Dataviz is not a one-way street: fostering critical thinkers

Last year, I wrote two posts about the important editorial role that the designer plays in visualizing data (you can read them here and here). This week, Moritz Stefaner did a much more eloquent (and concise) job of underscoring the sensibility and the responsibility of the designer in crafting a data visualization.

But what I found particularly insightful about Mr. Stefaner’s post is his different characterization of what many of us (including me) typically describe as “telling the story” through data. He challenges that oft-used paradigm and, instead, offers a more compelling mode–the participatory, audience-driven cognitive experience that is the true power of data visualization. To me, this is what I found the most compelling, the power that data visualizations have to create a community of critical thinkers.

The story-telling model, according to Mr. Stefaner, is a one-way street that invokes a limiting, linear “start here, end here” dynamic–one that ignores the true opportunities that data visualization presents. Mr. Stefaner’s more aspirational definition has the reader exploring and creating his/her own experiences through the visualization.

In hindsight, it makes so much sense, right? It’s the interactivity of data visualization beyond sorting, filtering, reading and reporting. Rather, it’s a way to respect and foster the intellectual curiosity of the reader, thus fostering a culture of critical thinkers who go behind the more passive consumption of being “told” a story.

Mr. Stefaner tells us that this is his motivation for creating rich, immersive projects, ones that turn his audiences into “fellow travelers,” as he calls them. I absolutely love this characterization, albeit to me, it is not without some challenges. For example, there are times when I find myself lost in a data visualization that is too complex. Rather than stimulate my intellectual curiosity and propel me deeper into that visualization, I find myself frustrated that I’m being left behind by the author/designer, that I’m missing something important. This isn’t what Mr. Stefaner is suggesting, but it’s worth noting nonetheless.

This is one of the best things that I’ve read in a while, and one I’ll remember.

Mapping data on the influence of traditional and digital media for social change

The shooting death of Trayvon Martin, the 17-year-old black Florida high school student who was killed by George Zimmerman on February 26, 2012, spurred one of the most widely reported, painful and controversial public conversations on race and social justice in recent memory. The story started as a local news piece, and quickly morphed into a national debate in newspapers and radio stations; on YouTube, Twitter, Facebook, Reddit and other social media channels; on front stoops, in office cubicles, and at kitchen tables;  across marches, rallies and demonstrations; and through online petitions and campaigns. These events coalesced and influenced the actions of news organizations, citizens, politicians and thought leaders in a very public way. This offline/online “networked” public discourse was a far cry from the analog (print, radio) news model of the past.

Understanding how information and news networks relate and influence one another helps you decide where to take your message, and to thus influence and help set the agenda for public debate. This is where today’s social change organizations will succeed or fail in their efforts to remain relevant and effective agents of change.

The Battle for Trayvon Martin: Mapping a Media Controversy Online and Off-line” study analyzes, piece by piece, each facet of the intersection between the offline and online reactions, advocacy, citizen journalism and organized media coverage of the Trayvon Martin news event, and presents an analysis that takes us to the very epicenter of the intersection between media coverage, online and offline activism on both a personal and grassroots level, and the results through the lens of public discourse. This pioneering February, 2014 study was authored by Erhardt Graeff, Matt Stempeck and Ethan Zuckerman of the MIT Center for Civic Media. The goal of the study is to analyze the evolution of the Trayvon Martin story and to understand the role that activists in how the story played out across offline and online media channels .

Using data to quantify influence in public discourse

To the best of my knowledge, the authors are doing something that no one has done before for traditional and digital media (the methodology will give you a headache, in a good way)—they attempt to quantify and measure far beyond the “clicks” on articles that many of us traditionally use to measure engagement and, from that, to glean our influence over the message (I know I’m over simplifying but not by much).

Rather, they map the spread and cross-pollination of those ideas across all media (offline and online, traditional and participatory) and make correlations around consumption (who is clicking) and engagement (what they do and share afterwards), tracking it all back to the message (how does all of this effect how analog and digital news outlets cover the issue). It’s a fascinating cycle, and one that any organization interested in shaping public opinion and effecting social change would be better served to learn.

This post attempts to translate the findings of the study into takeaways that organizations who focus on social change can use to better understand the correlation between traditional, digital and social media today.

First, let’s take a look at one of the most helpful parts of the study—an analysis of the journalism ecosystem of today.

Yesterday’s traditional news gatekeepers are gone—replaced by “the networked public sphere.”

To be effective, social change organizations need to understand how to work and communicate in what the study defines as the “ecosystem” of news and information today.

I think of this in broader terms—to me it’s more of an information ecosystem. Regardless, it is not the topdown gatekeeper model from back in the day of print news—the managing editor, the reporter, and you—cultivating a personal relationship with a network of journalists to pitch your story. Don’t get me wrong, that world exists. But it has expanded by so much that if you don’t understand where else others are engaging, you’ll be talking to an empty room, albeit a virtual one.

The study underscores this by helpfully describing the new world of media as an ecosystem rather than an environment. The distinction may be lost on some of us, but the definition the authors present is clear.

“[Today's media ecosystem] is not monolithic or hierarchical—[rather] dynamic networks of media linked together by transmedia audiences [those who hop from one media and social platform to another—my take] coalescing around particular stories at particular times, [following] literal hyperlinks [to seek] the most influential source at a given moment.”

So what comprises digital media today? The study emphasizes both professional content (journalists) and amateur content (“citizen journalist” bloggers, for example). Add to this, everyone—those who write 50-word posts on Facebook that get shared, Tweeted and discussed; 140 characters on Twitter, those who post Instagram photos and opinions; the discussions on Reddit, etc.). The authors describe this as “the networked public sphere.”  And it’s a big universe with lots of moving parts. If you’re trying to control it, give up (that’s yesterday’s model). If you’re trying to be a smart influencer, read on.

The traditional gate-keeper role of the media has been upended by the democratization of information, which gives social change organizations the opportunity to seize and set the agenda of public discourse.

What’s cool about this networked public sphere model, and critical for social change organizations to understand, is that it presents unheralded opportunities for these organizations to actually set the agenda for public discourse. As noted above, the traditional gate-keeper role of the media has been upended (to a degree) by the democratization of information. If social change organizations (and more importantly, the individuals who serve as their advocates and ambassadors) choose to engage in digital media (carrying out conversations and sharing information on Twitter, Facebook; pushing cultivating relationships and content with bloggers, etc.,) their message becomes the news, and they get to frame it.

Use social media effectively and your message becomes the news—you get to frame the debate.

The study references recent media research around the revolution in Egypt (2010), and likens the Trayvon Martin story to that revolution, in terms of how it played out across all media and public dialogue. For example, the authors cite how Twitter’s #egypt hashtag reflected a blend of both personal political expression and a more conventional media push around a central message. To me, the Twitter conversation represents a hybrid of these formerly distant messaging cousins (the individual and the media outlet).

Think of it this way: Twitter users pushed out their own message about the revolution framed in a way that expressed their common sentiment, then the more “authoritative” (traditional) media outlets began reporting on that “framed” message, and that particular framing was—in turn—disseminated even further by the readers of those outlets. This is one way in which social media is influencing how even traditional news media are shaping and forming the message behind a story.

