It's a pretty typical piece design-wise, although you'll notice that it re-uses the watercolor effect from the Elwha story I did a while back. I wanted to come up with something fresh for this, but the combination with Talia's artwork was just too fitting to resist (watch for when we go from adding color to removing it).
In the end, I'm just happy to have worked on something that feels like such an important, powerful investigative story.
2016 was a busy year for interactive projects at The Seattle Times. According to our (very informal) spreadsheet, we did about 72 projects this year, about half of which were standalone. That number surprised me: at the start of the year, it felt like we were off to a slow start, but the final total isn't markedly lower than 2015, and some of those pieces were ambitious.
The big surprise of the year was Under Our Skin, a video project started by four young women in the newsroom and done almost completely under the radar. The videos themselves examine a dozen charged terms, particularly in a Seattle context, and there's a lot of smart little choices that the team made in this, such as the clever commenting prompts and the decision not to identify the respondents inline (so as not to invite pre-judgement). The editing is also fantastic. I pitched in a little on the video code and page development, and I've been working on the standalone versions for classroom use.
Perhaps the most fun projects to work on this year were with reporter Lynda Mapes, who covers environmental issues and tribal affairs for the Times. The Elwha: Roaring back to life report was a follow-up on an earlier, prize-winning look at one of the world's biggest dam removal projects, and I wrote up a brief how-to on its distinctive watercolor effects and animations. Lynda and I also teamed up to do a story on controversial emergency construction for the Wolverine fire, which involved digging through 60GB of governmental geospatial data and then figuring out how to present it to the reader in a clear, accessible fashion. I ended up re-using that approach, pairing it with SVG illustrations, for our ST3 guide.
SVG was a big emphasis for this year, actually. We re-used print assets to create a fleet of Boeing planes for our 100-year retrospective, output a network graph from Gephi to create a map of women in Seattle's art scene, and built a little hex map for a story on DEA funding for marijuana eradication. I also ended up using it to create year-end page banners that "drew" themselves, using Jake Archibald's animation technique. We also released three minimalist libraries for working with SVG: Savage Camera, Savage Image, and Savage Query. They're probably not anything special, but they work around the sharp edges of the elements with a minimal code footprint.
Finally, like much of the rest of the newsroom, our team got smaller this year. My colleague Audrey is headed to the New York Times to be a graphics editor. It's a tremendous next step, and we're very proud of her. But it will leave us trying to figure out how to do the same quality of digital work when we're down one newsroom developer. The first person to say that we just need to "do more with less" gets shipped to a non-existent foreign bureau.
On election day, voters in the Seattle metro area will need to choose whether or not to approve Sound Transit 3, a $54 billion funding measure that would add miles of light rail and rapid bus transit to the city over the next 20 years. It's a big plan, and our metro editor at the paper wanted to give people a better understanding of what they'd be voting on. So I worked with Mike Lindblom, the Times' transportation reporter, and Kelly Shea in our graphics department to create this interactive guide (source code).
The centerpiece of the guide is the system map, which picks out 12 of the projects that will be funded by ST3. As you scroll through the piece, each project is highlighted, and it zooms to fill the viewport. These kinds of scrolling graphics have become more common in journalism, in part because of the limitations of phone screens. When we can't prompt users to click something with hover state and have limited visual real estate, it's useful to take advantage of the most natural verb they'll have at their fingertips: scroll. I'm not wild about this UI trend, but nobody has come up with a better method yet, and it's relatively easy to implement.
The key to making this map work is the use of SVG (scalable vector graphics). SVG is the unloved stepchild of browser images — badly-optimized and only widely supported in the last few years — but it has two important advantages. First, it's an export option in Illustrator, which means that our graphics team (who do most of their work in Illustrator) can generate print and interactive assets at the same time. It's much easier to teach them how to correctly add the metadata I need in a familiar tool than it is to teach the artists an entirely new workflow, especially on a small staff (plus, we can use existing assets in a new way, like this Boeing retrospective that repurposed an old print spread).
Unfortunately, while these are great points in SVG's favor, there's a reason I described it in my Cascadia talk as a "box full of spiders." The APIs for interacting with parts of the SVG hierarchy are old and finicky, and they don't inherit improvements that browsers make to other parts of the page. As a way of getting around those problems, I've written a couple of libraries to make common tasks easier: Savage Query is a jQuery-esque wrapper for finding and restyling elements, and Savage Camera makes it easy to zoom and pan around the image in animated sequences. After the election, I'm planning on releasing a third Savage library, based on our <svg-map> elements, for loading these images into the page asynchronously (see my writeup on Source for details).
The camera is what really makes this map work, and it's made possible by a fun property of SVG: the viewBox attribute, which defines the visible coordinate system of a given image. For example, here's an SVG image that draws a rectangle from [10,10] to [90,90] inside of a 100x100 viewbox:
If we want to zoom in or out, we don't need to change the position of every shape in the image. Instead, we can just set the viewbox to contain a different set of coordinates. Here's that same image, but now the visible area goes from [0,0] to [500,500]:
Savage Camera was written to make it easy to manipulate the viewbox in terms of shapes, not just raw coordinates. In the case of the ST3 guide, each project description shares an ID with a group of shapes. When the description scrolls into the window, I tell the camera to focus on that group, and it handles the animation. SVG isn't very well-optimized, so on mobile this zoom is choppier than I'd like. But it's still way easier than trying to write my own rendering engine for canvas, or using a slippy map library like Leaflet (which can only zoom to pre-determined levels).
