“LA on the Move”, our exhibition organized by Metro Art at Union Station in Los Angeles, California, opened in October and will remain on view through next year. Through larger-than-life graphics, a massive 3D map, playful character designs, and even animal sounds, we’ve created an immersive experience that asks Angelenos to see themselves reflected in the lives of coyotes, mountain lions, monarch butterflies, red-tailed hawks, and california kingsnakes.

From City Animals to Union Station

The seeds of “LA on the Move” were planted at ArtCenter College of Design, where we first encountered the City Animals class taught by Santiago Lombeyda and Ivan Cruz. “It was a topic I hadn’t really thought about before,” Jen recalls. “The interaction of humans and animals in LA County—it was super intriguing. The more we got to know the projects and the students, the more excited we became.” Then there was a chance to have an exhibition that pulled together a lot of these concepts and also showcased the student work, created in association with Metro Arts at Union Station. From there it just started rolling”.

The final projects from the City Animals class focused on speculative projects that explored how Angelenos could redesign their homes, backyards, and neighborhoods to better integrate with the natural world. Jason explains, “The projects that the students did were really about how people in LA could think about the intersection of the built environment—their homes, their yards, their backyards—with the natural world”. From there, we led two intensive workshop sessions with the students, working side by side to visualize ecological data in bold, accessible ways that were displayed in the ArtCenter student center for the following month.

From there, we were connected with Arroyos & Foothills Conservancy, a non-profit organization focused on preserving and restoring natural open spaces and wildlife habitats. They became an essential partner, sharing datasets on animal sightings, migration patterns, and habitat corridors across LA County as well as expert advice and access to Southern California’s environmental researchers.

The research process: Data meets daily life

“I think the first thing that we did, and what we always do, is begin with research,” Jen explains. “but in time, we leaned on the expertise of our friends at Arroyos & Foothills Conservancy—they were incredibly helpful. The other part, I think that’s very important, is collecting anecdotal information when you’re talking to people that live in Los Angeles about their experiences”.

For us, stepping away from the data is essential. “It’s important to step away from the facts and the figures, and start talking to people because the experience that Californians have with wildlife is completely different than a New Yorker’s,” Jen says. “You can’t just go about your business like a city dweller and ignore nature in California. It’s integrated into your day-to-day experience”.

Los Angeles, we discovered, is extraordinary in its biodiversity. Jason notes, “Los Angeles has such a unique environment. And what we found is that it’s actually one of the three areas in the world that is considered a biodiversity hotspot“. This became a cornerstone of the exhibition—LA isn’t just a city with some nature on the edges; it’s where wildness lives alongside urbanity in remarkable, sometimes precarious, ways.

Five animals, five stories

We chose to focus the exhibition on five species: coyotes, pumas (mountain lions), red-tailed hawks, california kingsnakes, and monarch butterflies. Each animal became a character in the larger narrative of LA residents navigating neighborhoods, dating scenes, commutes, and survival just like the humans around them.

“One of the first things that you drew was the coyote that says: ‘I love LA.’ That’s one of the featured images in the show,” Jason recalls. For Jen, this illustration became a statement of intent: “A human says, I love LA—and we all know this phrase—but animals live there too. What’s their role in this? So, we wanted to make sure that the animals and humans get equal time in this show”.

The personification of the animals was deliberate and humorous. Jen explains, “The more you learn about animals, how they’re mating with other animals, for instance, you think about the LA dating scene, and then you think about animals, which have some funny crossovers. As we have these neighborhoods in a city, they also have their neighborhoods.” Jason chimes in, “For example, a monarch butterfly says, ‘Hey babe, let’s overwinter in Mexico’—a line that could just as easily come from an Angeleno planning a winter getaway…” Jen adds, “And the monarch is saying like, I’ve got a really busy schedule.” Jason elaborates: “They have this multi-generational migration habit where up to five generations of butterflies will go from Central Mexico all the way up to Nova Scotia and Southern Canada and then back again. And they do this over five different generations. Even more remarkable—five generations later they’ll return to the same tree”.

The California kingsnake became another favorite. “Well, it’s not an LA Dodgers hat. Thank you very much,” Jen jokes, describing the snake’s illustrated headwear. “It’s a Los Angeles hat”. The kingsnake’s ability to live almost anywhere—from woodland to wetlands to suburban basements—made it a perfect symbol of LA’s adaptability. As we say, “you live in my backyard.”

Navigating the hard truths

While humor runs through the exhibition, we didn’t shy away from difficult realities. Rattlesnakes, for instance, posed a design challenge. “I made this drawing. When you might be on a hike, you may encounter a rattlesnake. And this is frightening, right?” Jen recalls. “There was like a discussion about making the rattlesnake so it wasn’t so intimidating, which was funny because I was like, well, a rattlesnake is intimidating and very scary, and you can’t really take animals and smooth out all the rough edges, right? Because that’s not what they are”!

The story of P-22, the famous mountain lion, underscored the fragility of human-wildlife interactions. Jason reflects, “Take the story of P-22—a famous mountain lion that was known around the Mount Wilson Observatory. And eventually, through a series of interactions with humans (and despite best intentions) he dies”. The exhibition addresses this directly, including data on rat poison’s devastating impact on mountain lions and the importance of hazing techniques—like carrying a can filled with coins—to maintain healthy boundaries.

“Even though we anthropomorphized the animals, we shouldn’t forget the fact that there are negative results of some of our interactions with the animals. We should be mindful of that”.

Making data visible and inviting

One of our core practices is taking complex datasets and transforming them into visuals that invite exploration rather than intimidation. “Part of what we do is find information and basically make it much more understandable to the general public and to ourselves,” Jen explains. “Like rat poison killing pumas, right? We made this diagram so that we have the data there, but you can just see it more clearly”.

A standout piece in the exhibition is the massive chart “Animal Species at Risk in California”, which visualizes 930 species by class and phylum, showing which are extinct, endangered, or imperiled. Working with data visualization collaborator Paul Buffa, we transformed this overwhelming dataset into the shape of a California poppy—the state’s native flower.

“If I saw this information in spreadsheets, I would be very intimidated because it’s just a lot of information,” Jen admits. “But since we put it into this California poppy, which is a native plant, it invites you over to explore it. You don’t have to look at every single detail, but it is fascinating”.

The wall also includes a Sankey diagram comparing California’s at-risk species to global standards—revealing that California has considerably more species in danger. And the bar chart showing imperiled species? “It literally towers over your head. It’s about seven and a half feet tall, so we wanted it to have a physical relation to how you encounter the data”.

The iconic title wall: Observing Union Station

The exhibition’s title wall features three illustrated characters walking across a vibrant gradient backdrop—each carrying something that subtly references animal behavior. Jen describes how these characters emerged: “We were standing in Union Station, and I could see people walking through, going from the trains to the entrance, and it gave me this idea about what kind of people would be walking through LA and walking particularly in Union Station”.

The older gentleman carries a bag of groceries, echoing how animals travel to forage and transport food. The young woman holds a bundle of flowers, referencing seed distribution—how seeds attach to animal coats or are eaten and deposited elsewhere. “All said and done, the more time you spend with the exhibition, you know every element is intentional and thought out and has a relationship to the information that we learn as we go along,” Jen explains.

The massive 3D map: Placing yourself in the data

Perhaps the most captivating element of LA on the Move is the enormous 3D map, created in collaboration with Julian Hoffmann Anton. This wasn’t just a cartographic exercise—it became a months-long process of negotiation, expansion, and refinement.

“Every project we do, we discuss a map component,” Jen says. “And sometimes we have time to do it, and sometimes we don’t because what starts as a simple map becomes very complex. It’s because a map is political. You can’t leave anyone off because they’ll notice”.

Initially, the map focused narrowly on downtown LA and Union Station. But through conversations with Metro Arts staff and community input, it expanded dramatically—eventually encompassing all of LA County and parts of Orange and San Bernardino Counties. “We were pushed and pushed on the map, but that’s not a bad thing. It’s a much more inclusive map, so when visitors come to Union Station, they can find themselves”.

In addition to showing every detail of the city, the map tracks sightings of all five featured species across the region, revealing fascinating patterns. Mountain lion sightings appear surprisingly far south of downtown; California kingsnakes cluster in parks and mountains but occasionally show up near Marina Del Rey; while coyote sightings may reflect research centers as much as actual populations.

“I’ve never seen a map of this scale, physically, of this detail,” Jason marvels. “It’s an extremely detailed 3D rendering of the entire metro area”. And because it wraps around a corner, visitors can find neighborhoods that might have been cropped out of a conventional map. Jen describes a photograph of a man pointing to the side panel: “He’s finding himself, which we wouldn’t have had in our original idea”.

Adding Sound: Activating the Space

For the first time in a Data Vandals project, we incorporated audio. “I pushed for this because we wanted to activate the space as much as possible,” Jen says. “We’re dealing with walls, and we wanted ways to expand these rectangles out”.

Visitors can hear the sounds of pumas, coyotes, and hawks. “I thought, okay, if I’m walking through Union Station, what is it like to hear some of these animals?” Jen explains. The sounds are surprising—sometimes beautiful, sometimes unsettling. Jason describes, “The mountain lion has lots of really low growls, more aggressive than a purr, and I found those to be unsettling”. Coyote calls also sound strange and a bit frightening, but these sound elements ground the exhibition in sensory reality, reminding visitors that these are not cartoons but living, breathing neighbors.

Iconic cutouts and LA signage culture

Atop each wall, we placed large cutouts of the animals lifted high on Sintra board to add height and visual drama. Jason says, “We wanted them to refer to the history of the Hollywood back lot, even the Hollywood sign itself.”

Jen reflects on LA’s distinctive signage culture: “I think the signage is very different from anything you ever really see on the East Coast; in New York we don’t have that kind of sign culture and I find it fascinating and really attractive”.

The billboard aesthetic also responds to Union Station’s architecture—a stunning 1930s Art Deco space with soaring ceilings and intricate tilework. “Union Station is so gorgeous, you want to try to do it justice. Something that iconic, you worry that whatever you do is going to be overwhelmed”. To honor the building, we photographed the tile floors and extracted colors to integrate into our palette, creating a dialogue between the historic architecture and our contemporary street-style graphics.

As the exhibition settles into its year-long run, we hope it becomes a recurring destination; a place where commuters pause for five extra minutes, where families return to discover new details, where Angelenos see their neighborhoods reflected in a 3D landscape populated by shared species.

“I just want people to enjoy it and have fun with it and see themselves in the data,” Jen says. “It’s so fun to see the different types of people, and I feel like I could draw those people and put them into the exhibition. It reflects a lot of our intentions”.

Jason hopes for depth and revisitation: “I’d love that the exhibition is very detailed; you can return to it over and over and learn something new each time that you revisit it”. And Jen adds with a laugh, “I hope it brings us back to California again and again –  we love LA “!

“LA on the Move” is on view at Union Station through 2026.

For more information: https://datavandals.com/la-on-the-move.

The post LA on the Move: Data Vandals Bring Wildlife and Humans Together at Union Station appeared first on Nightingale.

Business leaders once championed dashboards as windows into organizational performance, but they became too familiar, too technical, and the value diminished. As evidence, one look at the relationship between those with roles in “business intelligence” in comparison to the business leaders they serve shows the massive gap in seniority, influence, and wages.

How did this happen? Let’s discuss these 3 ideas:

• Dashboard rot devalued BI
• Data people were never trained in design or communication
• D3.js is complicated

Dashboard rot devalued BI

Business leaders scrambled to use data to inform the C-suite, and in the process, multiple layers of the organization had their own dashboards. When BI software became a premium license, it was only a matter of time before enterprises began counting which dashboards were used and which had never been used. The overwhelming under-utilization of dashboards across an organization led to the term “dashboard rot” which is a fundamental misunderstanding of what the value was in the first place. It’s like counting all Word documents in an organization vs what is published. The value has always been in the insight, not in the number of documents.

The way BI software was monetized ended up devaluing its own importance. Dashboards became an IT cost-center in many regards instead of a strategic advantage. It became a burden in the organization, and in many organizations, “reporting” was seen as boring and a potential waste of time.

Thinking of the value of BI differently, if a dashboard can make a $1M decision easier, is it worth $1M? If, over its lifetime, it supports a $5B company for running its business daily, does that still make it worth $1M or more? On the contrary, organizations don’t think of investing in software in the same way: software is a strategic advantage, but dashboards are just the cost of doing business.

Data people were never trained in design or communications

Maybe part of the reason why dashboards instill a certain amount of hesitation is because most are not well designed. Many people working in analytics come from data science, data engineering, or data analysis backgrounds, and those fields lack significant design or communications training. While it is impossible to say all dashboards are badly designed, I’m certain that most people who create dashboards do not consider themselves to be “good designers.”

There’s a big difference between the kind of high-level graphic design we see in advertising or in consumer apps and the kind of important tweaks that could easily elevate most dashboards. In fact, most dashboards can probably get a significant lift by adjusting the language used in titles and labels alone.

The success of data literacy programs proves the importance of training people in more than just foundational data visualization practices.  This shift—if we can make it one—from data towards communication might see the value returned to business intelligence, ushering in a new generation of thought partnership between analytics professionals and organizational leadership.

D3 is complicated

The reason why BI software exists is because custom coding charts was difficult. When D3.js was invented, an entirely new way to draw shapes in the browser created new opportunities to visualize data from simple charts to multidimensional interactive tools. But developing charts with D3.js was far from straightforward and pushed it into the domain of software development.

While it is not the fault of D3 that dashboards have lost their zest, the complexity of doing this work opened the door for faster (and therefore cheaper) tools to take its place. Many frameworks to create interactive charts for business sprang up each with their own tradeoffs, each focused on their own flavor of front-end, and in the process, the software design was assigned to the UX designer. I’m a former UX designer, and I can tell you definitively that data visualization and data communication simply does not exist in user experience design—despite the fact that almost all software design is a visualization of data.

Maybe it’s time we drop the idea of dashboards and focus instead on data communication? By adopting this shift we might just recontextualize the power of data.

There’s a lot here to discuss, so please let me know what you think!

This article originally appeared at: https://www.linkedin.com/pulse/word-dashboard-broken-jason-forrest-agency-aco1e

The post The “Dashboard” is Broken appeared first on Nightingale.

The Data Visualization Society (DVS) launched on February 20, 2019 with a post on Medium. It was a shock, a movement, a global phenomenon.

That article begins: 

“Over the last few years, data visualization has grown dramatically more common in our day-to-day experience. We see it in our news articles, in our social media and, for many of us, every day at work. The task of designing, creating, and deploying data visualization isn’t just a single profession but shows up in the work of engineers, analysts, salespeople, doctors, journalists, designers, students, and more… We created the Data Visualization Society in order to address that lack of professional development in the field, create a larger community, and provide a space that makes it more possible to learn from one another, differences and all. In doing so, we hope to establish guidelines for professional development to support people starting out in data visualization as well as those who have established careers.”

