This site publishes high-touch, time-intensive data visualizations (and has a business that sustains it)

Over 7,000 artists played in the New York City area in 2013. Only 21 of those later made it, really made it, headlining at a venue with an over 3,000-person capacity — among them, bigger names like Chance the Rapper, X Ambassadors, Sam Smith, and Sylvan Esso.

I learned this sort of random but fascinating tidbit from a data visualization titled “The Unlikely Odds of Making it Big,” from the site The Pudding.

The Pudding is the home to high-touch, painstakingly crafted data visualizations — what the site calls “visual essays” — that are distinct in their obsessive complexity over points of cultural curiosity. Most pieces stand wholly apart from the U.S. news cycle; no anxiety-inducing interactives around budget, taxes, health care. Want to see everywhere jazz legend Miles Davis is mentioned across Wikipedia, and how he’s connected to other people, recordings, and places? Here you go.

(Other things I’ve discovered browsing The Pudding’s interactives: that the town where I live is probably not the microbrew capital of the U.S., that there’s pretty strong evidence that NBA refs favor the home team, that the song “No Diggity” by Blackstreet is irrefutably timeless, at least based on Spotify play counts, compared to its 1990s peers.)

Pudding is the newly partitioned off editorial arm of a three-person data visualizations company Polygraph (polygraph.cool!), started two years ago by Matt Daniels, a consultant with a digital marketing background. Daniels and his partners Russell Goldenberg and Ilia Blinderman publish sumptuous visualizations that scratch personal itches. The Pudding also works closely with freelancers on pretty much whatever questions they’re interested in exploring visually, as long as it’s based on data. Freelancers are paid a flat rate of $5,000 for each piece.

“We’re all over the map. But basically, every individual picks their idea, we vet it ourselves and make sure the data’s there, that it’s interesting, and we just go off and do it,” Goldenberg told me. (The ideas backlog for The Pudding is listed out in this public Google Doc.) “Our goal is for The Pudding to be a weekly journal. We specifically seek out stories that aren’t news related, because we don’t want to compete in that space. The Washington Post, The New York Times, FiveThirtyEight, lots of places are doing interactive graphics well, doing multiple data journalism pieces per day. That doesn’t jive with what we want to be.”

Goldenberg previously worked at The Boston Globe as an interactive news developer and Blinderman’s a science and culture writer who studied data journalism at Columbia. Despite journalistic credentials, The Pudding (and Polygraph) isn’t aiming to be a journalistic enterprise. The team might in the course of developing a visualization call up a few people to run questions by them, or have to create their own data source (this freelancer’s exploration of the Hamilton musical libretto, for instance), but most of the data it builds interactives on is already available (no FOIAing needed).

Work gets promoted on The Pudding site, and through the Polygraph and Pudding newsletters, which will eventually merge into one. Polygraph’s newsletter sharing the latest visualizations has about 10,000 subscribers; The Pudding’s has about 1,000 after launching this year. Otherwise, promotion is largely word of mouth — and some pieces have been able to spread widely that way. They’re definitely open to collaborating with “more visible partners,” Goldenberg told me, though “we’re not being aggressive about our outreach.”

(A similar project popped up last year called The Thrust, which wanted to serve as a home for data visualization projects that didn’t fit with traditional news organizations or into their news cycles. The creators left for full-time jobs at ProPublica and The New York Times and the site has stopped updating.)

The moneymaking side of Polygraph functions like a digital agency, with Daniels, Goldenberg, and Blinderman pushing out projects for large clients like YouTube, Google News Lab, and Kickstarter. Goldenberg wouldn’t disclose how much they charge for these sponsored pieces, but revenue generated from a handful of client projects funds the entire editorial side, including paying for freelancers pieces and the three current full-time staffers’ salaries.

“We try to take on client work to just support our staff and basically to sustain The Pudding, with about three to six freelancers each quarter — what we’re doing is maybe kind of backwards,” Goldenberg said. “The thing about our editorial work is that also essentially serves as marketing for us. Generally, when we publish a new project on The Pudding, we get a few business inquiries. It’s a nice symbiotic relationship.”

Polygraph is also hiring for two more full-time positions — a “maker” and an editor — both at competitive salaries, which suggests that its client-side business is going quite well. Its ambitions looking forward, though, are straightforward: publish more interesting data-driven visualizations.