This is not the first time that I've built something on this functionality: we've also used it for our Paper Hawks and for visualizing connections between Seattle's impressive women in the arts. But this is the most ambitious use so far, and a great chance to practice working closely with an illustrator as the print graphic was revised and updated. In the future, if we can polish this workflow, I think there's a lot of potential for us to do much more interesting cross-media illustrations.
It's been a busy few weeks, but I do at least have an article up on Source with an overview of my SRCCON session on creating more humane digital journalism.
This month, I'm teaching a class at the University of Washington on reporting with Python. This seems like an odd match for me, since I hardly ever work with Python, but I wanted to do a class that was more journalism-focused (as opposed to the front-end development that I normally teach) and teaching first-time programmers how to do data analysis in Node just isn't realistic. If you're interested in following along, the repository with the class materials is located here
I'm not the Times' data reporter, so I don't get to do this kind of analysis often, but I always really enjoy it when I do. The danger when planning a class on a fun topic is that it's easy to over-stuff the curriculum in my eagerness to cover the techniques that I think are particularly interesting. To fight that impulse, I typically make a list of material I want to cover, then cut it in half, then think about cutting it in half again. As a result, there's a lot of stuff that didn't make it in — SQL and web scraping primarily among them.
What's left, however, is a pretty solid base for reporters who are interested in starting to use code to generate and explore stories. Last week, we cleaned and searched 1,000 text files for a string, and this week we'll look at doing analysis on CSV files. In the final session, I'm planning on taking a deep dive into regular expressions: so much of reporting is based around interrogating text files, and the nice thing about an education in regex is that it will travel into almost any programming language (as well as being useful for many command line tools like grep or sed).
If I can get anything across in this class, I'm hoping to leave students with an understanding of just how big digital scale can be, and how important it is to have tools for handling it. I was talking one night with one of the Girl Develop It organizers, who works for a local analytics company. Whereas millions of rows of data is a pretty big deal for me, for her it's a couple of hours on a Saturday — she's working at a whole other order of magnitude. I wouldn't even know where to start.
Right now, most record requests and data dumps operate more at my scale. A list of all animal imports/exports in the US for the last ten years is about 7 million records, for example. That's approachable with Python, although you'd be better off learning some SQL for the heavy lifting, but it's past the point where Excel is useful, and it certainly couldn't be explored by hand. If you can't code, or you don't have access to someone who does, you can't write that story.
At some point, the leaks and government records that reporters pore over may grow to a larger kind of scale (leaks, certainly; government data has will be aggregated as long as there are privacy concerns). When that happens, reporters will have to develop the kinds of skills that I don't have. We already see hints of this in the tremendous tooling and coordination required for investigating the Panama papers. But in the meantime, I think it's tremendously important that students learn how to automate data at a basic level, and I'm really excited that this class will introduce them to it.
Judging by my peers, it's possible that I'm the only journalist in America who didn't absolutely love Spotlight. I thought it was a serviceable movie, but when it comes to this year's Best Picture award I still harbor a fantasy that there's an Oscar waiting in Valhalla, shiny and chrome, for Fury Road (or for Creed, if push came to shove).
But I'm not upset to see Spotlight win, either. The movie may have been underwhelming for me, but its subject deserves all the attention it gets (whether or not, as former NYT designer Khoi Vinh wonders, the Globe fully capitalizes on it). My only real concern is that soon it'll be mostly valuable as a historical document, with the kind of deep reporting that it portrays either dying or dead.
To recap: Spotlight centers on the Boston Globe's investigation into the Catholic Church's pedophilia scandals in the 1990s — and specifically, into how the church covered up for abusive priests by moving them around or assigning them to useless "rehabilitation" sessions. The paper not only proved the fact that the church was aware of the problem, but also demonstrated that it was far more common than anyone suspected. It's one of the most important, influential works of journalism in modern memory, done by a local newsroom.
It's also a story of successful data journalism, which I feel is often rare: while my industry niche likes to talk itself up, our track record is shorter than many of us like to admit. The data in question isn't complex — the team used spreadsheets and data entry, not scripting languages or visualizations — but it represents long hours of carefully entering, cleaning, and checking data to discover priests that were shuffled out of public view after reports of abuse. Matt Carroll, the team's "data geek," writes about that experience here, including notes on what he'd do differently now.
So it's very cool to see the film getting acclaim. At the same time, it's a love letter to an increasingly small part of the news industry. Investigative teams are rare these days, and many local papers don't have them anymore. We're lucky that we still have them at the Seattle Times — it's one of the things I really like about working there.
Why do investigative teams vanish? They're expensive, for one thing: a team may spend months, or even a year working on a story. They may need legal help to pursue evidence, or legal protection once a story is published. And investigative stories are not huge traffic winners, certainly not proportional to the effort they take. They're one of the things newsrooms do on principle, and when budget gets tight, those principles often start to look more negotiable than they used to.
In this void, there are still a few national publishers pursuing investigations, both among the startups (Buzzfeed, which partnered on our mobile home stories) and the non-profits (Pro Publica and the Marshall Project). I'm a big fan of the work they're doing. Still, they're spread thin trying to cover the whole country, or a particular topic, leaving a lot of shadows at the local level that could use a little sun.
It's nice to imagine that the success of Spotlight the movie will lead to a resurgence in funding for Spotlight the investigative department, and others like them. I suspect that's wishful thinking, though. In the end, that Oscar isn't going to pay for more reporters or editors. If even Hollywood glamor can't get reporters and editors funded, can anything?