Data tools used to track and analyze coverage

To trace the path, evolution and influence of the Trayvon Martin story, the authors use Media Cloud and Controversy Mapper (which are, by the way, two tools developed by the authors in conjunction with Harvard’s Berkman Center for Internet and Society.) This is good stuff (case in point: Controversy Mapper’s data visualization on SOPA/PIPA)—imagine being able to analyze not just what the media is covering but how (the message, the interpretation, the framing, the influence) in a rigorous, quantitative way. Well, they did that.

Media Cloud collects articles from more than 27,000 mainstream media outlets and blogs, and follows and tracks links mentioned in these sources to explore the coverage even further. Archive.org’s TV News archive helped the authors analyze broadcast TV (they mine transcripts of broadcast TV). On the digital side, the authors also used Google Trends to analyze searches, General Sentiment to track Tweets and hashtags, and the url tracking and shortening tool, bit.ly.

Data that the authors examined

First the broader media coverage: social media and professional news outlets. The authors  examined the number of times the story was referenced, Tweets and hashtags, TV, Google searches for the subject, location of coverage (e.g., front page indicating editorial prioritization) in national papers, and the public’s online activism (for example, a petition on change.org). The methodology and data collection were far more involved than my crude summary attempt. Because the goal of this post is to translate the study for a more general audience, I don’t do the methodology much justice. It merits a closer read.

Trayvon Martin

Let’s shift to the Trayvon Martin story. As you know, this unfolded offline initially—it was local—hyperlocal at first, narrowly framed and, as the authors describe it, “a fight between two people in an area known for occasional violence, stood little chance of attracting significant media attention.”

An initial amount of national media coverage gets returns

Why then, didn’t the story die? The difference was the immediate and unrelenting efforts of the Martin family to share their story. By quickly retaining Benjamin Crump, a pro bono civil rights attorney (interestingly, one who, according to the study’s authors, ascribed the failings of a previous probono effort in part to an inadequate media publicity strategy) who brought on a local attorney who, in turn, recruited a pro bono publicist (Ryan Julison). Julison was able to get coverage from two national media outlets, which later snowballed into other national media.

From a fraternity listserv to a national online petition: Leveraging online activism yields big results

According to the study, the story (spurred by the initial limited national coverage) was mentioned on a Howard University listserv. A Howard law grad got involved and launched a Change.org petition. His rationale was the lack of national coverage. He emailed his petition to other students at the university. Yep, email is how this got started.

More national coverage, social organizations step in, and Change.org becomes “an early leader” in media attention

The Huffington Post, Global Grind, a self-described multi-racial news and lifestyle website and activist organizations (ColorOfChange and the Black Youth Project) began covering the story, described by the authors as “early amplifiers.” As a result, the change.org petition began picking up speed (growing from 217 initial signatures on day one—March 8—to over 30,000 signatures five days later (March 13).

Change.org attracts celebrities, and even more attention

Something interesting happened on the sixth day after the petition was launched. A change.org employee asked a target group of celebrities whom he thought would be sympathetic to the cause to share the petition with their fans (Mia Farrow, Spike Lee, to name a few). They were interested, and they did share—to the tune of over 80,000 signatures a few days later (a 900% increase in signatures over the course of 3 days, according to the authors of this study).

The shift to mainstream media as the news authority on the story

The pattern until March 17 (when the publicist released 911 tapes to the public and the media) was as follows: low-profile, hyperlocal news story; narrow coverage on a national level that spurs a rapid rise of personal and social activism; which yielded high-profile coverage by celebrities and a resulting increase in national coverage.

When the probono civil rights attorney (Benjamin Crump) released the 911 call to the media, coverage—particularly in mainstream broadcast radio and TV—predictably mushroomed. The authors of the study specifically point out that the audio nature of the 911 call may have made it more appealing for radio and TV to cover.

But social change and race-based organizations and celebrities continue the momentum

Reddit’s /r/blackculture subreddit featured the change.org petition and Reverend Al Sharpton’s involvement continued the publicity. By now, civil rights and political leaders all over the country were taking up the charge through political demonstrations and rallies. The authors cite the Million Hoodie March in New York (spearheaded by a digital strategist) as a catalyst for more coverage. Interestingly, the authors point out, larger media didn’t feature the story on their front pages until after the march, positing that the actuality of the march made for an easier story to cover.

News hooks in traditional media need “real” events

There’s an interesting pattern here of mainstream media not covering the Martin story until something “real” happens (the authors describe these as “actualities”). Note how radio and TV began covering the story after an audio recording was released, and front page newspaper coverage began after an actual march took place. After Zimmerman was finally taken into custody (another “real” event) six weeks after the shooting, newspaper coverage peaked.

And then, of course, the President’s March 23 statement (“If I had a son, he’d look like Trayvon” brought all news coverage to its peak.

Who influences how a message is framed by national media outlets?

Let me answer that simply—it’s not the media outlets. Nowadays, the spin on a story often takes place outside of national media news sources. Frequently, by the time they report on something, they’re simply capturing what has already happened.

So if you want to influence how the a major news outlet writes a story, your message can begin in social and digital media, and with your activists and ambassadors.

Let’s look at how the conservative movement was able to influence the debate. The study cites how one notable conservative blogger (Dan Linehan, of the Wagist blog) claimed that Trayvon was a drug dealer. As you would expect, this message was spread and picked up by like-minded right-leaning blogs, and eventually did make its way to mainstream media (the Miami Herald), where it was amplified. So, regardless of the accuracy of the claim (and it was not credible), right-wing bloggers became effective ambassadors to mainstream media.

The study’s authors actually cite research that shows that repeating a myth in order to deny its credibility may have the opposite effect.

“Research has shown that restating a myth in order to negate it can actually produce familiarity and thereby help further propagate the misinformation.”

This has strong implications for social change organizations of all stripes—the public debate is often played out as a series of narratives that are alternately supported and refuted by proponents and opponents, respectively.

Two graphics show the networks of media that mentioned “marijuana” (figure 8 in the study) and “drug dealer” (figure 9) during this period (notice how prominent the right-wing Wagist bubble is in both graphics.) The large size of the Miami Herald bubble signals high frequency of news mentions of the word “marijuana” in the story; as does the similarly large sized Wall Street Journal bubble (“drug dealer” in the same context).

Figure 8: Network of interlinked media mentioning ‘marijuana’

Figure 8: Network of interlinked media mentioning ‘marijuana’ as taken from the authors’ study

Figure 9: Network of interlinked media mentioning ‘drug dealer’

Figure 9: Network of interlinked media mentioning ‘drug dealer’ as taken from the authors’ study

How opponents can inadvertently strengthen your messaging goals

What’s interesting is how left-wing blogs and organizations join the fray and, by refuting the right-wing claims, nonetheless continue to keep the negative framing in the limelight, as evidenced by the largish bubbles that represent ThinkProgress.org, for example. The author’s conclusions:

“This suggests a strategy for reframing a story—if an activist is able to gain mainstream coverage for [framing a message a certain way], opponents are likely to respond, [thus] perpetuating a debate that features the desired framing [of the activist].