The DVS was started by Elijah Meeks, Amy Cesal, and Mollie Pettit months after a great conference called Tapestry held at the University of Miami in the fall of 2018. I was there because, earlier in 2018, I had written a series of lengthy articles about the data visualizations of W.E.B. Du Bois and had been chosen to give what was probably the first public presentation on his amazing work. I was so nervous that I read my talk word-for-word from a paper script because I was so worried that I’d mess up in front of a room of my new heroes.

On the first day, I met Elijah and he invited me to sit with him for lunch. In the next few minutes, the people who joined that table were a who’s who of dataviz—Cole Nussbaumer Knaflic, Mona Chalabi, Steve Wexler—I think Amanda Makulec was there too, or maybe she was at the next table. I was a big fan of RJ Andrews and he was at the next table. I was buzzing just to be near them all. I met Robert Crocker in the coffee line. Bill Shandler, too. Mollie Pettit had some kind of an amazing jacket and I told my wife how cool she was that night. Joey Cherderchuck showed off a demo that literally blew my mind the next day. I had no idea at the time, but many of the people at that conference turned out to have a major role in my life ever since. I had arrived into a scene somehow without even realizing it was a scene.

But a few months later, when Amy, Elijah, and Mollie made their announcement, I instantly joined. I think I was the seventh person to sign up for the DVS, and I pretty much blew off work to watch the DVS Slack grow by hundreds of people every few minutes. Something was happening and it was exhilarating!

Like I said, I had been writing a lot about dataviz and I was bummed about not having a good home to publish in. The main game in town at the time was Toward Data Science, and the data visualization subsection was a small, neglected back corner of their Medium empire. 

I was chatting with Elijah—it was my fourth or fifth message on the first day of the DVS Slack: “You know, if we do this thing right, we could make an amazing publication.” A few messages later I said, “if DVS grows, maybe we could do a print magazine one day!” That was the beginning of Nightingale. I knew from day one that we could make a community publication, and we would eventually make something physical. 

I became the Publications Director, and together with Elijah, we brokered a deal with Medium to create an exclusive publication which they paid for monthly. It felt clandestine.

On July 15, 2019, we launched Nightingale the Journal of the Data Visualization Society with four articles: 

Welcome To Nightingale by me, Jason Forrest, an introduction to the new publication.

Florence Nightingale is a Design Hero by RJ Andrews, which was the germ of an idea that evolved into his incredible book on Nightingale a few years later.

Beyond Nightingale: Being a Woman in Data Visualization by Stephanie Evergreen on tokenism in our community.

From the Battlefield to Basketball: A Data Visualization Journey with Florence Nightingale by Senthil Natarajan on creating a rose diagram for basketball stats.

Together, we felt these four articles represented a significant new direction for discourse in our community. These were not peer-reviewed papers, these were not articles playing second fiddle to hum-drum machine learning how-tos—these were serious explorations into divergent corners of dataviz that had never been explored. They were interesting, and they were alive!

My article started with this perky introduction:

“Nightingale will bring you high-quality articles each day covering many of the applications of data visualization — including education, entertainment, history, sports, best practices, new techniques, and any other form of visual information design we find compelling. We’ll host interviews with thought leaders and interesting people from the DVS community and beyond. Like all adventurers, we want to explore the unexplored, to look to the past and dream of the future. It’s exciting!”

So, here we are, six years later, and it’s worth noting that, as adventurers—we have done exactly this. We have explored so many new concepts that have never been written about in dataviz before. We created new ways to share our thoughts, spotlight new perspectives, share our work in progress, and reflect on our accomplishments and failures in an effort to help others. We held interviews, reviewed books and conferences, and created a global platform for our field to easily share their feelings and half-baked ideas. I am shocked at how thoroughly our mission at Nightingale has lived up to this initial statement and that of the DVS in general. We have created a living, active dialogue.

But establishing Nightingale in partnership with Medium also created two crucial mechanisms for our success: it allowed us to pay writers and hire staff to edit, illustrate, post, and promote each article, but it also provided critical start-up funding for the DVS. It can not be underscored enough how building this editorial team has helped our community! While no one has become rich from writing a Nightingale article (or being on our team) we can honestly say that everyone involved has been paid. By establishing an editorial team, we ensured that articles were systematically published at a sustainable rate to keep the conversation going. Consistency is so important for a professional publication, and we’re proud to have fought the good fight to keep Nightingale standards high, to keep everyone paid, and publish roughly two thousand articles.

Building an editorial team was the real joy

It started with our first managing editor, Isaac Levy-Rubinett, a sports journalist with an interest in dataviz. He helped us establish our first group of editors, created standards and processes to get articles edited, designed, posted, and promoted. Isaac was a gifted editor and created the theme week concept and much more. Our next managing editor was Mary Aviles, a design researcher and writer from Detroit who brought our publication to the next level in many ways. Mary got shit done but with grace and a deep consideration for our writers and community. 

On Feb 8, 2021, Mary and I co-published an article called “The Future Of Nightingale,” announcing the brazen goal of launching a print magazine. I honestly don’t think anyone really understood what that meant, but Mary and I were excited to give it a try. We pulled in our hot-shot editor Claire Santoro to be our new “Content Editor”—a role focused on creating and editing the best content for the new print magazine. Claire is a data analyst focused on sustainability and she crafted much of the vibe of Nightingale Magazine. Claire came up with a lot of our series content, like the popular Dataviz Horror Stories, etc.

I remember we had a few meetings with our editorial committee team where we asked questions like: “What even goes in a print magazine?” and “How do you ship them?,” but we figured it all out together. We also re-platformed from Medium to our own website (this one) and set up a CMS, which was a total pain, but it meant that all articles would be free and open to everyone.

This was right as the first Outlier was happening. It was still the pandemic, so it was online, and that’s where we first saw the amazing work of Julie Brunet (aka datacitron). It was a leap-before-look moment when I wrote her on Slack, and immediately said:  “Do you want to be our Creative Director?” She agreed and designed our brand and (almost) every page of our print magazines since! Some of my most exciting professional moments over the past six years were seeing her designs for the first time—and there are too many of those special moments to count!

Various images from the first five issues of Nightingale Magazine

We had published two issues of Nightingale magazine when our next Managing Editor, Emily Barone, joined us after being a data journalist and editor at Time Magazine. We were so excited because we were finally working with someone who had done this before! Emily brought so much care to her role in addition to her operational publishing expertise. She came up with the special sections in the back of each magazine, among many innovations, and helped us publish two more print magazines and another few hundred articles online. Emily also handled a bunch of the extremely difficult shipping logistics and set us on the right road for Issue 5 of the magazine.

Printed copies of Issue 5 at the printers. About 600 of these also took an international trip to EU that took more than three months—UGG!

I’m so proud of each member of our editorial team! Our current group—Will, Teo, Ale, and Julie—have already begun to take over my day-to-day responsibilities and will continue to provide the same level of care and enthusiasm for our writers and community as we have for the past six years. Honestly, there’s so much I can say about each of our editors (my friends) that I could go on and on. But I’ll wrap this up by saying that I truly learned so much from each one of you and I will forever be grateful for your collaboration!

My master plan—revealed!

Ok, I’ll admit it. For the last six years I had an agenda all along—to expand our community and influence it towards creating more illustrative, human-focused design. When I announced the magazine, I told people “it’s like a fashion magazine, but for dataviz”—and that was exactly the point. To make dataviz more alluring and to build on the magic of embodying data by showing our community the added power of illustration and design.

This is in service to attracting more attention to the data, to shining a light on new perspectives, and to propose a new way of communicating information to people. If you look at dataviz before Nightingale, and look at it today, I think you can see how we helped dataviz evolve our field in this direction. Sure, Nightingale hasn’t been the only publication pushing for this, but it’s easy to see how we championed creative, engaging ways to illustrate data and expand the scope of what is possible—and we have done this on a global scale.

In conclusion

So, here it is, the 1,444th article. Yes, it’s a bit bittersweet, but transitioning into my next phase as contributor, patron, and I hope, advertiser, means that I get to find new ways to engage with our community and support this amazing publication that has done far more for me than I can express. 

I thank my co-founder and friend, Elijah Meeks, for your collaboration over all these years. You always showed me so much respect from our first meeting to today, and I have learned much from your guidance and become wiser for (mostly) following it. 

I’d also like to warmly thank my friend and collaborator, Amanda Makulec, the former Executive Director of the DVS, who has been with me from the beginning of the DVS until now. We have a deep respect for each other and have supported each other through the ups and downs (yes, there have been a few of those), but we always remained focused on doing what was best for our community. I can’t wait to see where you go in your next chapter!

Lastly – I WANT TO THANK YOU!!! For the past six years, I have constantly engaged with our global dataviz community as an editor, on social, at conferences, answering your customer questions and complaints (yes, mistakes have been made!) and I remain still buzzing to just be part of it all. It’s like that moment back at the Tapestry Conference, when I was surrounded by all those famous dataviz people I had heard about—and that feeling just never stopped. In many ways, the community I feel a part of today is a reflection of the community I had always wanted to be part of—like a dream come true, a fantasy realized, a warm conversation with old friends. Thank you all for being so amazingly kind.

What’s next for me?

As most people know, I have a lot of energy and a lot of ideas!

I’m currently building the Jason Forrest Agency—a dataviz agency specializing in interactive projects in business. We’re small but growing fast, and I think we bring a different perspective on how to apply data storytelling concepts in a way that feels more relevant than ever.

I have also been hard at work on Data Vandals, a data activism project which is becoming increasingly more public. There’s so much more to explore by making dataviz more experiential and public. We’re excited that the idea is catching on! 

I also have a third “big thing” that is starting later this year. Unfortunately, I can’t announce it just yet, but my goal of advancing a more illustrative, human version of dataviz, and helping to open it up to the general public remains my focus—and it feels like the conversation will only get more dynamic from here.

Lastly, I look forward to writing more! I started Nightingale because I was a writer, but slowly this got pushed aside to deal with fun tasks like international shipping. I’ve also finished a book, so there will be so much more to write about, elaborate upon, and promote! 

So yes, I’ll be busy, and easy to find.

THANK YOU SO MUCH—IT’S BEEN AN HONOR!

Jason Forrest

The post My 6 Years at Nightingale: 1,443 Digital Articles, 5 Print Magazines, and a Whole Lot of Love appeared first on Nightingale.

Not many people know this, but I had a protracted search for work a little more than a decade ago. Eventually, I did land a job. And after I got it, I leaned into the process of hiring others so that I could learn how to build a team and get jobs in the future. Since then, I’ve hired—or helped to hire—dozens of people of various skills and job tenure, so I have a good understanding of what hiring managers are looking for, and how the process works.

Searching for work when you know you have the skills—but may not understand the right approach to get the job—is difficult in many ways. More than anything, preparing to get work, and then getting the work, is a process. Understanding the system will help you navigate the ambiguity and help you (try to) remain calm. Let’s look at an average hiring process so that we can customize our approach for each step.

What are people looking for?

For the job to open up in the first place, the hiring manager may have had to fight to get the headcount or maybe the organization identified the need and is creating a new role. Regardless, a role on a team usually starts long before you ever get involved—it’s good to keep this in mind. 

Usually, a job posting is the next step. This is a short description of the ideal candidate—a “unicorn” who can do everything from hardcore tech skills to nuanced skills in communication and management. Everyone knows that finding the right mix of skills may not be possible; but a job posting is, essentially, fishing for talent—and employers never know who will bite.

Getting past the AI filters

From there things take a bad turn. The rise of AI means that every resume is scanned by a bot for keywords and given a “job-fit score.” The highest-scoring resumes are then passed to a recruiter to review. That reduces the pool of applicants from hundreds to just a handful – it’s brutal. But when a real person finally looks at your resume, they check for matches to the skills. If you have a good hiring manager, they will look for, and explore, a portfolio of work you have linked from your resume. Having a portfolio is your best way to differentiate yourself from the competition.

We live in a time where traditional higher education faces challenges in validity from tech bootcamps and professional training. This reduces the need for a brilliant resume because showing people what you can do is a more direct way to help them understand your skills and it’s more immediate than a description of your academic or professional career.

Unfortunately, you absolutely still need a resume (I’m sorry!) to pass the bot test. Do yourself a favor and make your resume as minimal as possible, filling it with keywords that will connect to the skills in the posting and nothing more. Can you do data engineering? Check. Have you used Tableau? Check. Have you designed in Figma? Check. Your resume is for the machines, so make it unimaginative and as easy to read/scan as possible. Create it as text only. Elaborately designed PDFs are usually skipped, so don’t waste your time—spend that time on your portfolio! 

It’s astounding to me that so many people in data viz don’t have an online portfolio. Data viz is something seen and experienced, so showing people what you can do is essential. 

Your portfolio should do a few things: 

1. Show your published work, or sanitized examples of what you have done.
2. Demonstrate your skills and abilities in design, data, and data viz.
3. Walk through your process: how do you understand data and collaborate with others?
4. If possible, demonstrate who you are as a person. Are you curious? Creative? Do you have a mission? Are you fun? Prove it!

Congratulations, you got an interview!

Next come the interviews. Every organization has a different approach, but they all have a few steps. The first interview is a screen for team fit and skill check. They are trying to answer two questions:
(1) Is this a person I could see myself working with?
(2) Can they do the job?  

If you pass the first interview, you are often connected to technical colleagues to ask more detailed questions. They may be probing for engineering skills, a design review, or checking with other managers or team leaders. Once you pass those, they may want to conduct a test. This can take the form of an in-person coding review to an assignment to be done at home. After that, there’s usually one last interview with the big boss. Let’s get into the details on how you can optimize for each step.

The people interviewing you are likely sandwiching it between other meetings and may only be giving you a fraction of their attention. Your job is to get their full attention—to grab them and make them curious to learn more. One way to do this is to come with your own questions. Ask questions to your interviewer about the team, their skills, process, collaboration, timelines, and culture. This signals that you are curious and proactive. 

As you progress to the next round of interviews, be sure to take notes and respond personally to everyone using their names. These are your future colleagues and you want to build rapport from the first day. It’s crucially important that you take a collaborative mindset even in the interviews. If someone asks you a question that you don’t know—say you don’t know it and that you’ll follow up afterward (and you better do it, too). This shows you can be trusted and aren’t full of hot air.

If they give you a test, try not to get too stressed. Consider it more like a collaboration with professionals and try to have fun. Yes, they are looking for you to demonstrate what you know, but are also looking to see how you work. Try to keep your sense of humor. If you don’t know something, use it as an example to explain how you learn on the job. Remember, it’s not just a test of skills, but of how you react to situations.