“We want to push forward the craft of visual storytelling, and these are not things you do on a daily basis,” Goldenberg said. “We still want to take our time and spend a couple of weeks, maybe a month or more, on a project. Unless we have dozens of people working with us, we wouldn’t really be able to publish more than once a week or so. We’re mostly just trying to establish that rhythm, and keep pushing out good pieces.”

Flourish + Google = free for newsrooms!

In conjunction with Google News Lab we’ll be giving Flourish free to all newsrooms – including organisation features such as private templates and projects. We’ll also be setting up a newsroom team to help journalists and news developers get the best out of Flourish. You can read more about the partnership in this piece in FastCoDesign and in this Medium post by Google News Lab data editor Simon Rogers. We also made this video to give an overview of Flourish for newsroom users:

Read full story here

Data needs empathy to make it real

A gif from ‘Beautiful in English’

The latest in our series of data visualisation projects sees Shirley Wu and Nadieh Bremer apply their unique take on life to Google data. The pair together make up Datasketch.es. Shirley is based in San Francisco, Nadieh in Amsterdam, and the two regularly produce complementary work that tells beautiful stories.

This month, in the latest in our visual series, we have not one but two visuals, as the team looked at Google data we haven’t explored before: translations and culture searches.

In Beautiful in English, Nadieh reveals the top searched words in Google Translate from different languages. She explores how the words vary from the mundane to the sublime, and notes a common current of optimism through a number of the languages.

6 out of the 10 languages have a positive vibe going on, with 3 of them wanting to know the translation for good while the most often translated word across all languages is beautiful, or, as they say in Italian, bella

Beautiful in English

And in the complementary visual, explore-adventure.com, Shirley looks at the top searched cultural locations in different countries. She examined the thousands of cultural and tourism searches that take place on Google and illustrates how different countries search in unique ways. For instance, cities are the most searched cultural destinations. But the searches also vary season by season.

Nadieh and Shirley have different styles but one thing radiates from their work: humanity.

Traditionally, a lot of data journalism and data visualisation has not been big on emphasising humanity. Former New York Times developer Jacob Harris wrote a prescient piece in 2015 calling for more empathy in data journalism. He cited ProPublica’s Scott Klein in talking about the importance of the ‘near’ view, in addition to the ‘far’ view that data journalism often takes. In other words, data journalism can often seem abstract and removed from your personal life. Bringing it closer, the ‘near’ view, is what makes it real and concrete. It’s the difference between flying over a place and remarking how the cars look like toys and being there on the road in the back seat of a taxi. One is remote and removed, the other is happening to you.

Typically, data journalism is often paired with traditional narrative reporting, which can focus on teasing out the empathy part of the equation. That leaves the data work feeling anonymous and impersonal. But in a world where visuals have to stand alone, that is no longer adequate. Wrote Harris:

the main question is this: should we even try with our graphics to make readers care? The Devil’s Advocate would argue that it’s not the responsibility of our interactives to make people feel something about a topic — that is usually handled by a narrative piece paired with them — but I feel that in these days where charts may be tweeted, reblogged, and aggregated out of context, you must assume your graphic will stand alone. Neither of these arguments consider what the reader actually expects. What does the reader expect to feel from journalism and how can we learn from their experiences?

Two years is a long time in data. Now, empathy is at the heart of some of the most innovative data journalism and visualisation around. Take the work of Mona Chalabi, for instance, in which she brings her hand-drawn analysis to bear on issues ranging from politics to sex without ever dumbing-down or patronising.

A few weeks ago, data artist Giorgia Lupi gave a Ted talk showing how data can bring us closer to ourselves, how we can find ourselves in that data. It has hit a nerve, with more than half a million views to date. In WorldPotus, which Giorgia and her partners produced as part of our data visualisation project, they used Search data to explore how the rest of the world searched for the 2016 US Presidential election. It was playful, fun, and smart, reflecting the uncertainty and nuance of the data itself.

This is where art and data visulisation blur, and maybe it has always been so. Minard’s famous visual exploration of the French army’s retreat from Moscow is both data visual and artwork, a piece that tells us as much about ourselves and our reaction to it as it does about the subject at hand. To some, it’s the best dataviz ever produced. Personally, I love how it tells a story — but for me it’s simply beautiful, and that’s enough.

Which is where Shirley and Nadieh’s work takes us. The latest in our series, produced with artistic direction from Alberto Cairo, showcases data we have never visualised before and does it in a way which is human, beautiful, and above all, fun. And that is something we need more of in data journalism.

Simon Rogers is Data Editor at the Google News Lab and director of the Data Journalism Awards. Like to work with us? Get in touch. You can read more about our data visualisation project on FastCo.