Remember, these two graphs reflect the prominence of Trayvon Martin and the words “drug dealer” and “marijuana,” an association that his supporters deemed undesirable. All started by a right-leaning blogger, perpetuated by those who countered the claim, and widely covered (eventually by papers ranging from the Wall Street Journal to the New York Times).

Piggybacking to a related cause: Stand Your Ground Laws under attack

The authors describe how an organization with a different focus, The Center for Media and Democracy (CMD), a left-leaning organization, injected its concerns about the influence of the American Legislative Exchange Council (ALEC), a conservative lobbying organization that became an outspoken proponent of the “stand your ground laws” that were used in Zimmerman’s defense of the Trayvon Martin shooting) into the debate. The Center for Media and Democracy had launched a campaign against ALEC prior to the shooting but used the controversy to strengthen its campaign. And like-minded progressive organizations formed a cascading effect, as they piggybacked off the Center’s research to pressure corporations to withdraw ALEC funding. Eventually, even Paul Krugman of the New York Times wrote an op-ed (March 25) about Trayvon and the Stand Your Ground Laws, and change.org followed suit with many petitions to dismantle these laws. According to the authors of the media mapping study, on April 17 ALEC terminated its controversial task force on those laws.

Correlation between digital and traditional media coverage and reader engagement: All news sources tend to cover issues even after reader interest wanes

The study’s findings show that all media sources, (traditional and digital) are roughly correlated (when one was covering the story, so were the others)—this extends to news articles, TV coverage, searches, petition signatures, and clicks to links (via bit.ly) on this coverage. The conversation on Twitter appeared to peter out after a while, and the authors speculate that this was either because campaigns had used Twitter early on or simply because social media may be quicker than other mediums to move away from one story to the next. Overall, however, the “tail” of news coverage went beyond actual reader engagement (sharing, clicking on links to articles, etc.) which the authors believe may indicate that readers simply lost interest even whilst the media continued coverage.

Conclusions:

1. Broadcast media still matters.

Broadcast media amplifies (spreads the story through coverage) and serves as a gatekeeper (what it chooses not to cover has a harder time getting out into the public debate, and how it frames what it does cover tends to stick). But activists who use other media channels and platforms (petitions, social media, blogging, leveraging like-minded organization and allies, personal networks) are now solidly influencing how the message is shaped and formed (framing).

2. Social change organizations can spin traditional media for their own purposes.

Even though broadcast media still serve as strong gatekeepers to what does/doesn’t get covered and how it is framed, smart organizations leverage existing coverage to inform their supporters, piggyback off the coverage to mobilize their allies, and spin it (reframing) to meet their own messaging goals. And, from a messaging perspective, it’s promising, as evidenced by how successfully many Trayvon Martin proponents were able to shift the media narrative (the outcome of the trial is another matter).

3. The blogosphere covers issues long after broadcast media coverage peaks.

Smart organizations know this, and court bloggers accordingly, understanding what motivates them to write and when. So understanding who is blogging (or has the potential to blog) about your issues and cultivating those relationships is key.

4. Contemporary news outlets today are increasingly more likely to get the maximum out their investment of time and journalists to cover a story.

News outlets will cover a story even after readers have disengaged. Don’t get too excited. This has not been covered in a flattering light (see McJournalism).

5. Social media can create related micro-stories from broader events.

These micro-stories then become news events in themselves and create a longer tail for the original story (the Million Hoodie March, for example).

6. Social media can side-step traditional media gatekeeping functions if you have good content.

Some social media platforms that are particularly well-suited to a specific type of content (YouTube or Facebook for video-sharing, for example) quite powerfully and effectively side-step traditional media’s gatekeeper role, and thus are demonstrably able to shape public opinion. Organizations that know how to create relevant content for these and other  platforms can get their message across in huge ways.

7. Social media is so much more than spreading the word.

Because it is so heavily reliant on personal interpretation (one person sharing his or her opinion about a news event, in addition to simply sharing news of the event itself), social media is a powerful force in shaping the message and framing—and the public perception—about an event.

8. Deviant discourse: Social media upends the traditional notion that mainstream media are, indeed, the gatekeepers for news content and opinion

This has its downfalls. In the past, gatekeeper news organizations simply wouldn’t cover extreme views that were a small minority of public debate. Today, if enough people talk about it, it does indeed become mainstream news (the authors point to the widespread coverage of Obama’s citizenship as a case in point). The authors explain this “deviant discourse,” as they put it, brilliantly, and it’s worth quoting here:

“Our work suggests a mechanism through which social media users introduce potentially deviant frames into the mainstream: they harness ideas to a high attention story already underway and attempt to direct the attention generated by the story towards their interpretations and views.”

9. Use finding #8 (above) for good, and not for evil, okay?

(My opinion; not the authors’.)

Hope you enjoyed this post. Mad props to the geniuses at the MIT Center for Civic Media for this incredibly data-rich study. Mindblowing stuff.

Big data, small budget, good mission: Using data and other cool stuff for social change

These days, you can throw a rock and reliably hit any number of articles and headlines proclaiming the power of big data, open data, and transparency. The acceptance and adoption of using large, public sets of information to make informed decisions represents a sea change in how the corporate world, inacademia, think tanks and large NGOs are investing in their capacity to crunch more than numbers. No surprise there. But how does the little guy—the small grassroots organization with a small budget and a big mission around social change—fare?

I’ve been thinking a lot about this lately. Last year, I made a job shift. I moved from a very large, well-funded nonprofit to a relatively small healthcare advocacy organization. In my old job, I worked in data visualization and regularly called upon the considerable financial, technological and statistical resources that my employer afforded me. Today is a different story. I work with supremely talented and passionate people, but the data resources that I once took for granted are gone. The “data divide” is now staring me in the face. And that’s the reason for this post—the reality that, for all the promise that big data and technology claims to offer, many of today’s smaller nonprofits and grassroots organizations are not equipped to collect, understand and harness information to move their social mission. We are the “have nots” who look out onto the world of the “haves” with statistical modeling tools, economists or statisticians on hand, coders on staff or on contract.

The data divide—what is it?

The “data divide” is by now a familiar term to many of us. The Guardian wrote about “data apartheid” when it reported on the findings from the recent 2013 Open Data Index last November. Similar findings are in the Open Data Barometer 2013 report released late last year too. And we know how it exacerbates problems faced by developing countries in fostering an open, transparent government and an informed, participatory citizenry. As I wrote last year, a good example of how open data helps citizens overcome these hurdles lies in how La Nacion (Argentina’s national newspaper) teamed up with data journalists to publish data on a variety of indicators to the Argentine public—despite the government’s lack of a Freedom of Information law.

Data divide: Access to data does not translate into results

In a blog post dating back to 2011, Mike Gurnstein describes the data divide in a way that many health care advocates are talking about healthcare today. In discussing the Affordable Care Act, advocates regularly say that access to health care is not enough—it’s the quality of care that matters. And there is an entire movement around health system reform that underscores this. Gurnstein makes a similar point about data: “[A]ccess is not enough,” he writes. “[I]t is whether opportunities and pre-conditions are in place for the effective use of the technology particularly for those at the grassroots.” Go Mike. I haven’t a clue who you are, but you nailed it.