When you get to the last round interview and you are talking to the big boss, this is mostly a formality, so take this opportunity to learn about the vision and direction of the organization. Try to get them to open up about where they want to lead the group, and if they do, it’s an opportunity to help them accomplish their goals after you get hired—it’s like insider information for your future team. It also shows that you are ambitious and goal-driven and that you care about the trajectory of your new team.

Game the system

Once you understand the process, and have empathy for the people behind it, you instantly get some sense of how to optimize for each step. Be prepared, so try to focus on what you can control and let go of what you can’t.  Keep in mind that it’s a numbers game—apply to as many positions as you can. When you have interviews, make them count. The process of getting a job is both impersonal and deeply personal—understanding this is a measure of your professional maturity.

Looking for a job is super hard—trust me, I know—so try to keep your emotions in check. It’s easy to get worn down by the process so focus on who you are. Put your energy into your portfolio, your passion projects, and be prepared for the interviews. Get plenty of exercise, try to take the weekends off, and spend time with friends, family, and your professional network. I’m always amazed by how many people want to help—let them! 

Looking for work or hiring? Check out the Data Visualization Society’s Job Board.

The post How to Get a Job appeared first on Nightingale.

The Data Vandals (Jen Ray and Jason Forrest) are based in New York City and we see our work as an act of civic participation. Our work has taken a few forms: We have created isotype-inspired performances to talk about the hopes and fears of New York City, or the lesser known history of the Lower East Side of Manhattan We’ve also shared  ecological information about oak trees in Washington Square Park, and made a provocative game to engage people in a deeper conversation about gun violence.

This past summer, the Data Vandals went international, as we were invited to create a collaborative project with the graduate students of the VisCom department at the Kunstuniversität Linz, an art and design school in Linz, Austria. This was part of the school’s 50th anniversary celebration and part of a larger program called “Radical Collective.” We were invited by our friend Tina Frank, who chairs the VisCom department and has a long history of media-based social interventions.

Getting started

There were a lot of unknowns as we started the project, but we start any project by trying to understand our communication objectives. After exploring some options on what data we could use, we decided to focus on the students’ thoughts, feelings, and attitudes about  art, careers, and genAI. 

In addition, our project would also act as an index of the graduate student projects which would be exhibited at the same time. The combined artworks would be presented as part of a public exhibition on the main square of Linz. 

We decided to gather the data and student details into a Data Vandals kiosk. While talking with architecture professor Clemens Bauder, we all agreed to take inspiration from Herbert Bayer’s “Design for a Newspaper Kiosk” from 1924, while using simple materials of wood, cardboard, and hand painted signs. We also agreed to create a performance next to the kiosk that drew on anecdotal data from the assembled audience.

Collecting the data

In order to collect data from the students, we created a series of posters with survey questions. These poster surveys were placed around the art school near different departments and were designed to set an irreverent tone. As you can see from the photos below, the students gladly vandalized the posters with their data just as we had asked them to!

After collecting the data from the posters, we created a series of isotype-inspired designs using icons from the original posters.  We printed some of the posters quite large, which helped us to think about how people react to the designs when printed at scale. Since we are used to seeing charts online or as posters, seeing an isotype printed at a human scale creates a completely different emotional attachment to the image and elevates the importance of the data. In the end, we opted for a medium-size poster so that the overall design of the kiosk was more cohesive.

From there, we met with each student to extract some of their data to create an index of the rest of the exhibition. Combined with the poster data, this became the material for our sculpture and performance.

Let’s get dirty!

It was time to build and paint the sculpture. Even our teenage son, Wolfgang, got involved with the construction and worked alongside the other artists in the woodshop. As the kiosk was being built, we discovered an intuitive way to organize the data by filling two sides of the kiosk with data from the student projects and the other two sides with data from the poster surveys. To make the design as vibrant as possible, we added several whirligigs to catch the wind as well as a full-sized Data Vandals flag.

Radical Times – the exhibition

The only thing left to do was to set up the exhibition — and it went off without a hitch despite being threatened by some ominous storm clouds (potentially a problem given out simple materials!). The exhibition was installed at the foot of Linz’s  Pestsäule; a baroque column celebrating survival over pestilence and war, set in one of the oldest plazas in Europe. The citizens and tourists of Linz wandered the exhibits asking questions and joking with the students, obviously enjoying their surprise data encounter.

Photos by Violetta Wakolbinger or the Data Vandals

Our kiosk and the 10 additional student projects were collected together under the title “Radical Times.” The title of the show was shouted many times through a bullhorn held by Tina Frank.. In the video below you can hear her addressing the citizens of Linz.

Beach Balls of Data!

The only thing left was our performance, but we only had a few minutes to dash through it to beat the rain! This performance was a lot more fun than we planned as we again experimented with the format. We took the student survey data and broke it into deciles. Then, we painted a bunch of inflatable beach balls with one side orange, the other side as blue, and few as white. From here we asked for 10 volunteers to stand in a line. Jason introduced each survey question and asked the audience to throw the corresponding number of balls to the line of volunteers. For the question “I think my social media is haunted,” we had five orange balls (for the ”no” responses), three blue (“yes”) and two whites (“neither”) .  When the 10 volunteers held the balls in front of them, it created a stacked bar chart. For fun, we threw the balls back and forth and it turned into more of a “community” action with a lot of laughter! It was fun chaos but for each data point, you could see people really got into the action of playing with the data.

Photos by Violetta Wakolbinger or the Data Vandals

Bonus track – The Bar Chart

Our host, Tina Frank, had the idea to celebrate the exhibition with a “data drink” reception! She built out the idea with her students and we knew it was an awesome idea. The contents of the drink were based on the relationship between temperature change and rainfall levels in Linz – as temperatures have risen, rainfall has fallen. The customer had three choices: 2002, 2022, 2042. Drinks in 2042 had more alcohol and just a bit of water whereas drinks in 2002 were much more balanced and tasted much better. It was a hit! While many people unsurprisingly went with the stronger drinks, the 3 time periods provided some ability to modulate how much alcohol you drank, and each round brought a discussion on climate change.

We learned a lot AND had fun doing it

As I mentioned before, we consider all our projects to be experiments, and this project was one of the most interesting and successful. The deeply collaborative process allowed us to build something unique, and the students and citizens of Linz really responded to the design and overall feel of the kiosk. We were delighted to be part of this exhibit and just loved working with Tina, the professors of the VisCom department, and the students in the program. 

There are a few big lessons that we learned on this project. It helped us see the immediacy of our data on the students and professors as many of them graduated or looked towards their next year of school. It helped us understand how the scale and playful design of our kiosk helped to capture people’s attention. But most of all, we realized that we were able to connect with a community through data visualization by collaborating with the students and becoming a part of their community, at least for a few days.

The final data kiosk

Photos by Violetta Wakolbinger or the Data Vandals

More images from the rest of the exhibit

Faculty: Tina Frank, Anna Artaker, Marianne Lechner, Clemens Bauder

Students: Liza Belkevich, Martha Büchel, Louisa Clever, Leyla Dehring, Sebastian Dorner, Lisa Endresz, Alisa Matern, Mario Moder, Sophie Morelli, Anna Painer, Philip Paulus, Liza Rashica, Rosalie Siegl, Ivan Sukhov

Special thanks to our friend Markus Fiedler for all his support and fun!

Photos by Violetta Wakolbinger or the Data Vandals

The post The Data Vandals Take to the Streets – Of Linz! appeared first on Nightingale.

Monroe N. Work was an African American sociologist, scholar, and researcher who spent his life collecting information and helping others to understand it. The highlight of his career, according to Work, was the nine editions of the Negro Year Book between 1912 and 1938. Each edition was an encyclopedic collection of yearly facts and data that covered many aspects of African American life as compiled by Work from data submitted from the wider community. Each subsequent edition quickly became the essential source of Black data in the United States and was reported on widely by the White and Black press and used as a resource equally in many schools in America and abroad. 

But their author, Monroe N. Work, remains far less known than his contemporaries W.E.B. Du Bois and Booker T. Washington despite collaborating directly with both leaders. After sharing research with Du Bois early in his career, Work had the opportunity to start the Department of Records and Research at the Tuskegee Normal and Industrial Institute, which was presided over by Booker T. Washington. 

This positioned him at the intersection of Black leadership and education in the US for most of his life, to which Monroe Work threw himself into the task of expanding public consciousness through data. In order to drive the importance of certain datasets, the Negro Year Book went a step further by featuring a number of hand-drawn charts focusing on education, healthcare, and mortality. 

Collecting data may not be the calling for the most extroverted people, and this certainly was the case with Monroe Work. He was a soft-spoken, hard-working, and tenacious collector of facts whose dedication to data provided generations of scholars with the empirical ammunition to fight for equality and justice.

Find all editions of the Negro Year Book, plus my full research documentation in this public folder.

The road to Tuskegee

Born to formerly enslaved parents, Work had a protracted education (he didn’t get to attend high school until the age of 23) which eventually brought him to enroll at the University of Chicago to become a sociologist. After graduating in 1903, Work moved to Savannah, Georgia to work at Georgia State Industrial College, which offered a small salary but gave him access to a vibrant Black community and a start for his research.  

Moving to Savannah provided Work proximity to W.E.B. Du Bois in Atlanta, who welcomed him into a long-standing collaboration as a fellow Black scholar. After publishing several articles in Du Bois’s journal at Atlanta University, Du Bois personally invited Work to the initial meeting of the Niagara Movement conference in 1905 as a member of the “Committee on Crime, Rescue, and Reform” as well as the “Committee on Interstate Conditions and Needs.” 

Savannah, Georgia was a charged social environment and Work flourished as a key member of Black society. He established the Savannah Men’s Sunday Club with over 300 members and featured speakers (including Du Bois, Robert E. Park, and others) sharing new ideas on education, healthcare, crime, housing, and other factors inhibiting social equity—topics known collectively at the time as the “Race Problem.” This gave Work a platform to mature professionally and share a number of important papers on health and crime, as well as his early research on African languages. Savannah was also where Work met his wife of 41 years, Florence Evelyn Hendrickson.

He quickly became professionally respected as a fastidious keeper of the facts and his activism-through-research caught the eye of Booker T. Washington, a skilled and charismatic orator, fund-raiser, and president of Tuskegee Normal and Industrial Institute (later University) in Alabama that pioneered industrial, agricultural, and higher-education for African Americans. Washington at first contacted Du Bois to start a history department at Tuskegee, but when he declined, Work was contacted to consider the post. The decision to move to Tuskegee seemed like an easy decision, here’s what Work—and his wife Florence—had to say about it:

After arriving in Alabama, Work created the “Plan for Making Tuskegee a Greater Center for Information Relating to the Negro,” which mapped out a system for expanding a library as well as a “systematic gathering of data” relating to the Black experience that encompassed both historical and current events.

After publishing his first few papers and pamphlets on behalf of his new department, Booker T. Washington suggested that Work publish a “yearbook of Negro progress” to honor the 50^th anniversary of emancipation in 1913. The first edition of the Negro Year Book was published as a joint effort by Work and Tuskegee in 1912 at a hefty 225 pages. It sold for 25¢ and was mailed for 5¢ more—which is equivalent to $10 today. The first edition sold 5,000 copies quickly, which provided the necessary funds and enthusiasm to triple the page count and print run by the 1914-15 edition.

The first edition of the Negro Year Book in 1912 set the tone for the series by dividing its content into three sections: an overview of African American life (with supporting data) in 1911, an overview of Black Americans in context to the world’s Black population, and a final section documenting the story of enslavement and emancipation. More than anything, the first edition essentially converted the assorted newspaper snippets and assorted data already collected by Work and his team as a first-of-its-kind resource for collective Black memory.

By the 1914 edition, the Negro Year Book also solicited facts about African Americans as part of a campaign to collect information. This took the form of a contest for the “most practical suggestion” with a hefty prize of $50 (roughly $1,500 today). Collecting information was central to Work’s plan for his department, and by this time he had already been receiving newspaper clippings, quotes, and assorted notes from universities and researchers across the country. By turning this into a contest, Work created a real incentive for laypeople to contribute, and in essence, it helped him to crowdsource his archive.

Author Linda McMurry elaborates on his impact in her book Recorder of the Black Experience: A Biography of Monroe Nathan Work:

While the Negro Year Book was, and still is, a valuable asset to the historian and sociologist, its impact was also significant among laymen. The facts it supplied inspired blacks with confidence in their ability to progress and refuted the rumors of black decline that were widespread among whites. The prestige of Tuskegee Institute lent credence to the facts presented in the yearbooks and allowed them to be distributed through white newspapers and to be accepted in both the North and South. There are many mentions of the Negro Year Book in newspapers across the country. A periodical called The Republic even declared, “The Social, legal, financial, and educational contrasts between the American Negro in 1863 and 1913 are by the very dispassion of their telling made miraculous. The book is written for reference use, yet many successive pages read like romance.”

Work himself considered the Negro Year Book as his most significant accomplishment, saying:

The answering of inquiries about the Negro, which came to Tuskegee from all parts of the world, became an important aspect of the work of the Department of Records and Research. I kept the recipes to all questions received. On the basis of these replies there was published in 1912 the first Negro Year Book, a compilation of facts relating to the Negro. Almost immediately the Negro Year Book became a standard reference on all matters pertaining to the race. Its circulation in the course of time became world-wide.

What follows is a series of examples from various editions of the yearbooks. Every edition focused on education, an area that Work was particularly passionate about. (I’ll explore other topics of interest in forthcoming articles.)

Visualizing educational inequality

Work added a series of charts on education in the 1914-15 edition. By the next edition in 1916-17, the education chapter was elaborated to 58 pages and included the most number of charts. While W.E.B Du Bois created remarkable charts for the 1900 Paris Exposition on the same subject, Work focused on the bigger story of American education and the lack of investment in Black children.  

The series of charts begin with “Per Cent Negro Children In School And Out,” which features horizontally stacked bar charts sorted by the percentage of Black children in school, with Oklahoma at the top with about 62% in school and Louisiana at the bottom with about 28%.

What is immediately evident is the humble way the chart is printed. The chart is straightforward and the design is clean despite being hand-drawn. The chart is arranged in three sections—the group at the top being above 50%, then Texas at 50%, and the rest of the states trailing below 50% (all of them Southern).

Work presented the data in this format originally in the 1914-15 edition but then reworked the design over the next 10 years. As you can see below, his first chart was hand-drawn, while the next two versions are printed as rudimentary bar charts. The last two versions include data on White students, but use tick marks to show those out of school. It’s interesting to consider how Work experimented with the design of this chart over time, yet collected the data in the same way from the beginning.

The second chart in the series is as unique as any that shape our field. In my opinion, it is equally as captivating as Florence Nightingale’s rose and as engaging as Du Bois’s spiral:

This is the 1914-15 version of the chart, and the first time it appears in the Negro Year Book. It is hand-drawn, like most of the charts from this edition, but the measured conception of the chart really packs a punch. “Days Of Schooling Per Year If Each Negro Child Of School Age Got His Share” is a unique design that connects Work’s statistical analysis directly to his argument of unequal investment in education. Each five-spotted dice pattern represents a week in school, each dot a school day.  