Data needs empathy to make it real was originally published in Google News Lab on Medium, where people are continuing the conversation by highlighting and responding to this story.

Envious of that dataviz? Now you can do it (with a Flourish)

What if you could re-use a data visualization? Data journalism is intricately related to open data — and open data journalism is traditionally taken to mean opening up just the data itself. But complex data visuals are often closed and difficult to reuse, even for the organisations which create them.

It makes sense — there is a kind of pride in creating a series of one-offs. However, some visuals deserve to be re-used with different data or with tweaks to design. Before today, re-using a visual without serious coding has only been a task for developers.

Flourish will make that process a whole lot easier. The tool, built by data journalism design team, Kiln, allows users to upload new data and change colours and details in complex visualisations.

Here at the Google News Lab, we are all about making the work of data journalists easier using technology, and we have teamed up with Kiln to make Flourish freely available to newsrooms and journalists. Over the next few months, we will be working with newsrooms to help them open up their visuals and make them easier to replicate and customize — and to re-use visualisations created and open sourced by other designers and data journalists.

Take this globe, built to display searches for immigration to G7 countries from around the world over time. Flourish allows new data to be uploaded and the new visual to be downloaded or embedded anywhere.

Connections globe map

Here’s a possible result: a visual I made with different data, showing searches for ‘move to…’ from G7 countries. It uses the same basic visual but with some key differences in content and colours.

Map showing searches for ‘move to…’ from G7 countries.

Flourish comes with several visual templates, ready for new data. It includes the globe above, a ‘horse race’ chart and a shaded map.

Some of the templates available in Flourish.

Visuals can just be reused as they are, or ‘stories’ can be created to allow you to narrate through the visual.

It turns any newsroom developer into a tools developer, allowing non-coding journalists to make high-end interactives and stories with no tech support. Crucially for the data journalism community, it lets newsrooms share templates with each other. Newsrooms can keep some private templates and release others to the world free or open-source.

From today, you can start using Flourish, or work with the team to create new templates and visual stories.

Interested? You can sign up here to find out more. Let me know what you do with it.

Read more on FastCo.

Simon Rogers is Data Editor at the Google News Lab and Director of the Data Journalism Awards (entries close soon!).


Envious of that dataviz? Now you can do it (with a Flourish) was originally published in Google News Lab on Medium, where people are continuing the conversation by highlighting and responding to this story.

New from Tilegrams: make a hex map with France and Germany borders

Last year we launched Tilegrams with Pitch Interactive to make the process of creating a hexagon map or other cartogram easier. Now we have a new version: as of today, Tilegrams supports regional boundaries in France and Germany.

Both countries will see elections this year (you can find Google Trends data on the France elections, which will take place first). But it could apply to any dataset.

Search interest in Paris St Germain (by Camilo Moreno Kuratomi)

The idea behind Tilegrams was to make the production of a cartogram open to everyone. When we launched it last year, I wrote:

This year, we will launch a number of tools to support data journalism. This particular tool is designed for serious developers and amateurs (like me) who want to make their own hexmaps. It’s published on github so anyone can take the code and re-use it. It allows you to make both static and interactive maps.

Since then, Al Jazeera used it for their US election results map (and wrote a Medium post about it here).

How does it work? It’s pretty straightforward:

Step 1: Choose a pre-loaded map

The tool comes pre-loaded with a number of key hexmaps to get you started.

Step 2: choose your data

If the pre-loaded maps aren’t what you need, then choose a dataset and see it visualised by clicking ‘Generate cartogram from Dataset’. You can also upload and paste in your own dataset in CSV format.

Step 3: edit the data

The ‘resolution’ slider changes what each hexagon represents. And if you don’t like the way the map looks, you can drag the hexagons around to create your own map style.

Step 4: export the visual

There are two download options: svg to produce a static image that can be edited afterwards; and TopoJSON for the interactive version.

You can read more about how to use it in this blogpost from the Pitch team.

Go straight to Tilegrams.


New from Tilegrams: make a hex map with France and Germany borders was originally published in Google News Lab on Medium, where people are continuing the conversation by highlighting and responding to this story.

What YouTube told us about the popularity of 2017’s Best Picture Nominees : An interview with…

What YouTube told us about the popularity of 2017’s Best Picture Nominees : An interview with Polygraph

On Sunday night, millions of Americans will tune into the 89th Academy Awards to celebrate the most critically acclaimed films of the year.