In the same way in which the “digital divide” of the 90s and 00s required education and digital literacy to make real the opportunities that online access offered, bridging the data divide for small organizations relies on more than making data available, but also in affording these groups the ability to use it effectively through knowledge (data literacy—an understanding of how to read data and how to represent/visualize it effectively for a common purpose) and resources (the realization of this understanding into actual tools).

How can data help grassroots organizations and smaller nonprofits?

Here in D.C., Applied Predictive Technology (APT), a tech firm that sells predictive analytics software, volunteered to analyze the data that a local charter school was collecting from the tablet apps that its students were using. APT used this “data dive” to help teachers assess how well the tablet reading apps were working for different kids—allowing teachers to tweak the reading curriculum and apply intervention to different types of students.

One of the best organizations out there is New York-based DataKind. If you really want to understand how socially-conscious data scientists are working to achieve social change through data, take five minutes to check out the variety of projects they work on. Over the past several years, DataKind has been launching “data drives” in cities around the U.S. Similar in nature to “hackathons” or “code-a-thons,” these DataDives team up volunteer data scientists/analysts and social organizations over the course of a few days to build apps or software that solve a well-defined data problem. And then they solve it.

When DataKind held a DataDive for D.C. Action for Children, a small organization that collects data on the indicators that affect the well-being of children to mitigate poverty, good things started to happen. The nonprofit also runs the DC chapter of the Kids Count program and, through Kids Count, it was doing a great job at collecting data (that was their mission). But the work that they were producing was static—PDFs—a situation common to many small organizations. Fortunately, they realized that, to make the data meaningful, easier to analyze, and more effective at highlighting the poverty problems that needed to be solved, they needed to visualize it. This is where DataKind came in. Their volunteers worked for a month to create an interactive data visualization tool (eDatabook) that mapped the well-being indicators and poverty clusters across the District. The best part? It’s replicable. Other DC Count programs across the country can adopt it as well.

Using data and hackathons to help on a local level

The vast global data modeling regularly published by the World Bank is impressive. But municipalities are using data to tackle local problems too. Like D.C. Action for Children, cities are pairing up with volunteer data analysts and coders to sidestep the issues of inhouse capacity and expertise.

To fight ongoing problems with obesity and diabetes, for example, New York City launched its first health data code-a-thon this past December. The result? An app called “Vera.” Based on a user’s risk for diabetes, Vera texts users reminders and tips for physical activity, glucose monitoring and even good food intake.

Leveraging hackathons for broader impact

Voting: On a broader scale, the Voting Information Project, a small group of elections experts who focus on improving the voting experience for the public through cutting-edge technology, held its first hackathon in November, 2013 (disclaimer, I was affiliated with the organization that funds this project). The hackathon yielded fast and effective results, including first-ever voter lookup tools, that were used by Americans everywhere.

Healthcare: On June 2, 2014 Health Code-a-palooza will bring together programmer teams who, over the course of 48 hours, compete to see who can use a Medicare data set to build the best app for doctors to use to improve the quality of care that they deliver to patients. This hackathon is part of the Health Data Consortium’s annual Health Datapalozza, an event that features data and healthcare experts discussing how open data can drive meaningful improvements in the health reform movement. But you have to admit, the coding is pretty cool too. If you’re interested in learning more about how open data is playing out in the field of healthcare, read more about the Health Data Consortium.

Challenges and questions around transforming the data culture in small nonprofits

Lack of data literacy can impede an organization’s ability to articulate its need.
As I mentioned, part of the problem is not just access to data, but being able to frame a goal, understand which data to collect and establish good collection practices—data literacy. For an organization taking nascent steps toward data collection, this can be daunting. It requires a change in the organization’s culture, investment of time (if not technology and staff) and a reprioritization of traditional methods of executing its goals. Much of that work is internal. But some of that can be helped by organizations such as DataKind’s, who actually mentors organizations to help them frame their problem and prepare for the end result.

Sustainability beyond the initial volunteer effort

And what happens after the project concludes? What if something breaks? How do you continue to foster an environment of learning and change in an organization after it takes its first steps toward a data culture? Again, an approach like DataKind’s is promising. They stick around, monitor the project and provide follow-up support to ensure that the work keeps going. That makes sense, because it’s part of DataKind’s mission. In future posts, this is something that I’ll be writing more about, as well as how data volunteers and organizations are finding each other. If you’ve got ideas or stories to share, let me know. You can follow me on Twitter at @uriona.

Case study: creating a 50-state data visualization on elections administration

Ever wonder how well states are running their elections systems? Want to know which state rejects the highest number of absentee ballots? Or which state has the lowest voting time? And which state has the highest rate of disability- or illness-related voting problems?

A new interactive elections tool by The Pew Charitable Trusts (the Elections Performance Index) sheds some light on many of the issues that affect how well states administer the process of ensuring that their citizens have the ability to vote and to have those votes counted. Measuring these and other indicators (17 in all, count ‘em), Pew’s elections geeks (I was a part of the team) partnered with Pitch Interactive to develop a first-of-its-kind-tool to see how states fare. Today’s post is a quick take on how the project was created from a data visualization perspective.

Pew Election Performance Index interactive

Pew’s latest elections interactive: The Elections Performance Index

Lots of data here, folks. 50 states (and the District), two elections (2008 presidential and 2010 mid-term) and 17 ways to measure performance. Add to that the ability to allow viewers to make their own judgments–there is an overall score, for sure–but the beauty of this tool is that it allows users to slice and dice the data along some or all indicators, years and states to create custom views and rankings of the data.

You might already know about Pitch Interactive. They’re the developers who created the remarkably cool and techy interactive that tracks government-sponsored weapons/ammunition transactions for Google’s Chrome workshop (view this in Chrome) as well as static graphics like Popular Science’s Evolution of Innovation and Wired’s 24 hours of 311 calls in New York.

The data will dictate your approach to a good visualization

When we sat down with Pitch to kick around ideas for the elections interactive, we were initially inspired by Moritz Stefaner’s very elegant Your Better Life visualization, a tool that measures 11 indicators of quality of life in the 30-plus member countries of the Organization for Economic Cooperation and Development (OECD). Take a look–it’s a beautiful representation of data.

And though, initially, we thought that our interactive might go in the same direction, a deeper dive into the data proved otherwise. Comparing 30 countries along 11 indicators is very different than comparing 50 states plus DC, 17 indicators and 2 election cycles. Add to that the moving target of creating an algorithm to calculate indicators for different user-selected combinations, and you’ve got yourself a project.

After our interactive was live, I talked to Wesley Grubbs (founder and creative director at Pitch) about the project. I was interested in hearing about the hurdles that the data and design presented and how his creativity was challenged when working with the elections data. One of the first things that he recalled was the sheer quantity of data, and the complications of measuring indicators along very different election cycles. If this sounds too wonky, bear with me. Remember, one of the cool things about this interactive is that it allows you to see voter patterns (e.g., voter turnout) along two very different types of elections–mid-term elections (when many states elect their governors, their members of Congress and, in many cases, municipal elections) and the higher-profile presidential elections. Pitting these two against one another is a bit like comparing the proverbial apples and oranges. Voting patterns are dramatically different. (The highest rate of voter turnout in 2008–a presidential election–was 78.1 % in Minnesota. Compare that to the highest rate in the 2010 midterm election–56% for Maine, and you’ll see what I mean.)