Work divides the total number of days attended by the number of children of school age to get the per-capita average for each state. It’s a great way to show the scale of the issue in a way that creates empathy and grabs attention. (On a personal note, can you imagine every African American child in Louisiana only being in school for a month and two days per year?)

Work hid the real surprise in a data table that followed the chart. There, he showed the average number of years that it would take a child to complete an elementary course (grades 1-8). By this accounting, it would take an African American person 33 years to get an elementary school education in South Carolina. 

This chart was recreated twice in the following editions. In 1916-17, the chart was typeset instead of hand-drawn:

While it’s interesting to consider how the impact of the hand-drawn versus printed charts cultivates different emotional responses, the data, itself, is equally stirring. While Louisiana and South Carolina are relatively unchanged, Maryland and North Carolina see significant improvement. Texas initially improves between the editions but then stagnates in the subsequent version.

The 1918-19 edition was the last year this chart was included, and it was oriented vertically on the page with more standardized typesetting. The story in the data is effectively the same with the overall trend in the data flattening out. Louisiana and Georgia see modest gains while South Carolina actually drops to 25 days annually, down from 26 days.

By changing the format and design of this chart, the charm is completely gone. The proximity of the dots doesn’t visually align with the idea of a five-spot dice and the numbers are lost on the page. It’s heartbreaking to see this version because it doesn’t live up to the impact of the previous versions. 

It’s uncertain what exactly changed, was this a different printer? Why did the orientation become vertical? Why did they use asterisks instead of dots? Regardless, it’s an interesting exercise in design exploration. It’s clear that Monroe Work could visualize data to make an efficient and compelling argument, but access to funding and technology likely forced him to focus his efforts elsewhere.

The next chart in the series compares investment in White versus Black schools.

The chart is sorted in descending order by White investment and the bars themselves appear to be made from a kind of tape in the printing process. As you’ll see this technique is used across many of Work’s charts and I assume this is due to how the book was printed, likely using an offset lithography process—the standard at the time. 

While the story in the data is clear, the chart could be made more self-evident. As I show in this chart based on the original data, if Work would have removed some of the states where investment was more equitable, then the scale of unequal investment in Black and White schools would be more obvious. But this was not his intention, as Work was interested in presenting as much data as possible to challenge popular opinion and erode misconceptions.

Work’s factual approach furthered his book’s reach significantly by making it less controversial for White publications and schools to cite. The Virginia Vicksburg Herald published a feature article calling the 1915-16 edition “helpful and inspiring almost beyond measure,” while the Denver Star called Work an “Historian who knows his business.” The Colorado Statesman even ran a front-page essay about the overall inspiration of his work concluding with thanks to Work for “this timely message to our people… for the benefit of making them firmer in the cause that concerns them and is of the greatest importance in their lives.” Because Work collected and presented the data without emotion, it gave visibility to the facts at a time when prejudice could easily have omitted them. (See here for my collection of reporting on Work and the Negro Year Book.)

Interestingly enough, the previous edition of the Negro Year Book in 1914-15 featured a novel and very different approach to this data:

I’ve mentioned the humble printing methods of the book mainly because they stand out for their ingenuity despite an obvious lack of resources. The sophistication of the story of Work’s data is nuanced and clever; its hand-made nature appeals to us as dataviz practitioners because we can see the hand behind the analysis.  

This chart is essentially a unit chart of “$” signs struck many times with a typewriter to create a crude icon for “dollar.” These units are arranged in rows with the corresponding amount at the end of each row. We see the values for White and Black schools with the most per capita spent in Washington, DC. For Mississippi, we are left with only 93¢—not even enough for a single icon.

Again, we see Work experimenting with the format of the chart to make a point. One certainly wonders if Work had seen Du Bois’s Paris Exhibition charts, or if he was informed by other charts used in sociology. There are references to additional charts by Work and his department in an exhibition at the Georgia State Fair in 1908 and also in some of his earlier pamphlets. Further research is needed to help them come to light.

While the publication was expanding between the editions—from 228 pages to 488 in just three years—Work emphasized collecting more facts. Following each of Work’s charts is a table of the data. Interestingly, there isn’t a chart on this data on the number of teachers despite the inclusion in the paragraph that introduces the chapter on education. It is fascinating all the same.

In order to highlight access to education, Work focuses the next two charts and a map to explore Illiteracy:

Work shows that African American illiteracy in 1915-16 was clearly connected to age and geography. The data is stark. The depth of illiteracy in the Southern states presages the impending Northward Black Migration for better living conditions.  

The next chart shows the total scale of education by attainment for African Americans. It’s a chart that Work continues to publish even after the rest of the charts fall away in later editions because I believe it shows the urgency of Black scholarship. 

The remaining suite of three charts round out the yearbook’s education chapter and are included and updated from the second edition of 1913-14 through the eighth edition of 1931-32 in similar formats. All are created out of the same “tape” to create the bar charts.

In these charts, Work explains the finance of Black education. These numbers would have signified vast amounts of money at the time. Collecting and quantifying the sum total of Black education in America shows the scale of the inequity in a way that few other metrics can. While the African American population is an ethnic minority in the United States, the data proves how little had been invested at a time when the need for education would have been imperative. By compiling the total costs for all education, then breaking out the funding opportunities available, Work not only points the way for other Black educators to get access to funds (which he lists in detail at the end of the chapter) but also brings his story full circle in detailing the massive investment by African Americans directly into their community schools.

Setting the record (straight)

At the beginning of his career in sociology, at the age of 37, Monroe N. Work began to define what would be his life’s work: “If Sociology has primarily to do with human beings in their associative capabilities, then its primary function is thorough investigation and research, to collect a body of information that will point out, make clear, what these relationships are and what in the present, the now, should be done in order that these relationships may be made more harmonious, more just and proper.”

Work’s collaborator and friend Jessie P. Guzman noted in writing his obituary:

Work biographer Linda McMurry writes, “The principle driving force in Work’s life was neither accommodation nor protest, rather it was an abiding faith in the ‘impact of fact’. His main concern was to obtain the best possible outlet for the fruits of his research.”  A few chapters later, she adds, “With his faith in the impact of fact and his uncharismatic demeanor, Work’s quiet, scholarly presentations were in keeping not only with Tuskegee’s program but also with his own personality… Indeed, throughout his almost 40 years at Tuskegee, Monroe Work was a quiet but insistent voice for change in the institute’s approach to both education and race relations.”

Through a steadfast belief in facts, Monroe N. Work not only established the structure for how data on African Americans was collected, but he also invested his life in presenting it to the world at large. There is much in Work’s life to share; in a follow-up to this story, I’ll present a body of charts that he created to effect dramatic improvements in African American health conditions. 

Special thanks

Monroe Work first came to my attention by Dr. David H Jackson Jr., Provost of North Carolina Central University. It was his guidance to explore the Negro Year Book which has continued my exploration into Black scholarship. I believe that data visualization may have played a significant role for many activists and scholars throughout the beginning of the twentieth century, the civil rights movement among them.

Thanks very much to Emily Barone for editing!

You can find every image from this article as well as all editions of the Negro Year Book here in this public Google folder containing all my research materials:

While Monroe Work is a remembered figure in African American history, his life has not received much documentation. There is only one book about his life and work, Recorder of the Black Experience: A Biography of Monroe Nathan Work by Linda O. McMurry from 1985. Her care and deep empathy for Work’s life does his legacy a great service.

Prior to McMurry’s biography, an accounting of Work’s life was compiled in 1949 as an in-depth obituary by Jessie P. Guzman, who collaborated with Work from 1938-44 when she took over for him after his retirement at the Tuskegee Institute Department of Records and Research.  She went on to compile and edit two subsequent volumes of the Negro Year Book in 1947 and 1952. 

There is also a lengthy article, “You Can’t Argue with Facts: Monroe Nathan Work as Information Officer, Editor, and Bibliographer,” by Mark Tucker published in 1991. While he focuses on an enormous bibliography that Work assembled later in his career, it is a great summation of his work and contains additional research and scholarship.

Additional links

Monroe Work portrait in National portrait gallery: https://npg.si.edu/object/npg_S_NPG.67.28

Site created highlighting Work’s documentation of Lynching: https://plaintalkhistory.com/monroeandflorencework/

Tuskegee archives: http://archive.tuskegee.edu/repository/digital-collection/the-negro-yearbook/

The post Pioneer in Black Data: Monroe N. Work and the Negro Year Book appeared first on Nightingale.

But before I get into it – let’s hear it for Mary Aviles!

I’d like to take a moment to give a heartfelt thank you to our former Managing Editor, Mary Aviles, who is leaving us to focus on her next chapter in work and life. To say that she has been an inspiration to us all is an understatement. 

Mary has an amazing capacity for strategic high-level thinking as well as rolling up her sleeves to get the work done. As an editor, Mary has supported hundreds of authors of all skill levels over the last two years, and I have personally seen her nurture and guide them in a selfless way. I have been one of those writers as well, and I really enjoyed collaborating with her as an editor—she poked holes in weak statements, cleaned up details, and urged me to make my work more interesting, easier to read, and just better overall. 

Nightingale – and the Data Visualization Society as a whole – is indebted to Mary for all that she has done to sustain our operations and encourage us to set our goals higher. She has been a champion of our focus on community, inclusion, diversity, and social engagement. She has driven multiple initiatives behind the scenes to support emerging voices and has worked to see individuals recognized for their contributions. 

Mary helped us think of Nightingale as a community celebration—and, indeed, Issue 2 is bursting with ideas from and by our community. While we are excited to see what Mary gets up to next, we also consider Issue 2 to be a perfect manifestation of her impact on our team and community.

OK let’s get into it!

This issue is all about inspiration. It started with a conversation with our editorial committee a few weeks after Issue 1 came out. As you can see below, the committee shared MANY ideas; it’s interesting to look back and see what became reality, like the idea of inspiring climate action and a survey asking readers how they are inspired. Here’s our slide showing the sticky note brainstorm:

Indeed, the committee all agreed that we needed to start the issue with a survey to try to learn more about the who, what, when, where, and why of inspiration. We sent out a quick survey and received about 50 responses from people all around the world. Here’s a look at the spreadsheet:

After some clean-up, our Creative Director Julie Brunet (aka datacitron) took the text and data and had some fun with it. The issue opens with a few pages of some of the most exciting and non-traditional dataviz we’ve seen, as she turns survey responses into “scientific diagrams.” 

This article culminates in an especially brilliant diagram charting the course of inspiration itself. Maybe we can get Julie to write a whole article about it because it’s so clever!

Here’s the Table of Contents for Issue 2. I hope it will whet your appetite not only for the names and concepts listed but for the types of articles contained inside:

In many ways, Issue 2 builds on what we believe were the strengths of the first issue. The astute reader will see a few more in-depth articles with a longer page count. Our editorial team also commissioned more original articles for Issue 2, in addition to reworking some digital content from our archives, and commissioned custom illustrations. Be sure to check out some of my personal favorites, the articles by Pilar Dibujito, Matthew Brehmer, Gary Wolf, Andy Kirk, Katie McCurdy, Joseph Mackereth, Andy Cotgreave, and Dee Williams & Rahul Bhargava. Ok, I guess I mean, just read them all! Haha

Our content editor, Claire Santoro, really outdid herself in exploring the inspirational qualities of Ed Hawkins’ Warming Stripes visualization, not only in evaluating the inspirational impact his work has had, but also in interviewing two practitioners who played key roles in the Warming Stripes’ origin story—Ellie Highwood, who created a Warming Stripes baby blanket, and Joan Sheldon, who created a Globally Warm scarf. It’s an amazing story of how inspiration fuels more inspiration, generating more positive energy for more people. If that weren’t enough—Chesca Kirkland created an amazing infographic charting the uses of the Warming Stripes and their continued influence. We’re so proud of this team for bringing you a story that you could only really find here in Nightingale!

Issue 2 also saw an extension of our Career Tooltips section, with a great article by Elijah Meeks (“Turn that No Into A Yes”) that I’m sure almost anyone working in dataviz will enjoy, commiserate with, and gain insights from. One of our former editors, Raeedah Wahid, shares some of her experience for early career folks in “7 Tips for Starting Out in Data Journalism.” We also featured a number of the career journey maps highlighted in the webinar/interview series Careers in Dataviz hosted by Elijah Meeks throughout 2022.

There are two excellent dataviz challenges in Issue 2—this time including the amazing submissions from Issue 1! Jeremy Singer-Vine continues his Data Is Plural challenge with a fun take on Bob Ross, while our friend Alli Torban takes the reins to host this issue’s Dear Nightingale challenge. As a fun bonus, we convinced the original Dear Nightingale hosts Stefanie Posavec and Giorgia Lupi to provide some commentary on the Issue 1 submissions. 

But wait! There’s more!

This issue includes not 1… but 2 supplements! Since we heard so many enthusiastic comments from our readers on The Kids’ Table ‘zine from Issue 1, we decided to push it a bit further—this time it’s an activity poster! PLEASE send in your completed projects—we’d love to share some in Issue 3!

We are also excited to present “Unimaginable Death: Visualizations of COVID-19 Pandemic Milestones,” a special 40-page booklet by Paul Kahn, Liuhuaying Yang, and Hugh Dubberly. This unique project explores the ways data journalism teams visualized massive quantities through different chart types and approaches from around the world. If you’d like to learn more, I interviewed the authors here. We are thrilled to present this supplement in collaboration with Northeastern University and are proud to support such important research from across the dataviz community.

Since our theme for Issue 2 is inspiration, we really tried hard to pack in as many inspiring data visualizations as we could. Richard Brath shows off plenty of inspiring approaches to incorporating text in his article, while our creative director Julie Brunet has fun with some twisted takes on Agatha Christie book covers.

There’s just too much to cover here, and guess what – since Issue 2 is sold out already – here’s your annual reminder that all paying DVS members can find the digital copy of the magazine in the Member Resources area of the Data Visualization Society website. 

Super-mega thanks to Claire Santoro, Mary Aviles, Julie Brunet, our editorial committee, the DVS board of directors for their continued support, and of course YOU—the data visualization community, for being loyal Nightingale readers and contributors! 

Want to share your thoughts on Issue 2 or suggestions for Issue 3? Drop us a line – get in touch, send us your ideas, get involved!

The post A look inside Nightingale Magazine Issue 2! appeared first on Nightingale.

But it turns out that isotype charts were prevalent in US government documents in the 1930s and 1940s. If you look for them, you can find isotypes sprinkled all over the US during this time — they just weren’t made by Otto or Marie Neurath. No, the growth and popularity of pictorial statistics in the USA are thanks to a different under-recognized figure in design history: Dr. Rudolf Modley.