Over the last few weeks, we’ve been curious about whether YouTube data could tell us something about the differences in how Americans watched this year’s Best Picture nominees. Were there “hotspots” or certain parts of the country where La La Land was more popular? Were Americans in the Midwest more interested in Hacksaw Ridge than Americans in the South?

To answer these questions, we worked with Polygraph, a collective specializing in data visualization, to map America’s interest in this year’s Best Picture nominees. We gauged interest by taking a look at the YouTube trailer views for each film across the country and built heat maps to help tease out those geographic differences.

A heat map for each Oscar nominee’s popularity, based on YouTube trailer views. See more at: googletrends.github.io/google_oscars

We spoke with the team at Polygraph to learn more about their process in building this visualization and some of the data and design challenges they tackled along the way.

“It was easy to get lost in all the possibilities.”

On the data

What would you recommend to others working with YouTube data? Were there any unique challenges you faced with visualizing YouTube data?

The data gathering, in this case, was much simpler than many other projects, since we had access to all the granularity and detail we could ask for.

The downside of this was that it was easy to get lost in all the possibilities. Looking at past cartographic projects and assessing the level of detail they’ve used helped us a good deal by giving us a springboard for exploration, and allowed us to narrow down the number of worthwhile directions.

What was something that surprised you as you first started visualizing the YouTube data?

Not seeing results was a bit of a surprise!

The time variable played a key difference between us seeing interesting results and a painfully bland dataset, so it took several attempts to figure out the appropriate window of time for each movie’s analysis. This was particularly important because we needed a standardized number-crunching approach for all the nominees which would allow us to compare films that had hugely different popularity nationally, and were released throughout different parts of the year.

Did the data surface any patterns or hypotheses that you’d encourage other journalists or storytellers to further explore?

As the NY Times showed with their TV maps and the Oscars project reaffirmed, regional tastes in culture exist, despite being initially difficult to spot. These are particularly interesting when it comes to the urban/rural split, but also manifest in unexpected ways (e.g., Fences being disproportionately popular in Kansas) — we’d love to see greater explorations of these disparities.

“We really wanted readers to pick up on the hotspots that had a particular connection to the film.”

On the design

What was your design process in building the visualization?

Experimenting with varying levels of geographic smoothing. Upper: Views for Arrival’s trailer, with less smoothing, rendered in QGIS. Lower: Views for different nominees, with a higher degree of smoothing, rendered in D3.

After we processed the data, the first step was to do some preliminary mapping to get a sense of what we were working with. We decided to implement a regional smoothing algorithm to make the geographic trends more pronounced. We then brought the data into the browser, and started playing with color palettes, interaction, etc.

Seeing all the maps together allowed us to settle on the narrative of profiling the best-picture nominees with some additional research and annotations to explain some of the geographic trends. This guided the design hierarchy and layout of the story. Once we had the basic structure, it was just a lot of small design/code/feedback loops until we ended on something we were happy with.

What was a rule that you consciously followed in the design? A rule that you consciously broke?

Performance and a mobile-friendly experience were some guiding design principles. Although we toyed with making the maps interactive and zoomable, we determined that revealing the national trends was more compelling and tried to make the experience mimic that. We generated the maps using JavaScript, but then ended up baking them out to static image files. This way we didn’t need to load tons of data or do all the rendering client-side.

A rule we consciously broke was with our color scale. Technically speaking, we should have used a proper diverging color scheme, with equal parts below and above “normal.” We instead decided to bucket all of the under-indexing values into a single bin, and use a few breaks for the over-indexing values. This allowed the map to portray what we wanted — where the movie was most popular — without distracting with the other data points.

What’s one thing you intentionally wanted a reader to come away with? How did you design the visualization to enhance that?

We really wanted readers to pick up on the hotspots that had a particular connection to the film. We decided to experiment here with “connected annotations.” Instead of a traditional annotated map with an arrow, we decided to visually connect the prose and the map. We relied on a two-way hover event that triggered a visual change in both the specific section of prose and the hotspot on the map. This way we could direct the reader’s attention to the prose when looking at the map, and to the map when reading the prose.

A big thanks to the folks at Polygraph as we continue our series of visual experiments, alongside Alberto Cairo as consultant art director. Our last project was a look at language through Google Trends. Keep an eye out for our next project!


What YouTube told us about the popularity of 2017’s Best Picture Nominees : An interview with… was originally published in Google News Lab on Medium, where people are continuing the conversation by highlighting and responding to this story.

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