Your audiences will influence your design

Another challenge early on was the tension between artistry and function. In an ideal world, the most beautiful thing is the most clear thing (an earlier post, “Should graphics be easy to understand?“, delves into this further). I remember reviewing the awesomeness behind Wes and his team’s early representations of the data. From my perspective as a designer, these were breathtakingly visual concepts that, to those who hung in there, served up beauty as well as clarity. But from a more pragmatic perspective, an analysis of our audience (policymakers and key influencers as well as members of the media and state election administration officials) revealed that the comfort-level with more abstract forms of visualizations was bound to be a mixed bag. Above all else, we needed to be clear and straightforward, getting to the data as quickly as possible.

Wes decided to do just that. “It’s funny,” he said. “We don’t often use bar graphs in our work. But in this case we asked, what’s the most basic way to do rankings? And we realized, it’s simple. You put things on top of one another. So what’s more basic than a bar chart?”

“We had to build trust–you can’t show sparkle balls flying across the screen to impress [your users]–you have to impress them with the data.”–Wesley Grubbs, Pitch Interactive

When I asked Wes how, at the time, he had felt about possibly letting go of some of the crazy creativity that led him to create the Google weapons/ammunitions graphic, he simply responded, “Well, yes, we do lots of cutting edge, wild and crazy stuff. In this case, however, a good developer is going to go where the data leads them. In addition, the audiences for this tool are journalists, academics, media–the range of tech-saavyness is very broad. We had to build trust–you can’t show sparkle balls flying across the screen to impress them–you have to impress them with the data.”

Turn your challenges into an asset

When we brought up the oft-cited concern around vertical space (“How long do you expect people to scroll for 50 states, Wes?”, I remember asking) his approach was straightforward: “Let’s blow up the bar chart and make it an intentional use of vertical space. Let’s make the user scroll–build that into the design instead of trying to cram everything above the fold.”

I think it worked. This is a terrific example of visualization experts who, responsibly, put the data and the end users above all else. “We could have wound up with a beautiful visualization that only some of our audiences understood,” says Wes. “We opted to design something accessible to everyone.”

How did Pitch build the Elections Performance Index tool?

Primarily using D3, a javascript library that many developers are now using for visualizations. It was not without its drawbacks, however. When I asked Wes about lessons learned, the first thing that he mentioned was the importance of understanding the impact of end-user technology on different programming languages. “D3 isn’t for everyone,” he notes. “Take a look at your users. What browsers are they using? The older stuff simply won’t work with many of the best tools of today. You have to scale back expectations at the beginning. The hardest part can be convincing organizations that the cutting-edge stuff requires modern technology and their users may not be in line with that. It’s all about the end user.”

Well, as an end user and a participant in the process I’m pleased. I hope you’ll agree to take the tool for a spin.

The “art” of compromise: Is there room for compromise in designing data graphics?

In my last post, I discussed how expectations and perceptions of designers are as important to quality data visualizations as are more conventional resources, such as time, people and money. But there is also a flip side to this–there are times when, as designers, we may be faced with a choice to compromise on how we present data. The compromises we agree to–or reject–are as important to our field as anything else. (Kudos to me for resisting the urge to title this “drawing the line in infographics.”)

A friend related to me a recent conversation in which an art director who, when presented with a bar graph of extreme values (very high and very low), asked the designer to “fudge” the size of the smaller bars. (They were visible–not hairline–but too small to comfortably fit the values inside of them. Presumably the art director wanted to nudge them up so that the numbers would fit inside of the bars.) My initial reaction was er… not favorable. I felt like a mother bear protecting her cubs (the cubs, in this tortured analogy, are the data). I may have uttered a few choice words, even.

The ethics of compromise.

But, once I calmed myself down, it occurred to me that this might be something interesting to write about. I polled a few designer and non-designer friends. What do you think, I asked. Was this a bow to art or clarity? Was it an unintentional breach of ethics or a well-intentioned attempt to make information easier to understand? Was it goal-driven or just lack of creativity? Don’t jump on the art director just yet. This isn’t about the choice that person made (that’s the subject of another post). It simply reflects the reality that, as in other professions, we’ll all be asked to make choices that, to others, may appear to be inconsequential. We need to make sure we handle these choices intentionally and carefully.

Here’s what came to mind after my conversations with other designers.

Book-binding: an invisible art

Let’s think about the book-binding trade of back in the day. The men (mostly men, anyway) who bound books hundreds of years ago were tradesmen. They had a craft which they revered. They apprenticed and, as journeymen, they traveled from place to place, learning and honing their craft to become–eventually–book-binders. This is not unlike the path that many information designers take today.

For all the painstaking zeal and meticulousness put into the binding of the book, the end result was rarely if ever examined once produced. If the thing didn’t fall apart in your hands, you were satisfied as a consumer.

I won’t bore you with the mechanics, but suffice to say that binding a book involved a lot of work, much of which was invisible to the eventual and subsequent owners. Once purchased, the book was read, perhaps the craftsmanship briefly admired, and then it was shelved or passed on, sometimes for generations (think of the family Bible). And yet, for all the painstaking zeal and meticulousness put into the binding of the book, the end result was rarely if ever examined once produced. Again, not unlike the process of visualizing data, much of the effort and care involved in sewing pages into folios, hand-stitching the spine–remained largely unseen. If the thing didn’t fall apart in your hands you were satisfied. End of story. And yet, despite this invisibility, these bookbinders pursued their craft with diligence and and care nonetheless. How well or how poorly they plied their trade was not immediately evident, as these old books often outlived their makers. They had no immediate incentive to be unduly diligent. And yet, I like to think that most of them did not cut corners. Why? I’d say it was self-respect and public recognition of the importance of their craft. Maybe I’m over romanticizing books (I do collect them).

Our craft: Are we short-order cooks or visual content experts?

My point? This is an issue of the ethics of our craft. As designers, we need to ask ourselves: are we short-order cooks or visual content experts? Are we hacks or tradesmen/women? Is data visualization a craft or only a paycheck? Is data an obstacle to be overcome or a living boundary that, with each challenge, offers us the opportunity to learn, do better, and to empower our readers by bringing information to the surface in a manner that brings with it a new understanding? And while, from the perspective of the client (or, in this case, the art director) it may not always be apparent that the accommodations they ask us to make are not wise, it is–nonetheless–our responsibility to do the right thing, and bring others along. In this way, we advance the field forward and our professionalism as well.

And that’s the crux of this post.

Whatever your intentions, what is the effect of the small compromises that you make in being precise, transparent and correct in how you present data?

The more seasoned amongst you may shake your heads and think that these things are self-evident. But to those of you who are just starting out (be it as younger designers or managers in charge of new data viz projects), this may not be something you’ve thought much about. It may not even seem like much of a big deal to you.