Born in Vienna, Rudolf Modley was involved as a student volunteer in the earliest days of Neurath’s Museum of Society and Economy. After years of service, Modley eventually moved to the USA to serve as Neurath’s proxy at the Museum of Science and Industry in Chicago, but had ideas of his own and began designing isotype charts by himself.

A young man on his own in America, Modley’s life then follows the path of many first-generation immigrants — he saw an opportunity and worked hard to take advantage of it. The rise of the New Deal in the early 1930s saw many government agencies looking to pictorial statistics to visually communicate their plans to an eager American population. Of course, the officials in the US government looked to Rudolf Modley’s company conveniently named “Pictorial Statistics, Inc” to do so.

He worked tirelessly to bring pictorial statistics to the common American as a new method to understand the increasingly scientific world around them. It only makes sense that he took this new form of communication to the biggest mass media at the time: newspapers.

“Makes The Reading of Statistical Information A Pleasure”

Modley’s star was on the rise as a result of his work with the US government. A February 1938 issue of The New Yorker indirectly announced Modley’s plans when they wrote: “He and his staff will take on any sort of research of graphs [with] Telefact, a feature which he is preparing for newspaper syndication.”

Later, in his 1952 book Pictographs and Graphs, Modley writes about charting in newspapers: “Another difficulty in charting for newspapers is the speed with which the charts must be prepared. The research and finished artwork must be done in a day or two, which puts tailor-made charts beyond the reach of many newspapers. For this reason, several methods for making timely charts available in syndicated form have been tried. As early as 1937, Telefact, a graphic syndicate, made its appearance. Its charts dealt with general social and economic subjects, and, during World War II, with information pertaining to the war. Designed to be used over a period of time, they were topical without following the latest news as a newspaper would.”

Telefact was syndicated widely across the USA, and featured in newspapers in Minnesota, South Carolina, Virginia, Arizona, Utah, and many others. As you can see on the left, they were inserted wherever space allowed, and visually competed for the reader’s attention with advertisements and headlines.

In his 1938 article “Pictographs Today and Tomorrow”, Modley says: “… another effort has been made to have pictographs penetrate even into remote areas by means of a syndicated newspaper feature called Telefact, which presents a fact of social or economic importance each day… The pictograph technique opens up new possibilities of influencing and shaping public opinion. It makes possible the presentation of factual material in simple terms and to an audience which is much larger than any yet reached by factual information.”

A Treasure Trove of Charts

There are quite a few Isotype practitioners that have been overlooked but Rudolf Modley is the most known among them with a surprisingly large body of work that is very poorly documented.

Imagine my surprise when searching newspapers.com to find not 2–3 mentions of Telefact, but over a thousand. So far I have manually collected more than 480 charts from daily newspapers with double this amount created from 1938–1945.

The scale of the find is what is so surprising. With so many charts to scan through, we see so many design ideas explored by Modley and his staff. Not only can we see how different subjects are presented using this charting method, but we also can see how the design templates of Isotype have been applied to various types of data.

This laboratory of pictorial statistics feels different from most Isotype examples that we know from books and exhibitions. While many Isotypes are part of a broad study or education focus, Telefacts were designed to be immediate and independent. The concepts that are communicated in Telefacts are naturally interesting but independent of any larger story, acting like bite-sized snippets of economic trivia.

This was exactly the point. It is easy to see Modley’s agenda in the marketing of Telefact as well as in the charts themselves. This project was designed to help people learn the facts — but more than that — to help common people leverage the world of science and statistics in their normal lives.

Of course, Modley was also just as interested as exposing them to Pictorial Statistics as well. He writes in his 1937 book How to Use Pictorial Statistics:

“Numerous textbooks on subjects as varied as history, geography, and biology have been extensively illustrated by pictograph technique. While it is thus assured that the coming generation will be used to the method, another effort has been made to have pictographs penetrate even into remote areas by means of a syndicated newspaper feature called Telefact, which presents a fact of social or economic importance each day.”

How To Present Hundreds of Telefacts Charts When We Can Barely Focus On One?

I found myself wondering what to do with all this content. While I find the charts compelling, I also find the act of scanning through them equally interesting. But I kept wondering how I could help other people have a similar experience? Our dwindling attention span is hard to navigate, so I kept asking myself what was the best way to get this work out into the public and allow them to learn more from it?

I decided to create a Tumblr to share my work for a few reasons. First, Tumblr allows for a very intuitive experience where the user can see the charts as a group and also as individuals. Each image is meta tagged, so these charts will now be searchable among the vast quantity of SEO optimized images on Tumblr. But most importantly, these images will now be indexed by Google, so they will be publically available and accessible.

Please check it out: https://modley-telefact-1939-1945.tumblr.com

Looking At A Few Telefact Charts

There’s a lot to love in these charts. Not only do we see the progenitors of the everyday infographic that we see in our newspapers and magazines today, but also a snapshot of what life was like in the late 30s and into the WW2 era. Let’s take a look at a few interesting charts:

Many charts use standard isotype methods to visually display statistics but also reduce them to a more immediate understanding of the content. This chart uses simplified ‘guide pictures’ to indicate the split between farmers and non-farmers. The chart compares the urban and rural incomes with the corresponding number of children in each.

This telefact chart contains very little text but breaks one of Neurath’s main rules by including the numbers along with the pictorial representation of the number. This is a significant divergence from the Isotype practice, but at the same time adds a significant layer of meaning needed to communicate to a general audience. The basic numbers presented are simple and give substance to the charts.

As for the chart, it is interesting to see just how much water there is in an average egg. The ‘waves’ of water are visually interesting as they are used in a sort of horizontal stacked bar-chart, with the solid, non-water segment showing the rest of the percentage. The use of the wave to denote water is an interesting exercise in symbol abstraction, as the quantity of water in a stick of butter does not map to our understanding of an ocean. Somehow it still works.

Of note is that each Telefact has the month and day included in the right corner to show exactly when it was to be published. Unfortunately, they do not include the year.

The ‘unit arrow’ diagram on the left is another isotype chart type popularized by the Neurath’s and was used throughout their careers. It’s clear that Modley regularly kept a close eye on their work and, for better or worse, continued to build on their ideas.

In the chart on the left, each arrow represents the millions of dollars in exports and easily shows exactly where the US sells its goods. In order to squeeze so much information into such a small area, Modley creates a massive simplification of the map shapes in a semi-geometric fashion. Modley experiments with this kind of geometric simplification throughout this period of his work.

Otto and Marie Neurath occasionally used the power of analogy to focus the audience to consider data in a certain way as best used in the chart “Only an Ocean In Between”. In the chart on the upper right, however, Modley uses a similar idea to compare the wind velocity for five major cities as the distance it takes to blow the leaves off a tree. The power of analogy is particularly very strong, and while Modley didn’t use this technique often, it is especially powerful in the right context.

In terms of subject matter, Modley knew salacious subjects would help to attract attention. This chart of fatal accidents — complete with almost 30 little dead guys — used repetition to grab attention and a basic title to seal the deal.

In the end, this is ultimately a conventional pictograph, with little Isotype influence. That’s not inherently a bad thing, as an overview of the hundreds of Telefacts shows a huge quantity of these types of pictographs interspersed between more complex charts and diagrams.

Pictographs are easy to understand and require less visual sophistication by the audience. By using so many basic pictographs it shows a willingness by Modley and his team to focus on the data and not over-do it. Certainly creating these charts each week/day was a huge amount of work, so standardizing the process for creating the charts was just as important as knowing when to move on to the next one.

This map is again very much in the mode of what you’d see created by the Isotype team. The chart’s learning objective is to compare population density with recreational areas. While well-intentioned, it is unfortunately not a very successful chart as it is too crowded and not well labeled.

In collecting so many Telefact charts it also becomes an important opportunity to learn from Modley’s failures. Understanding when a chart is successful, and when it is not, helps us understand how to bring these ideas into our own practice. In the chart above, Modley tried to cram too much into too small an area creating a messy design that is hard to visually read.

I’d invite everyone to interrogate the design of each of these charts for the same purpose of learning what works and what does not.

We can also study these charts to see exactly how Modley’s efforts began to diverge their Isotype origins. Both charts above are quite different from what the Neuraths were designing yet are still sophisticated designs.

The chart on the left, “How Many People One Farmer’s Work Feeds”, speaks about the benefits of modernization and mechanization, the changes in American culture over a century, and hints at the role of agriculture in international trade. On the bottom row, Modley literally draws a blank as to what the future holds. This is a provocation; not a projection into the future, but a dare to the audience to learn more in order to shape it. By crafting a design that focuses on the possibilities, it departs from the normal isotype objective of illustrating the statistical ‘known’.

The “S” design of the chart on the right is typical of some other charts that Modley invented. It was created as a way to display a process happening over time but visually compressed into a small space. Modley uses a similar design in a book called the “New York Primer” which came out the year before, so it is a design concept he was exploring at the time.

Exploring these design concepts is important as it shows that contemporary information designers can (and should!) continue to explore new design patterns based on Isotype to further their communication needs. Rudolf Modley’s team was constantly experimenting, just as his earlier text has said, by “open[ing] up new possibilities of influencing and shaping public opinion.” This also proves that Modley wasn’t “merely” an imitator, but a practitioner on his own merit.

These two Telefacts also show how Modley was experimenting with graphic design concepts as well. The chart to the top left, “Salaries Above $300,000” rides the line between data visualization and design sloganeering. It shows another reoccurring subject of Telefact charts, the breakdown of jobs in Hollywood. The chart’s label assuming a graphic that drives the appeal of the whole design.

“For Every Job 500 Applicants” to the top right shows an entirely different illustration of a 1-out-of-500 statistic. Each dot is a person that is restrained behind a barrier while a solitary silhouette strolls up to the studio entrance. Black is used to grab the audience’s eye while a sort of isometric view provides the compositional structure.

Both of these Telefacts would easily attract attention in a crowded newspaper layout. By studying these works we see other ideas in exploring the aesthetic possibilities in Isotype designs, opening us to the possibilities beyond Neurath, Arntz, and Modley.

The Importance of World War II to Telefact

WWII was extremely important for the Telefact series as quoted by Modley at the beginning of this article. The war provided loads of information that would have been very interesting to Americans. Modley’s Telefact charts were there to explain details of the mechanisms and fighting techniques; feeding a public hungry for news of loved ones serving in the war.

While many Telefacts were still providing pictorial statistics, diagrams helped to decode the complexity of the new technology of war as well as help explain how Americans could contribute in their own ways. Certainly, we see a rise in cross-section diagrams that help explain the components of everything from bombs to bombers, and houses to air-raid shelters. The diagrams spanned a variety of styles, likely created by different artists on Modley’s team.

What’s interesting is that this move to diagrammatic information design occurs before Marrie Neurath’s books for children. It’s interesting to consider that both designers began a more illustrational approach to designing information, rather than just statistics, after the war. While Marie Neurath certainly had much more of a focus on creating charts and illustrations for books after her emigration to England in 1941, the Isotype team had a split focus on exhibition design and institutional education during the 1930s. Otto Neurath’s focus on ‘learning through the eye’ was certainly a reality by the post-war period, with many primary books lavishly illustrated by the time. Perhaps the importance of statistical education took a more general back seat to more qualitative information design as the world refocused on an optimistic future.

The Grunge of News: Aesthetics & Business

I also want to take a moment to celebrate the beauty of the grungy ‘realness’ of newsprint. Cheap paper, bad lithographs, ink slip, punctured and torn paper, and plain old dirt are all present in these reproductions.

The process of creating syndicated news graphics would have been purely physical at the time. Images would have been drawn by hand or reproduced photographically. The text might have been set by hand or optically produced likely about 4x larger than printed. The reproductions would have been mailed weekly to papers around the country allowing each to then prepare for local printing. It is all gloriously messy, made even more so by the digitization process of scanning, adjusting for contrast, and sharpening the rough edges of the not-so-sharp image.

We could also consider the Telefact a kind of “science comic” as the method of creation and distribution would have largely been the same for both. Telefacts were distributed by Science Service, a newspaper syndicate begun by two journalists dedicated to “pioneering the dissemination of accurate, accessible, and engaging news of science to the public primarily through the mainstream media through its syndicate service.” We can easily consider Telefact to be their version of the funny papers.

Modley was sincere in his quest to get pictorial statistics into modern practice, and in 1943 he published his first collection of over 1,000 icons. The book Pictorial Symbols collected his pre-made icons as seen throughout the Telefact series in order to equip those who wanted to make their own charts. Prices for charts, icons, and custom icons were very reasonable, with your custom selection of icons priced at $.05 cents for the first 50 icons, then $.01 cent each after that (50 icons would cost the equivalent of $30 USD today)

Turning the book Pictorial Symbols over reveals a full-page ad for Telefact on the back cover. Nested at the bottom is the announcement of the acquisition of the Telefact series by McClure Newspaper Syndicate, which ultimately spelled the beginning of the end.

McClure Newspaper Syndicate would have been one of the largest companies in the business, distributing 10,000 features with combined sales of $100 million a year. Lasting more than a century in business, McClure was one of the biggest distributors of comics, bringing everything from Rube Goldberg to Batman and Robin to thousands of papers every day. That kind of reach, with those kinds of resources, would have been attractive to Modely. While it is clear that Modley and his team continued their involvement with the series for at least two more years, we see less pictorial statistics and more traditional charts begin to appear in the series in 1945.

For those familiar with Isotype, the chart at the above right, “Expected Cut In War Production Program,” would have literally been the antithesis of Neurath’s teachings. The very invention of the Isotype concept was in opposition to the scaling of icons to show their quantity. It’s clear that by this point Modley was likely not present in the creation of the Telefact series as he would never have supported such a chart. That same chart also illustrates the end of the war, which ended up being a significant complication as well.

The syndication of newspaper content had been booming since the turn of the century, but as the war began its last year, many newspapers cut back their pages to contribute towards war rations. After the war, the rise of televisions provided a new challenge that reduced newspaper sales further. In my research, I could not find any Telefact charts after 1945.

Rudolf Modley continued to work in pictorial statistics for many years afterward. He published a number of books about graphic communication and also collaborated with historians to explain American and European economic history.

Communication With All People Everywhere

As the post-War world embraced multi-national collaboration and standardization, Modley found himself collaborating with celebrity cultural anthropologist Margaret Mead. Their 1968 essay Communication Among All People Everywhere in Natural History Magazine outlines their shared interest in cataloging all manner of icons and methods of visual communication as an emerging Lingua franca.

As Modley had been documenting icons for decades already, another famous designer, Henry Dreyfuss, also sought his assistance in compiling icons for a similar project. In an act of cosmic completeness, in the late 1950s, Modley enlisted Marie Neurath to help collect a wide survey of icons from many industries. The two collaborated for years sending icons and letters discussing their work. Dreyfuss published his Symbol Sourcebook: An Authoritative Guide to International Graphic Symbols in 1972 to widespread acclaim. It’s an incredible book, unique in many ways, and includes an introduction by Buckminster Fuller, as well as a 2-page spread introducing Isotype written by Marie Neurath.

Collecting icons must have fulfilled Modley, who continued to work on his own Handbook of Pictorial Symbols right up until his death in 1976. The earlier collection in his Pictorial Symbols book from 1942 was expanded with the help of Gerd Arntz and extended to include over 3,250 wayfinding and Olympic icons. It’s an awesome resource issued by the low-cost Dover Publications (which is still readily available used for under $5).

The book begins with an essay by Modley called “Introduction to Graphic Symbols”. It seems only fitting to finish this essay by noting the reverence he held for Otto Neurath as evidenced in his writing. There it is, right on the first page, “The modern techniques of graphic presentation of facts and figures were developed by Otto Neurath in the early 1920s in Vienna. If you learn these techniques, you too can use graphic symbols to set forth complex facts in simplified, more easily understood and more easily remembered form.”

Modley, at the end of his life, performs the ultimate pivot, from Otto Neurath to you. Practicing this kind of isotype/pictorial statistics was just as possible then as it is today. Rudolf Modley’s mission as pictorial statistics teacher and Neurath evangelist rings true to the very end.

There are so many examples of isotype and pictorial statistics to take inspiration from. What better place to start your learning journey than by scanning through several hundred Telefact charts?

Go find them at: https://modley-telefact-1939-1945.tumblr.com

Thanks as always to: Georges Hattab, Alyssa Bell, and  RJ Andrews for their editorial help and support.

Several of the essays cited in the above article have also been recovered from original sources:

Pictographs Today and Tomorrow, Rudolf Modley, Public Opinion Quarterly, 1938

Communication Among All People Everywhere, Margaret Meade and Rudolf Modley, Nature Magazine, 1968

Handbook of Pictorial Symbols introduction, Rudolf Modley, 1976

The author is especially indebted to the work of Hisayasu Ihara for their pioneering research on Rudolf Modley. If you are interested, I’d urge you to read more:

Rigor and Relevance in the International Picture Language, Rudolf Modley’s Criticism against Otto Neurath and his Activity in the Context of the Rise of the “Americanization of Neurath method”, Hisayasu Ihara, 2009

This article comes as part of a series on Isotype and derives mainly from research on the Isotype design process created by Otto and Marie Neurath with Gerd Arntz. My goal is to teach people about the techniques and mindset of this data-driven design team, in order to inspire new information design concepts today.

The post The Telefacts of Life: Rudolf Modley’s Isotypes in American Newspapers 1938–1945 appeared first on Nightingale.

How To Solve It is not just a book about math but is actually a book about how to solve problems – any problems – and it became the foundation for the field of heuristics. Based on Polya’s prodigious knowledge and historic research, the book has a curious structure, with several introductions to the book, an outline of his four-step framework for solving problems, and then a dictionary of heuristics: which is actually an explanation of 67 different concepts to aid in solving problems.

For a math book, its is a joy to read. Polya’s voice is funny, knowing, and sincere. You can tell he spent years in the classroom coaching students of all kinds, and while the book is absolutely about math, it is equally universal, as he said In the late 1960s: “I believe the most important part of thinking that is developed in mathematics is the right attitude in tackling and solving problems. We have problems in everyday life. We have problems in science. We have problems in politics. We have problems everywhere. The right attitude for problem solving may be slightly different from one domain to another, but we have only one head, and therefore it is natural that there should be one general set of tactics to tackling all kinds of problems. My personal opinion is that the main point in mathematics teaching is to develop these tactics of problem solving.”

For the majority of the book Polya teaches his process by solving geometry examples or finding patterns in number sequences but often switches it up using non-math examples applying the same heuristic for solving a crossword puzzle or understanding ancient Egyptian engineering. It’s here that How to Solve It really comes into its own, as Polya’s method results in a series of questions that can be applied to almost any problem.

After reading (and re-reading) How to Solve It, I think Polya’s heuristics can absolutely help our thinking in unraveling problems in data visualization. This (rather long) article is split into 4 parts. I present my analysis of the book before an explanation of the concepts because I didn’t want to get lost in too many details – and there are a lot of them. Part II focuses on the book and process itself, Part III on it’s legacy, and Part IV is a very long annotated list of the 64 heuristics terms, concepts, and key protagonists.

Before I get into the details, here’s the whole book – read it for yourself! https://notendur.hi.is/hei2/teaching/Polya_HowToSolveIt.pdf

Part I: Analysis of Polya’s heuristic methodology

Polya’s process versus design thinking

I’ve been reading How to Solve It as a supplement to guiding the design process. While math and data are connected, mathematics and design have hardly been cozy. It’s interesting to consider Polya’s process of solving mathematical problems in comparison to design thinking. 

How to Solve It was written and published in Zurich in 1940 before the first English language version was published in 1945. Design thinking evolved in the 1960s as an approach to exploring production issues in industrial design, but both arguably drew from the same well of the scientific method with similar outcomes.  

Polya’s method takes us inside the creative process by establishing a series of questions to understand the problem – which, for our needs, I think of as the communications objective. Polya puts more emphasis on the earliest phase of the discovery process, focusing on learning, research, and understanding the goal (and data) before the mechanics of doing the work and testing for accuracy.

The discovery phase in design thinking has evolved into a loosely collected series of design exercises by whole industries of practitioners to discover the right solution to the right problem. These exercises largely crowd-source the research and problem solving which inevitably results in a degree of groupthink as an outcome. The actual creative process may be supported through these kinds of design thinking exercises, but it’s just not the same thing. Hopefully the iterative process catches any group-think indulgences through prototyping and testing the result with users – or at least it should.

It’s certainly arguable that Polya’s heuristics may not be as comparable to the wide range of “wicked problems” that design thinking is usually used for, but certainly, the same kind of mindset is at play.  Where design thinking is largely used to understand complex problems, Polya is codifying the types of internal questions that manifest through the creative process itself. 

For example, the Double Diamond approach is a standard way of thinking about the 4 phases of design thinking. The diamond represents a series of diverging and converging activities to complete a project or task. After playing around with it for a while, I’ve come to think of Polya’s process as more of a staircase up through the Problem phases of the Double Diamond approach while the second and third steps – devising a plan & carrying out a plan – are 2 sides of the same coin, as the planning of the act and the follow-through result in the 4th step – Checking/Looking back. Stating it more colloquially, once you understood the problem, and figured out to solve it, it’s all downhill from there.

What does it all mean? Well, I think Polya’s method could be more effective for small groups or individuals as it takes less input from others and establishes a framework to translate the data and design to known patterns and semiotics of visual culture. 

How might Polya’s heuristic process fit into a dataviz methodology? 

Data visualization is light on process and I loved reading this book in search of clues to how we might use these heuristics to make our work better, more intuitive, or more creative. Polya again makes an apt comment: “Mathematics is interesting in so far as it occupies our reasoning and inventive powers… To make such steps comprehensible by suitable remarks or by carefully chosen questions and suggestions takes a lot of time and effort; but it may be worthwhile.” 

Part II: Polya’s method

Ok, let’s dig into the actual book. Here’s an outline directly from the beginning of the book. 

Here’s a summarized text version (which I tweaked a smidge):

 1. Understand the Problem 

1. What do you know?
2. What don’t you know?
3. Draw a diagram of the problem and notate the various parts.

2. Devise a Plan

1. What associations does the problem remind you of? What kinds of similar problems can you think of?
2. Can you decompose the problem or think of it differently?
3. Can you add auxiliary information to make sense of the problem?
4. If you can’t solve the problem, can you solve a related problem? (My favorite!)

3. Carry Out the Plan

(Literally, do the math.)

4. Look Back

• Can you check the result? 
• Does your answer make sense?
• Can you use your solution for something else?

After this, Polya spends the rest of the book walking through the 67 heuristics. I’ll post an annotated list at the end (in part IV), but there are many wonderful concepts nested between the math-solving approaches.

In addition, Polya also explains the nature of heuristics:

The aim of heuristic[s] is to study the methods and rules of discovery and invention… 

as well as heuristic reasoning:

…not regarded as final and strict but as provisional, …whose purpose is to discover the solution of the present problem. 

Throughout the book, Polya’s method is used over and over again as a question-and-answer format to reveal the structured nature of the process. While this may feel obvious for some, the rigor of the approach is refreshing as it gives voice to the natural process many of us lean on to help us untangle the messy world around us. Understanding the power of analogy, research, composition and decomposition, validation, and even chance events is somehow reassuring as defined.

PART III: The legacy of How to Solve It

Teaching as a way of living

In the late 1960s, Polya was speaking on his process to a class of elementary school math teachers. His comments were delivered with the same precise and clever voice found in the book. He speaks a lot about how kids can be mentally engaged with math through practice and then passes along a list of ideas for teachers to do the same: 

Teaching is not a science; it is an art. If teaching were a science there would be a best way of teaching and everyone would have to teach like that. Since teaching is not a science, there is great latitude and much possibility for personal differences. In an old British manual there was the following sentence, “Whatever the subject, what the teacher really teaches is himself.” 

This really resonates with me, so I’ve adjusted it substituting the word ‘teaching’ for ‘dataviz:’

[Dataviz] is not a science; it is an art. If [dataviz] were a science there would be a best way of teaching and everyone would have to [practice] like that. Since [dataviz] is not a science, there is great latitude and much possibility for personal differences. In an old British manual there was the following sentence, “Whatever the subject, what the [practitioner] really [designs] is [themselves].” 

Polya also lists out some general advice, and again, I think this is directly relevant for us as dataviz practitioners: 

• Be interested in your subject.
• Know your subject.
• Know about the ways of learning: The best way to learn anything is to discover it by yourself.
• Try to read the faces of your students, try to see their expectations and difficulties, put yourself in their place.
• Give them not only information, but ‘know-how,’ attitudes of mind, the habit of methodical work.
• Look out for such features of the problem at hand as may be useful in solving the problems to come — try to disclose the general pattern that lies behind the present concrete situation.
• Do not give away your whole secret at once – let the students guess before you tell it – let them find out by themselves as much as is feasible.
• Suggest it, do not force it down their throats.

What we design, what we share with others, is rarely the subject matter of our design and insights, but rather a reflection of who we are as people. Polya’s humanism radiates with the care of a teacher’s fondness for the future. As Polya says, “Teaching to solve problems is education of the will,” and I wonder if that isn’t a life lesson for all of us.

How to Solve It: Modern Heuristics

The influence of Polya’s book has rippled across fields of study.  Heuristics is now a practice that can be seen across many fields – chiefly in cognitive psychology, philosophy, economics, and computer science/AI research. 

Zbigniew Michalewicz and David B. Fogel’s well-known book on modern heuristics for algorithm design even nabs the title from Polya. Their book, How to Solve It: Modern Heuristics begins, “Gyorgy Polya’s How to Solve It stands as one of the most important contributions to the problem-solving literature in the twentieth century. Even now, as we move into the new millennium, the book continues to be a favorite among teachers and students for its instructive heuristics…  Essentially, the book is an encyclopedia of problem-solving methods to be carried out by hand, but more than that, it is a treatise on how to think about framing and attacking problems.”

As I said, I’ve been reading How to Solve It as a supplement to guiding the design process for dataviz. It’s our process that helps us to accomplish how we help others understand the story of the data. Polya’s technique may be a further rubric to guide us on our creative journey.

Does Polya’s logic fit into a dataviz mindset? Will these ideas resonate with our community? Could an augmented version of How to Solve It be re-conceived for individuals following a design thinking approach? 

What do you think? I’ve outlined a lot of ideas in this article, but let’s talk about it! Add your comments on social media or email me! I’d love to discuss it more!

Part IV: Selected quotes from “A SHORT DICTIONARY OF HEURISTICS”

In case you want to learn more, I’ll include selected quotes from Polya across his 67 definitions of heuristics here. Certainly, I don’t expect anyone to read this cliff notes version as a stand-in for the original, but it’s probably much easier to scan through them to become acquainted with the concepts. Who knows – it might help you in your creative process or inspire your process!

• Analogy

Similar objects agree with each other in some respect, analogous objects agree in certain relations of their respective parts…

Analogy pervades all our thinking, our everyday speech and our trivial conclusions as well as artistic ways of expression and the highest scientific achievements. Analogy is used on very different levels. People often use vague, ambiguous, incomplete, or incompletely clarified analogies, but analogy may reach the level of mathematical precision. All sorts of analogy may play a role in the discovery of the solution and so we should not neglect any sort.

Inference by analogy appears to be the most common kind of conclusion, and it is possibly the most essential kind. It yields more or less plausible conjectures which may or may not be confirmed by experience and stricter reasoning. The chemist, experimenting on animals in order to foresee the influence of his drugs on humans, draws conclusions by analogy. But so did a small boy I knew. His pet dog had to be taken to the veterinary, and he inquired:

“Who is the veterinary?”

“The animal doctor.”

“Which animal is the animal doctor?”

• Auxiliary elements

There are various reasons for introducing auxiliary elements. We are glad when we have succeeded in recollecting a problem related to ours and solved before. It is probable that we can use such a problem but we do not know yet how to use it. For instance, the problem which we are trying to solve is a geometric problem, and the related problem which we have solved before and have now succeeded in recollecting is a problem about triangles. Yet there is no triangle in our figure; in order to make any use of the problem recollected we must have a triangle; therefore, we have to introduce one, by adding suitable auxiliary lines to our figure. 

• Auxiliary problem

… a problem which we consider, not for its own sake, but because we hope that its consideration may help us to solve another problem, our original problem. The original problem is the end we wish to attain, the auxiliary problem a means by which we try to attain our end.

Yet we should not fail to look around for more good problems when we have succeeded in solving one. Good problems and mushrooms of certain kinds have something in common; they grow in clusters. Having found one, you should look around; there is a good chance that there are some more quite near.

• Bolzano, Bernard (1781-1848)

He writes about this part of his work: “I do not think at all that I am able to present here any procedure of investigation that was not perceived long ago by all men of talent; and I do not promise at all that you can find here anything quite new of this kind. But I shall take pains to state in clear words the rules and ways of investigation which are followed by all able men, who in most cases are not even conscious of following them. Although I am free from the illusion that I shall fully succeed even in doing this, I still hope that the little that is presented here may please some people and have some application afterwards.”

• Bright idea

…or “good idea,” or “seeing the light,” is a colloquial expression describing a sudden advance toward the solution. The coming of a bright idea is an experience familiar to everybody but difficult to describe…

• Can you check the result?

It is clear that our nonmathematical knowledge cannot be based entirely on formal proofs. The more solid part of our everyday knowledge is continually tested and strengthened by our everyday experience. Tests by observation are more systematically conducted in the natural sciences. Such tests take the form of careful experiments and measurements, and are combined with mathematical reasoning in the physical sciences. 