Making those small compromises weighs on you, wears you down and–worse–makes the next compromise all the greater in scope and easier to bear.

What is the effect of compromise on the designer and the team?

So, what happens when a designer makes those compromises? When I asked a few designers, they all had one response in common: morale and self-esteem. Here’s the thing: making that one small edit will be invisible to everyone but you. It’s not like your readers will ask you to send them your Illustrator file so that they can measure pixels before they read further. Like the bookbinder who sewed thread onto page folios, no one but you will see the guts of your files. But making those small compromises weighs on you, wears you down and–worse–makes the next compromise all the greater in scope and easier to bear. And these things add up to the slow devolution of what was once a craftsman/woman (if I may be allowed to use such an archaic term) to a hack.

And what happens when an art director suggests those compromises? Well, you risk losing the respect of seasoned members of your team, that’s obvious. Worse, you risk creating an environment that is progressively sloppy. And while no one will catch the small compromises, they sure as hell will catch the big ones. Remember the infamous Fox piechart?

Other examples of altering data

It doesn’t stop with information designers, as I’m sure you know. Another designer who Photoshops medical imagery (for example, a CT scan or slides of cancer cells) told me about a doctor who, when preparing images of slides for a research publication, asked the designer to darken some areas to make them more visible (thus allowing him to better make his case). The designer balked–these aren’t just pictures, he told the doctor–they’re data.

And if you want a more mainstream example, how about the furor over the Time cover of OJ Simpson in 1994? Or, more recently (2008), the Hillary Clinton ad which featured then Presidential candidate Obama with arguably darker skin?

What is unacceptable compromise to one might be reasonable accommodation to another.

There may not be room to make the wrong compromises, but there is always opportunity for discussion.

No one is perfect. And each of the examples that I gave leave plenty of room for discussion. As a newspaper friend recently noted, some photographers are adamant about not retouching any photos they take–including not cutting subjects out of backgrounds. Others are not as rigid. And not all of the participants in my informal poll reacted with extreme horror at the thought of slightly lengthening bars. Some merely grimaced. But all agreed that if you’re going to tread on thin ice, you’d best aware of it. Another friend points out that he noticed a disconnect between his former employer (a newspaper) and his current one (a corporation). He’s doing the same work–designing information graphics. But whilst former journalist colleagues (having their own code of ethics) would never have asked him to fudge the appearance of data, he feels that–in his current role as a designer in the corporate world–his colleagues have a lesser understanding and appreciation of what asking this might mean.

This isn’t necessarily a bad thing–handled correctly, it can present an opportunity for education. But you have to be willing to put yourself out there–a place that not everyone (perhaps less experienced designers or as employees with less seniority) is comfortable occupying.

As designers, let us be keenly aware of how the small choices we make for ourselves can add up to large consequences for our profession. I’d love to hear more from you on this. Have you been place in similar situations? How did you handle them?

Infographics: Does time equal quality?

Does time equal quality in good infographics? Nope, not necessarily. I’ve been giving this a lot of thought lately and, in reading recent posts by Seth Godin and Alberto Cairo, it’s interesting to see how each touches upon what I see as the pressures and attitudes that affect how well we design good information graphics.

In Mr. Godin’s case, he mentions what he calls “the attention paradox.” While he’s not specifically writing about design, his comments nonetheless aptly relate to the work designers do. As more marketers crave attention, the more they’re willing to part with content that is good at reaching an audience, and terrible at retaining it. Makes sense, right? In a time in which we’re increasingly consumed with tracking metrics and measuring success by the numbers it is par for the course to get caught up in the rat race for the next big thing (big being determined by 30-second relevance and traffic for that day). Surprisingly, information graphics are no exception. And why should they be?

I recently mentioned that, because we’re all under pressure to create more and more content, “repurposing” content is seen as a good way to take advantage of the sweat equity put into other pieces (web articles, reports, data collection) and to convert that into an infographic. This pressure to produce can have real drawbacks–clients mistakenly assume that information can be quickly “designed” just because in their estimation, the facts and the message have already been proscribed. Here–quality can suffer from lack of time. But the point that I was really getting at in my post, which I unfortunately failed to articulate clearly–was the designer’s role.

When designers are treated as service desks and not content experts (“Here are the facts, here is the message, now please make this pretty. Call me when you’re done.”), you simply don’t get the best work.

Fortunately, Alberto Cairo, in “Empower your infographics, visualization, and data teams” gets to the point. According to Mr. Cairo (and I agree) the real problem is the limited perception of the designer’s role. He mentions how, in news rooms, graphic designers are often seen as “service desks.” This isn’t limited to news rooms. In my own life, I occasionally get requests to design graphics “you know, like the New York Times” (yes, I really do). As Mr. Cairo points out, we all laud the New York Times and other large media outlets (one of my personal favorites is New Scientist) for their high-quality information graphics–pieces that can take months to make with large teams of content producers and designers in place. I agree with Mr. Cairo’s perspective that this fact might lead you to erroneously conclude that time and staffing (more people, more time) equals great work (bluntly, he says, “You can’t.”).

The solution lies, in part, in treating and using your designers as partners who help to shape content effectively.

So, what does this mean, exactly? Bring your designer into the room when you’re having editorial discussions about how to create content, before you’ve decided on what shape that content will take. Listen to your designers and expect them to offer up ideas about how to turn that into information design (be it static, motion or interactive).

Designers should read the content.

Expect your designer to read, read, read and understand. I ask my designers to read research reports before they create infographics or data visualizations. This may be a “duh” moment to some of you, but you’d be surprised how many people (including designers) don’t think of this or, worse, don’t see this as part of the designer’s role. How do you design what you don’t understand? How do you filter out the best parts of information and data without having reviewed the source?

And don’t micromanage the design. Leave them alone to create and use their expertise. Trust them, as content partners, to visualize not just the data, not just the facts, but the voice that carries the design.

I’m sure there’s more and would love to hear from you about what other recommendations you have.

Should graphics be easy to understand?

Ah, the glamorous life of the data visualization designer… to draw or not to draw? To obfuscate or not to obfuscate? I’ve been doing some reading lately about a debate that is making its way amongst the data viz community. At what point does too much illustration, creativity or innovation get in the way of the primary purpose of data visualization? And how well is the design community being transparent about art based on data versus data visualization? Or, to put it more simply, should data visualization be easy to understand and what happens when it’s not?

Allow me, first, to offer up my own definition, artfully cadged from people much smarter than I and enhanced by my own experience in the field, such as it is. So, data visualization is what, exactly?

Information served up visually in order to inform and improve/enhance our understanding of the data.

Clumsy, but I’m hitting the main points: inform and understanding. If pressed, I would add the word “easily.” Actually, it’s the word “easily” that prompted me to write this.

If you can’t understand a data visualization piece, then it’s pretty useless, isn’t it? Maybe it’s beautiful, but if you walk away more confused than you began, it’s useless. And if you walk away as confused, or a bit less confused, it’s still useless.

How far can we take this concept? Here is a quick survey of what folks have been saying lately. Props to infosthetics for providing a good starting point for these discussions. And here they are:

Stephen Few’s blog post on the two types of data viz is a good start. According to Few (Tufte’s alter-ego), there are two approaches to presenting data graphically—data visualization and data art. As he puts it, “rarely do the twain meet.” Therein lies the problem. They do meet. All the time. Though Few makes a good point—failing to distinguish between them creates confusion and harm, I would argue that the two are not mutually exclusive.

Few defines data visualizations as products created to inform, and “data art” as visualizations of data created to entertain—“art based on data”—something which can be judged accordingly.

My response? Would that the public were quite as discerning as he. The train has left the station and what we have before us is—at worst—a proliferation of eager designers too quick on the draw to consider the very important questions that need to be asked about the data that are being depicted. At best, a cadre of informed (and willing to learn) designers who humbly allow the information, the audience and the goals of the visualization to drive the design—who are loathe to add one extra pixel that doesn’t belong, and willing to take away any element that obscures a better understanding of the data. I’d like to think that I fall into the latter category but I fall somewhere in the middle, as do most designers.

Rather than drawing a bright line between these two approaches and dogmatically refusing to accept a middle ground, I suggest we embrace a blend of these when they are produced well—when they inform and present a clearer understanding of the data and are at the same time aesthetically pleasing. As a designer who chooses to serve both masters—art and data, I find joy in being able to translate a jumble of Excel rows and columns into a plain bar chart—sometimes the beauty lies in the hard work of sifting through the data and simplifying complexity. And sometimes the joy comes from experimenting with different formats and adding visual accents to enhance the data—provided, of course, that the user’s ability to understand the data is not impeded, but enhanced.

Nevertheless, I agree with Few’s depiction of the pitfalls of “data art” being misperceived as data visualization, and I’ll add one myself. In addition to spreading poor practice instead of best practice, it creates unrealistic expectations about what is acceptable in a data visualization, particularly for those of us who are working in the industry in a supportive capacity to researchers and writers with an uneven understanding of best practices (how many of us have been asked to create 3D graphics or exploding pie-charts on a whim?).

And a rising tide floats all boats. In this case, I’ll agree with Few’s point that the proliferation of “data art” and other fancy-schmanzy graphics that pass for data visualization imply that data viz is a closely-guarded secret known only to denizens of the data underworld (paraphrasing liberally from Mr. Few, here). But I take issue with his assertion that this prevents the “democratization of data”—implying that the public is somehow being dissuaded from engaging and creating data. For better or for worse, they aren’t. Just google “infographics.”

As an interesting aside, note that Eagereyes’ Robert Kosara wrote a primer on the two types of data visualization that Few discusses, waaaay back in 2007. Like Few, Kosara was also bothered by the blurred line between data and art. What Few calls “data art” Kosara called “artistic visualization.” Nonetheless, they each underscore the same points—keep data and art separate in order to be as transparent and clear about the data as possible. I agree with the goal.

As Kosara puts it, “looking at one type of visualization expecting the other will lead to disappointment and misunderstandings.”

Kosara, uses what is, in my opinion, one of the best data viz sites out there (infosthetics) as an example of sites that don’t make those distinctions, thus creating confusion. Granted, this was back in 2007. I wonder what he’d say now? Nonetheless, I disagree. Let’s not confuse lack of best practice (for example, normalizing your data to prove a point, and not being transparent about it) with the so-called sin of creating a piece that is visually striking. A designer can produce a graph with no artistic aspirations whatsoever that nonetheless obscures the data. And a designer can produce a terrific visual that observes best practices (to inform) and serves up the data artistically and well.

Adam Crymble has a different moniker for Few’s “data art” and Kosara’s “artistic visualizations.” He calls these graphics “shock and awe.” I love that term. Of all the discussions that I have read, Adam’s make the most sense to me. He doesn’t touch on all data viz that is artistic, but rather focuses on the extreme—and in this I strongly agree with the points he makes.

Adam Crymble: “shock and awe” graphics

We’ve all seen these very beautiful, complex visualizations that belong inside of a picture frame or a screensaver. Or, for a few seconds, they give us pause and food for thought.

a complex visualization of World Economic Forum data

I’ve seen them, written about them and admire them for what they are—unique explorations of the complexity of data. An artistic or visual expression of the complexity of the information we spew out and take in. But they don’t inform in the traditional sense of the definitions of data viz. They may underscore a pattern, convey a sense of weight through sheer numbers or complexity (as the example above does), but that’s about it. They’re pretty much impossible to understand on a granular level without some work.

Adam’s assertion that these complex visualizations have no place in the academic world is beyond my ken. For the record, the example above is mine, not his (see his post for his own, more humorous example). But if he is correct that peer reviewers are afraid to betray their lack of understanding of these graphics, and thus—through tacit acceptance—are endorsing their validity, well then that should concern all of us.

The most interesting point to be gleaned from Adam’s perspective, I think, is the bullying nature of shoving a terabyte of data in front of someone’s face and saying “Aren’t I clever? Don’t you get it?” I don’t. Point well-taken, Adam.

Mark Ravina writes an interesting rebuttal to Adam’s criticism of “shock and awe” graphics. He compares these artistic and complex visualizations to early feminist scholarship that provoked anger when it challenged the systemic sexism of the ivory tower. I’m a huge fan of confrontation and anger-provoking methods to push movements forward. In the early 90s, ACT-UP did the same thing for GLBT rights, if you’ll recall. Without ACT-UP, Queer Nation and Lesbian Avengers, there would be no fancy Human Rights Campaign fundraising dinners today. I get it.

But Ravina’s assertion that these complex visualizations of data somehow push the field forward is a bit much for me. He calls them “intellectual challenges.” I’m not so sure about that. How many of us who are willing to spend more than a few seconds trying to piece together a gazillion threads and data points in a fancy graphic. I think we consider it more of a waste of time to do anything other than admire the concept, the novelty of the presentation and then move on. Intellect doesn’t play a big role here (the creator, on the other hand, gets some bragging rights for creativity). Does it stick? Does it move the field forward? Um, maybe, sometimes?

Ravina spends a fair amount of time discussing how humanities researchers (he knows them better than I, certainly), insist on tables when they ask for data. I didn’t really read that into Adam’s criticism of these graphics—he was merely pointing out that data viz designers were making information too complex—he never claimed that the solution was to create charts. Then Ravina cites the misuse of pie charts to make the point that just because something is familiar, it can be misused. Is he implying that unfamiliar things can’t? As he puts it, “is schlock worse than shock?” Aside from the clever turn of phrase, it’s a bit of a moot point. Nothing that I have read criticizes innovation—merely obfuscation.

Mark Ravina: “Is schlock worse than shock?”

Ravina makes good points. He surveyed (presumably informally) graphs produced in history journals and notes that the bulk of them rely on formats developed (according to him) 200 years ago—pie charts, line charts and bar graphs. And he mentions how slow the field (I’m unclear if he means academics or history journals in particular) has been to adopt and thus understand formats that even today’s eighth graders are learning (box plots, for example). That’s a valid argument, certainly, but it has little to do with the complex visualizations that Adam was addressing or, for that matter, that Kosara and Few discuss. (To be fair, Ravina’s post was mostly in response to Adam’s).