• Can you derive the result differently?

When the solution that we have finally obtained is long and involved, we naturally suspect that there is some clearer and less roundabout solution: Can you derive the result differently? Can you see it at a glance?… Having found a proof, we wish to find another proof as we wish to touch an object after having seen it.

• Can you use the result?

Exploit your success! Can you use the result, or the method, for some other problem?

• Carrying out

To conceive a plan and to carry it through are two different things.

We may use provisional and merely plausible arguments when devising the final and rigorous argument as we use scaffolding to support a bridge during construction. When, however, the work is sufficiently advanced we take off the scaffolding, and the bridge should be able to stand by itself. In the same way, when the solution is sufficiently advanced, we brush aside all kinds of provisional and merely plausible arguments, and the result should be supported by rigorous argument alone.

In short, the new unknown should be a sort of stepping stone. A stone in the middle of the creek is nearer to me than the other bank which I wish to arrive at and, when the stone is reached, it helps me on toward the other bank.

Trying to prove formally what is seen intuitively and to see intuitively what is proved formally is an invigorating mental exercise. 

• Condition

A condition is called redundant if it contains superfluous parts. It is called contradictory if its parts are mutually opposed and inconsistent so that there is no object satisfying the condition.

• Contradictory

Thus, if a condition is expressed by more linear equations than there are unknowns, it is either redundant or contradictory; if the condition is expressed by fewer equations than there are unknowns, it is insufficient to determine the unknowns; if the condition is expressed by just as many equations as there are unknowns it is usually just sufficient to determine the unknowns but may be, in exceptional cases, contradictory or insufficient.

• Corollary

a theorem which we find easily in examining another theorem just found. The word is of Latin origin; a more literal translation would be “gratuity” or “tip.”

• Could you derive something useful from the data?

We have to find the connection between the data and the unknown. We may represent our unsolved problem as open space between the data and the unknown, as a gap across which we have to construct a bridge. We can start constructing our bridge from either side, from the unknown or from the data.

• Could you restate the problem?

These questions aim at suitable VARIATION OF THE PROBLEM.

• Decomposing and recombining

You have an impression of the object as a whole but this impression, possibly, is not definite enough. A detail strikes you, and you focus your attention upon it. Then, you concentrate upon another detail; then, again, upon another. Various combinations of details may present themselves and after a while, you again consider the object as a whole but you see it now differently. You decompose the whole into its parts, and you recombine the parts into a more or less different whole.

If you go into detail you may lose yourself in details. Too many or too minute particulars are a burden on the mind. They may prevent you from giving sufficient attention to the main point, or even from seeing the main point at all. Think of the man who cannot see the forest for the trees.

Of course, we do not wish to waste our time with unnecessary detail and we should reserve our effort for the essential. The difficulty is that we cannot say beforehand which details will turn out ultimately as necessary and which will not.

Therefore, let us, first of all, understand the problem as a whole. Having understood the problem, we shall be in a better position to judge which particular points may be the most essential. Having examined one or two essential points we shall be in a better position to judge which further details might deserve closer examination. 

After having decomposed the problem, we try to recombine its elements in some new manner. Especially, we may try to recombine the elements of the problem into some new, more accessible problem which we could possibly use as an auxiliary problem.

There are, of course, unlimited possibilities of recombination. Difficult problems demand hidden, exceptional, original combinations, and the ingenuity of the problem-solver shows itself in the originality of the combination. There are, however, certain usual and relatively simple sorts of combinations, sufficient for simpler problems, which we should know thoroughly and try first, even if we may be obliged eventually to resort to less obvious means.

There is a formal classification in which the most usual and useful combinations are neatly placed. In constructing a new problem from the proposed problem, we may

• Definition

…a term is a statement of its meaning in other terms which are supposed to be well known

• Descartes, René (1596-1650)

…great mathematician and philosopher, planned to give a universal method to solve problems but he left unfinished his Rules for the Direction of the Mind. The fragments of this treatise, found in his manuscripts and printed after his death, contain more—and more interesting—materials concerning the solution of problems than his better known work Discours de la Méthode although the “Discours” was very likely written after the “Rules.” The following lines of Descartes seem to describe the origin of the “Rules”: “As a young man, when I heard about ingenious inventions, I tried to invent them by myself, even without reading the author. In doing so, I perceived, by degrees, that I was making use of certain rules.”

• Determination, hope, success

It would be a mistake to think that solving problems is a purely “intellectual affair”; determination and emotions play an important role. Lukewarm determination and sleepy consent to do a little something may be enough for a routine problem in the classroom. But, to solve a serious scientific problem, will power is needed that can outlast years of toil and bitter disappointments.

• Diagnosis

We are here particularly concerned with the student’s efficiency in solving problems. How can we characterize it? We may derive some profit from the distinction of the four phases of the solution. In fact, the behavior of the students in the various phases is quite characteristic.

Incomplete understanding of the problem, owing to lack of concentration, is perhaps the most widespread deficiency in solving problems. With respect to devising a plan and obtaining a general idea of the solution two opposite faults are frequent. Some students rush into calculations and constructions without any plan or general idea; others wait clumsily for some idea to come and cannot do anything that would accelerate its coming. In carrying out the plan, the most frequent fault is carelessness, lack of patience in checking each step. Failure to check the result at all is very frequent; the student is glad to get an answer, throws down his pencil, and is not shocked by the most unlikely results.

• Did you use all the data?

Have we got what we need? Is our conception adequate? Did you use all the data? Did you use the whole condition? 

• Do you know a related problem?

In fact, when solving a problem, we always profit from previously solved problems, using their result, or their method, or the experience we acquired solving them. And, of course, the problems from which we profit must be in some way related to our present problem.

There is usually no difficulty at all in recalling formerly solved problems which are more or less related to our present one. On the contrary, we may find too many such problems and there may be difficulty in choosing a useful one. 

• Draw a figure

The beginner should construct as many figures as they can in order to acquire a good experimental basis… Yet, for the purpose of reasoning, carefully drawn free-hand figures are usually good enough, and they are much more quickly done. Of course, the figure should not look absurd; lines supposed to be straight should not be wavy, and so-called circles should not look like potatoes.

If there are many details, we cannot imagine all of them simultaneously, but they are all together on the paper. A detail pictured in our imagination may be forgotten; but the detail traced on paper remains, and, when we come back to it, it reminds us of our previous remarks, it saves us some of the trouble we have in recollecting our previous consideration.

• Examine your guess

Your guess may be right, but it is foolish to accept a vivid guess as a proven truth—as primitive people often do. Your guess may be wrong. But it is also foolish to disregard a vivid guess altogether—as pedantic people sometimes do. Guesses of a certain kind deserve to be examined and taken seriously: those which occur to us after we have attentively considered and really understood a problem in which we are genuinely interested. Such guesses usually contain at least a fragment of the truth although, of course, they very seldom show the whole truth. Yet there is a chance to extract the whole truth if we examine such a guess appropriately.

Many a guess has turned out to be wrong but nevertheless useful in leading to a better one.

No idea is really bad unless we are uncritical. What is really bad is to have no idea at all.

• Figures

Figures are not only the object of geometric problems but also an important help for all sorts of problems in which there is nothing geometric at the outset. Thus, we have two good reasons to consider the role of figures in solving problems.

• Generalization

Passing from the consideration of one object to the consideration of a set containing that object; or passing from the consideration of a restricted set to that of a more comprehensive set containing the restricted one.

The more general problem may be easier to solve. This sounds paradoxical but, after the foregoing example, it should not be paradoxical to us. The main achievement in solving the special problem was to invent the general problem. After the main achievement, only a minor part of the work remains. Thus, in our case, the solution of the general problem is only a minor part of the solution of the special problem.

• Have you seen it before?

It is possible that we have solved before the same problem that we have to do now, or that we have heard of it, or that we had a very similar problem. These are possibilities which we should not fail to explore. We try to remember what happened. Have you seen it before? Or have you seen the same problem in a slightly different form? Even if the answer is negative such questions may start the mobilization of useful knowledge.

….We cannot know, of course, in advance which parts of our knowledge may be relevant; but there are certain possibilities which we should not fail to explore. Thus, any feature of the present problem that played a role in the solution of some other problem may play again a role. Therefore, if any feature of the present problem strikes us as possibly important, we try to recognize it. What is it? Is it familiar to you?

• Here is a problem related to yours and solved before

This is good news; a problem for which the solution is known and which is connected with our present problem, is certainly welcome. It is still more welcome if the connection is close and the solution simple. There is a good chance that such a problem will be useful in solving our present one.

The intention of using a certain formerly solved problem influences our conception of the present problem. Trying to link up the two problems, the new and the old, we introduce into the new problem elements corresponding to certain important elements of the old problem. 

• Heuristic

…or “ars inveniendi” was the name of a certain branch of study, not very clearly circumscribed, belonging to logic, or to philosophy, or to psychology, often outlined, seldom presented in detail, and as good as forgotten today. The aim of heuristic is to study the methods and rules of discovery and invention. A few traces of such study may be found in the commentators of Euclid; a passage of PAPPUS is particularly interesting in this respect. The most famous attempts to build up a system of heuristic are due to DESCARTES and to LEIBNITZ, both great mathematicians and philosophers. Bernard BOLZANO presented a notable detailed account of heuristic. The present booklet is an attempt to revive heuristic in a modern and modest form. 

• Heuristic reasoning

Reasoning not regarded as final and strict but as provisional and plausible only, whose purpose is to discover the solution of the present problem. We are often obliged to use heuristic reasoning. We shall attain complete certainty when we shall have obtained the complete solution, but before obtaining certainty we must often be satisfied with a more or less plausible guess. We may need the provisional before we attain the final. We need heuristic reasoning when we construct a strict proof as we need scaffolding when we erect a building.

Heuristic reasoning is good in itself. What is bad is to mix up heuristic reasoning with rigorous proof. What is worse is to sell heuristic reasoning for rigorous proof.

.…A heuristic argument presented with taste and frankness may be useful; it may prepare for the rigorous argument of which it usually contains certain germs. But a heuristic argument is likely to be harmful if it is presented ambiguously with visible hesitation between shame and pretension.

• If you cannot solve the proposed problem

Do not let this failure afflict you too much but try to find consolation with some easier success, try to solve first some related problem; then you may find courage to attack your original problem again. Do not forget that human superiority consists in going around an obstacle that cannot be overcome directly, in devising some suitable auxiliary problem when the original one appears insoluble.

• Induction and mathematical induction

Induction is the process of discovering general laws by the observation and combination of particular instances. It is used in all sciences, even in mathematics. Mathematical induction is used in mathematics alone to prove theorems of a certain kind. It is rather unfortunate that the names are connected because there is very little logical connection between the two processes. There is, however, some practical connection; we often use both methods together. 

• Inventor’s paradox

The more ambitious plan may have more chances of success.

This sounds paradoxical. Yet, when passing from one problem to another, we may often observe that the new, more ambitious problem is easier to handle than the original problem. More questions may be easier to answer than just one question. The more comprehensive theorem may be easier to prove, the more general problem may be easier to solve.

The paradox disappears if we look closer at a few examples. The more ambitious plan may have more chances of success provided it is not based on mere pretension but on some vision of the things beyond those immediately present.

• Is it possible to satisfy the condition?

It is good to foresee any feature of the result for which we work. When we have some idea of what we can expect, we know better in which direction we should go. Now, an important feature of a problem is the number of solutions of which it admits. Most interesting among problems are those which admit of just one solution; we are inclined to consider problems with a uniquely determined solution as the only “reasonable” problems. Is our problem, in this sense, “reasonable”? If we can answer this question, even by a plausible guess, our interest in the problem increases and we can work better.

• Leibnitz, Gottfried Wilhelm (1646-1716)

Great mathematician and philosopher, planned to write an “Art of Invention” but he never carried through his plan. Numerous fragments dispersed in his works show, however, that he entertained interesting ideas about the subject whose importance he often emphasized. Thus, he wrote: “Nothing is more important than to see the sources of invention which are, in my opinion, more interesting than the inventions themselves.”

• Lemma

Lemma means “auxiliary theorem.” The word is of Greek origin; a more literal translation would be “what is assumed.”

• Look at the unknown

This is old advice; the corresponding Latin saying is: “respice finem.” That is, look at the end. Remember your aim. Do not forget your goal. Think of what you are desiring to obtain. Do not lose sight of what is required. Keep in mind what you are working for. Look at the unknown. Look at the conclusion. The last two versions of “respice finem” are specifically adapted to mathematical problems, to “problems to find” and to “problems to prove” respectively.

Focusing our attention on our aim and concentrating our will on our purpose, we think of ways and means to attain it. What are the means to this end? How can you attain your aim? How can you obtain a result of this kind? What causes could produce such a result? Where have you seen such a result produced? What do people usually do to obtain such a result? And try to think of a familiar problem having the same or a similar unknown. And try to think of a familiar theorem having the same or a similar conclusion. Again, the last two versions are specifically adapted to “problems to find” and to “problems to prove” respectively.

• Modern heuristic

…endeavors to understand the process of solving problems, especially the mental operations typically useful in this process… Experience in solving problems and experience in watching other people solving problems must be the basis on which heuristic is built. In this study, we should not neglect any sort of problem, and should find out common features in the way of handling all sorts of problems; we should aim at general features, independent of the subject matter of the problem. The study of heuristic has “practical” aims; a better understanding of the mental operations typically useful in solving problems could exert some good influence on teaching, especially on the teaching of mathematics.

• Notation

Speaking and thinking are closely connected, the use of words assists the mind. Certain philosophers and philologists went a little further and asserted that the use of words is indispensable to the use of reason.

Speaking and thinking are closely connected, the use of words assists the mind. Certain philosophers and philologists went a little further and asserted that the use of words is indispensable to the use of reason.

Signs must be, first of all, unambiguous. It is inadmissible that the same symbol denote two different objects in the same inquiry. If, solving a problem, you call a certain magnitude a you should avoid calling anything else a which is connected with the same problem. Of course, you may use the letter a in a different meaning in a different problem.

• Pappus

…an important Greek mathematician, lived probably around A.D. 300. In the seventh book of his Collectiones, Pappus reports about a branch of study which he calls analyomenos. We can render this name in English as “Treasury of Analysis,” or as “Art of Solving Problems,” or even as “Heuristic”; the last term seems to be preferable here. A good English translation of Pappus’s report is easily accessible7; what follows is a free rendering of the original text:

“The so-called Heuristic is, to put it shortly, a special body of doctrine for the use of those who, after having studied the ordinary Elements, are desirous of acquiring the ability to solve mathematical problems, and it is useful for this alone. It is the work of three men, Euclid, the author of the Elements, Apollonius of Perga, and Aristaeus the elder. It teaches the procedures of analysis and synthesis.