However, he conflates different types of complexity, predictably citing Tufte and Menard (some of you know how I feel about that) as well as Rosling. Perhaps it’s a matter of taste, but I feel that Rosling bends over backwards to make his visualizations inspiring and accessible (not necessarily complex and beautiful), whereas the Menard graphic, while certainly elegant and ground-breaking, does not (of course not, and how could it, given when it was produced).

Lastly, one of the most important concerns that Adam raised was around obscuring data. By introducing unnecessary complexity into a visualization or graphic, data visualization designers can make academic and peer review verification and transparency needlessly difficult. Ravina counters this by saying that liars will lie. I don’t think that’s the point. They will lie, but transparency is as much about spotting errors or raising valid concerns as it is about unmasking willful deceit. Hats off to Ravina for taking the time to provide some very thoughtful counterpoints to the discussion.

Excelcharts is a pretty good resource for charting and data viz in general, despite the Excelcharts.com name (*smiling*). Jorge Camoes nicely (and literally) draws the elusive line between art/entertainment and data/information.

Excelcharts.com Data Visualization Continuum

More importantly, he puts a restraining hand on eager designers, quite reasonably underscoring Few’s point to make sure that, as designers, we emphasize that charts and graphics are readable and easy to understand, not memorable or beautiful. Of course, I’ll see your readability and raise you ten, Jorge. Let’s make the data understandable and, if we can, beautiful as well.

Lastly, there is this. It is a tome. You could spend hours here. It’s an open-review paper, part of which is around data viz, part of which I have skimmed. It deserves careful reading, and I’m eager to do so and write a follow-up post.

Well, if you’ve hung in there with me, I hope you have learned something. I know I have.

The joys and sorrows of concentric circle graphics

There are not many good examples of concentric circle graphics out there. La Nacion produced one last year about subway strikes, and The Guardian produced an interactive graphic on gay rights in the U.S. Both of these intrigued me because, in my day job, I produce endless variations of graphics dealing with 50-state data. And most of the time, when we look at 50-state data, we draw… you guessed it: maps. Or bar graphs showing quantity or line graphs showing changes and trends over time but no matter what we do, it involves data for the 50 states, most often over time. 50 states multiplied by several years is a lot of lines to draw, bars to fill and state maps to create. So I’ve been thinking about ways to tell the story in different formats–going beyond the map, so to speak. Last Wednesday, we created this concentric circle interactive. Here’s how we did it, and the process we took to decide on the format.

Stateline PCS jobs screenshot

One of the most onerous dimensions to 50-state data is the sheer physical size and length of the data. Our website used to allow for a content well of 500 pixels. Try shoving 50 state labels across 500 pixels and you’ll quickly see why it’s a challenge.

But even with all the real estate in the world, long, horizontal displays are also taxing on the user if there is a comparative aspect to the data. There is simply too much bouncing back and forth from the left to the right. Go long and you lose the comparative advantages of a horizontal layout because users with small screens must scroll vertically and can’t see the entire landscape at once. Of course, layering the data into different views as an interactive can solve that. But sometimes you want to show the data all at once. And for that, a static graphic can work well.

Understandably, a map is often the solution. But maps have their limitations too. There’s only so much that you can infer from a map. If your data consist of more than 4-5 gradations it can be tough to create the at-a-glance, concise overview for which a map is best suited.

And if there are no regional patterns discernible in your map, readers wind up staring at a jumble of color with only a legend to tie it all together.

Which brings me to concentric charts. They’re not pie charts (if you look up pie charts on wikipedia, you will see that there is a distant cousin to the pie chart called a “ring chart,” also known as a multi-level pie or a radial tree). These appear to be somewhat visually similar to concentric circle graphs but have a different use–they tend to show hierarchy in data–you might see these when your computer shows you how much disc space you have, for example.

Filelight disk usage graphic

This ring chart shows computer hard drive disk space

A concentric chart, on the other hand, can tell a different story altogether. In a recent post on La Nacion’s subway strike graphic, I mentioned how designer Florencia Abd manages to plot out a time across four nodes (year, month, day and time) as well as another variable–type of incident/strike. That’s a lot of ground to cover in a static graphic. Imagine doing it in other ways and I’m sure you’ll agree.

La Nacion Conflictos Bajo Tierra

Because a circle is, well, round, its shape lends itself quite well to a relationship-based approach. Not so much a pie-chart (where the user sees the parts in their physical relationship to the whole), but rather using the organic form of a circle to help the user more easily compare complex data. And if you add concentric circles, you take advantage of the hierarchy inherent to those circles to create layers–an intuitive way to order your data–perfect for showing levels or ratings where you use the inner and outer rings to denote the endpoints in a scale (e.g., one thing is stronger, larger or more intense on the outside than it is on the inside) or time, as the subway graphic above shows (the outer ring shows 5 a.m. and the inner ring shows 11 p.m.).

So, what does all this have to do with the U.S. map? As I mentioned, the strength of a map is to show geographic relationships in data. For example, southern states vote “red” (or conservative) in the U.S.; whereas a swath northeastern states might vote “blue” (progressive). For this, a map is helpful because regional differences tell the story and are easy to spot.

But the nice thing about concentric charts is that they, too, can show geography, or any groupings, for that matter. As the Guardian’s example shows, each “slice” of the concentric chart belongs to a state and groups of slices are regions. In the Guardian example, each ring (or level) of the chart denotes a particular right afforded to gay couples.

The Guardian gay rights in the US

My team took this in a different direction. We wanted to show states and regions as well. But we also wanted to show change over time, as well as intensity on a scale. So when the Bureau of Labor Statistics released its employment figures, we had a few choices. We needed to show how changes in employment have affected each state since the recession (from April, 2007 to April, 2012). Because the recession started in December, 2007, we wanted to show how employment looked in each state before the recession, during the recession and how (and which) states were pulling themselves out of the recession.

We could have created an interactive that showed how the same views above changed over time (presumably you’d see a pre-recession view showing states doing well, a recession view showing most states doing poorly, and post-recession years showing mixed results). The most valuable piece of this would be, of course, geographical patterns in the data, if they existed (how did the Rust Belt fare, or the East Coast, for example). You could overlay this with population or any other demographic data to tell an interesting story.

When we looked at the data, we saw that there were not very strong geographic patterns to show. So we decided to create a concentric chart. Why? Because we didn’t have geographic patterns, but we did have temporal patterns (most states did poorly during a particular period of time, which contrasted well with the mixed results that states showed as they were attempting to pull themselves out of the recession, at least in terms of their employment figures). And the fact that we used a circle meant that we didn’t have to create a very long or wide table or chart, and we could stray from the map approach.

We decided to make this a light interactive–by rolling your cursor over each state’s cell you can see a small bar graph showing change in employment over time. This worked for us because our goal wasn’t to show specific numbers (how much employment rose and fell in a particular state), but rather intensity and patterns over time.

The debate continues (check out the comments on Nathan Yau’s post on the Guardian graphic) on whether or not these concentric graphs are merely eye candy when a simple bar or line chart would do just as well. I would opine that, if used correctly, they work well. Let me know if you agree. Here’s a screenshot of our interactive, and you can view the live version here.

Stateline PCS jobs interactive