“In analysis, we start from what is required, we take it for granted, and we draw consequences from it, and consequences from the consequences, till we reach a point that we can use as starting point in synthesis. For in analysis we assume what is required to be done as already done (what is sought as already found, what we have to prove as true). We inquire from what antecedent the desired result could be derived; then we inquire again what could be the antecedent of that antecedent, and so on, until passing from antecedent to antecedent, we come eventually upon something already known or admittedly true. This procedure we call analysis, or solution backwards, or regressive reasoning.

“But in synthesis, reversing the process, we start from the point which we reached last of all in the analysis, from the thing already known or admittedly true. We derive from it what preceded it in the analysis, and go on making derivations until, retracing our steps, we finally succeed in arriving at what is required. This procedure we call synthesis, or constructive solution, or progressive reasoning.

“Now analysis is of two kinds; the one is the analysis of the ‘problems to prove’ and aims at establishing true theorems; the other is the analysis of the ‘problems to find’ and aims at finding the unknown.

“If we have a ‘problem to prove’ we are required to prove or disprove a clearly stated theorem A. We do not know yet whether A is true or false; but we derive from A another theorem B, from B another C, and so on, until we come upon a last theorem L about which we have definite knowledge. If L is true, A will be also true, provided that all our derivations are convertible. From L we prove the theorem K which preceded L in the analysis and, proceding in the same way, we retrace our steps; from C we prove B, from B we prove A, and so we attain our aim. If, however, L is false, we have proved A false.

“If we have a ‘problem to find’ we are required to find a certain unknown x satisfying a clearly stated condition. We do not know yet whether a thing satisfying such a condition is possible or not; but assuming that there is an x satisfying the condition imposed we derive from it another unknown y which has to satisfy a related condition; then we link y to still another unknown, and so on, until we come upon a last unknown z which we can find by some known method. If there is actually a z satisfying the condition imposed upon it, there will be also an x satisfying the original condition, provided that all our derivations are convertible. We first find z; then, knowing z, we find the unknown that preceded z in the analysis; proceeding in the same way, we retrace our steps, and finally, knowing y, we obtain x, and so we attain our aim. If, however, there is nothing that would satisfy the condition imposed upon z, the problem concerning x has no solution.”

• Pedantry and mastery

To apply a rule to the letter, rigidly, unquestioningly, in cases where it fits and in cases where it does not fit, is pedantry. Some pedants are poor fools; they never did understand the rule which they apply so conscientiously and so indiscriminately. Some pedants are quite successful; they understood their rule, at least in the beginning (before they became pedants), and chose a good one that fits in many cases and fails only occasionally.

To apply a rule with natural ease, with judgment, noticing the cases where it fits, and without ever letting the words of the rule obscure the purpose of the action or the opportunities of the situation, is mastery.

The questions and suggestions of our list may be helpful both to problem-solvers and to teachers. But, first, they must be understood, their proper use must be learned, and learned by trial and error, by failure and success, by experience in applying them. Second, their use should never become pedantic. You should ask no question, make no suggestion, indiscriminately, following some rigid habit. Be prepared for various questions and suggestions and use your judgment. You are doing a hard and exciting problem; the step you are going to try next should be prompted by an attentive and open-minded consideration of the problem before you. You wish to help a student; what you say to your student should proceed from a sympathetic understanding of his difficulties.

And if you are inclined to be a pedant and must rely upon some rule learn this one: Always use your own brains first.

Teaching to solve problems is education of the will. Solving problems which are not too easy for him, the student learns to persevere through unsuccess, to appreciate small advances, to wait for the essential idea, to concentrate with all his might when it appears. If the student had no opportunity in school to familiarize himself with the varying emotions of the struggle for the solution his mathematical education failed in the most vital point.

• Practical problems

Practical problems are different in various respects from purely mathematical problems, yet the principal motives and procedures of the solution are essentially the same. 

There is a widespread opinion that practical problems need more experience than mathematical problems. This may be so. Yet, very likely, the difference lies in the nature of the knowledge needed and not in our attitude toward the problem. In solving a problem of one or the other kind, we have to rely on our experience with similar problems and we often ask the questions: Have you seen the same problem in a slightly different form? Do you know a related problem?

In solving a mathematical problem, we start from very clear concepts which are fairly well ordered in our mind. In solving a practical problem, we are often obliged to start from rather hazy ideas; then, the clarification of the concepts may become an important part of the problem. Thus, medical science is in a better position to check infectious diseases today than it was in the times before Pasteur when the notion of infection itself was rather hazy. Have you taken into account all essential notions involved in the problem? This is a good question for all sorts of problems but its use varies widely with the nature of the intervening notions.

In a perfectly stated mathematical problem all data and all clauses of the condition are essential and must be taken into account. In practical problems we have a multitude of data and conditions; we take into account as many as we can but we are obliged to neglect some. Take the case of the designer of a large dam. He considers the public interest and important economic interests but he is bound to disregard certain petty claims and grievances. The data of his problem are, strictly speaking, inexhaustible. For instance, he would like to know a little more about the geologic nature of the ground on which the foundations must be laid, but eventually he must stop collecting geologic data although a certain margin of uncertainty unavoidably remains.

• Problems to find, problems to prove

The aim of a “problem to find” is to find a certain object, the unknown of the problem.

The unknown is also called “quaesitum,” or the thing sought, or the thing required. “Problems to find” may be theoretical or practical, abstract or concrete, serious problems or mere puzzles. We may seek all sorts of unknowns; we may try to find, to obtain, to acquire, to produce, or to construct all imaginable kinds of objects. In the problem of the mystery story the unknown is a murderer. In a chess problem the unknown is a move of the chessmen. In certain riddles the unknown is a word. In certain elementary problems of algebra the unknown is a number. In a problem of geometric construction the unknown is a figure.

The principal parts of a “problem to find” are the unknown, the data, and the condition.

The aim of a “problem to prove” is to show conclusively that a certain clearly stated assertion is true, or else to show that it is false. We have to answer the question: Is this assertion true or false? And we have to answer conclusively, either by proving the assertion true, or by proving it false.

A witness affirms that the defendant stayed at home a certain night. The judge has to find out whether this assertion is true or not and, moreover, he has to give as good grounds as possible for his finding. Thus, the judge has a “problem to prove.” Another “problem to prove” is to “prove the theorem of Pythagoras.” We do not say: “Prove or disprove the theorem of Pythagoras.” It would be better in some respects to include in the statement of the problem the possibility of disproving, but we may neglect it, because we know that the chances for disproving the theorem of Pythagoras are rather slight.

If a “problem to prove” is a mathematical problem of the usual kind, its principal parts are the hypothesis and the conclusion of the theorem which has to be proved or disproved.

• Progress and achievement

Have you made any progress? What was the essential achievement? We may address questions of this kind to ourselves when we are solving a problem or to a student whose work we supervise. Thus, we are used to judge, more or less confidently, progress and achievement in concrete cases. The step from such concrete cases to a general description is not easy at all. Yet we have to undertake this step if we wish to make our study of heuristic somewhat complete and we must try to clarify what constitutes, in general, progress and achievement in solving problems.

…Another aspect of the progress of our work is that our mode of conception changes. Enriched with all the materials which we have recalled, adapted to it, and worked into it, our conception of the problem is much fuller at the end than it was at the outset. Desiring to proceed from our initial conception of the problem to a more adequate, better adapted conception, we try various standpoints and view the problem from different sides. 

• Puzzles

What the questions and suggestions of the list can do is to “keep the ball rolling.” When, discouraged by lack of success, we are inclined to drop the problem, they may suggest to us a new trial, a new aspect, a new variation of the problem, a new stimulus; they may keep us thinking.

• Reductio ad absurdum and indirect proof

Reductio ad absurdum shows the falsity of an assumption by deriving from it a manifest absurdity. “Reduction to an absurdity” is a mathematical procedure but it has some resemblance to irony which is the favorite procedure of the satirist. Irony adopts, to all appearance, a certain opinion and stresses it and overstresses it till it leads to a manifest absurdity.

Indirect proof establishes the truth of an assertion by showing the falsity of the opposite assumption. Thus, indirect proof has some resemblance to a politician’s trick of establishing a candidate by demolishing the reputation of his opponent.

Both “reductio ad absurdum” and indirect proof are effective tools of discovery which present themselves naturally to an intent mind. Nevertheless, they are disliked by a few philosophers and many beginners, which is understandable; satirical people and tricky politicians do not appeal to everybody. We shall first illustrate the effectiveness of both procedures by examples and discuss objections against them afterwards.

It must be confessed that “reductio ad absurdum” as a means of exposition is not an unmixed blessing. Such a “reductio,” especially if it is long, may become very painful indeed for the reader or listener. All the derivations which we examine in succession are correct but all the situations which we have to face are impossible. Even the verbal expression may become tedious if it insists, as it should, on emphasizing that everything is based on an initial assumption; the words “hypothetically,” “supposedly,” “allegedly” must recur incessantly, or some other device must be applied continually. We wish to reject and forget the situation as impossible but we have to retain and examine it as the basis for the next step, and this inner discord may become unbearable in the long run.

Yet it would be foolish to repudiate “reductio ad absurdum” as a tool of discovery. It may present itself naturally and bring a decision when all other means seem to be exhausted as the foregoing examples may show.

• Redundant

See CONDITION. {I can’t help but see this as a math joke – JF}

• Routine problem

Routine problems, even many routine problems, may be necessary in teaching mathematics but to make the students do no other kind is inexcusable. Teaching the mechanical performance of routine mathematical operations and nothing else is well under the level of the cookbook because kitchen recipes do leave something to the imagination and judgment of the cook but mathematical recipes do not.

• Rules of discovery

The first rule of discovery is to have brains and good luck. The second rule of discovery is to sit tight and wait till you get a bright idea.

It may be good to be reminded somewhat rudely that certain aspirations are hopeless. Infallible rules of discovery leading to the solution of all possible mathematical problems would be more desirable than the philosophers’ stone, vainly sought by the alchemists. Such rules would work magic; but there is no such thing as magic. To find unfailing rules applicable to all sorts of problems is an old philosophical dream; but this dream will never be more than a dream.

A reasonable sort of heuristic cannot aim at unfailing rules; but it may endeavor to study procedures (mental operations, moves, steps) which are typically useful in solving problems. Such procedures are practiced by every sane person sufficiently interested in his problem. They are hinted by certain stereotyped questions and suggestions which intelligent people put to themselves and intelligent teachers to their students. A collection of such questions and suggestions, stated with sufficient generality and neatly ordered, may be less desirable than the philosophers’ stone but can be provided. The list we study provides such a collection.

• Rules of style

The first rule of style is to have something to say. The second rule of style is to control yourself when, by chance, you have two things to say; say first one, then the other, not both at the same time.

• Rules of teaching

The first rule of teaching is to know what you are supposed to teach. The second rule of teaching is to know a little more than what you are supposed to teach.

First things come first. The author of this book does not think that all rules of conduct for teachers are completely useless; otherwise, he would not have dared to write a whole book about the conduct of teachers and students. Yet it should not be forgotten that a teacher of mathematics should know some mathematics, and that a teacher wishing to impart the right attitude of mind toward problems to his students should have acquired that attitude himself.

• Separate the various parts of the condition

Our first duty is to understand the problem. Having understood the problem as a whole, we go into detail. We consider its principal parts, the unknown, the data, the condition, each by itself. When we have these parts well in mind but no particularly helpful idea has yet occurred to us, we go into further detail. We consider the various data, each datum by itself.

Having understood the condition as a whole, we separate its various parts, and we consider each part by itself.

We see now the role of the suggestion that we have to discuss here. It tends to provoke a step that we have to take when we are trying to see the problem distinctly and have to go into finer and finer detail. It is a step in DECOMPOSING AND RECOMBINING.

Separate the various parts of the condition. Can you write them down? We often have opportunity to ask this question when we are SETTING UP EQUATIONS.

• Setting up equations

To set up equations means to express in mathematical symbols a condition that is stated in words; it is translation from ordinary language into the language of mathematical formulas. The difficulties which we may have in setting up equations are difficulties of translation.

In order to translate a sentence from English into French two things are necessary. First, we must understand thoroughly the English sentence. Second, we must be familiar with the forms of expression peculiar to the French language. The situation is very similar when we attempt to express in mathematical symbols a condition proposed in words. First, we must understand thoroughly the condition. Second, we must be familiar with the forms of mathematical expression.

It is very much the same in setting up equations. In easy cases, the verbal statement splits almost automatically into successive parts, each of which can be immediately written down in mathematical symbols. In more difficult cases, the condition has parts which cannot be immediately translated into mathematical symbols. If this is so, we must pay less attention to the verbal statement, and concentrate more upon the meaning. Before we start writing formulas, we may have to rearrange the condition, and we should keep an eye on the resources of mathematical notation while doing so.

• Signs

As Columbus and his companions sailed westward across an unknown ocean they were cheered whenever they saw birds. They regarded a bird as a favorable sign, indicating the nearness of land. But in this they were repeatedly disappointed. They watched for other signs too. They thought that floating seaweed or low banks of cloud might indicate land, but they were again disappointed. One day, however, the signs multiplied. On Thursday, the 11th of October, 1492, “they saw sandpipers, and a green reed near the ship. Those of the caravel Pinta saw a cane and a pole, and they took up another small pole which appeared to have been worked by iron; also another bit of cane, a land-plant, and a small board. The crew of the caravel Niña also saw signs of land, and a small branch covered with berries. Everyone breathed afresh and rejoiced at these signs.” And in fact the next day they sighted land, the first island of a New World.

Our undertaking may be important or unimportant, our problem of any kind—when we are working intensely, we watch eagerly for signs of progress as Columbus and his companions watched for signs of approaching land. We shall discuss a few examples in order to understand what can be reasonably regarded as a sign of approaching the solution.

In a well-constructed chess problem there is no superfluous piece. Therefore, we have to take into account all chessmen on the board; we have to use all the data. The correct solution does certainly use all the pieces, even that apparently superfluous white knight. In this last respect, the new move that I contemplate agrees with the correct move that I am supposed to find. The new move looks like the correct move; it might be the correct move.

Thanks to Mary Aviles for the edits and conversations on Design Methodology

The post How to Solve It – and by It – I Mean *Anything* appeared first on Nightingale.

I’m told by the authors that this project grew out of a loose collaboration over many years, its origins going back to 1995.  At the time, Paul Kahn was running the Dynamic Diagrams agency and Hugh Dubberly was Design Director at Netscape where they collaborated on a series of celebrated maps of early web-based applications. Twenty years later, they both found themselves teaching in the Information Design and Data Visualization MFA program at Northeastern University where they met Liuhuaying Yang when she was a graduate student in that program.

We asked the authors to talk about what prompted them to write about these visualizations, their collaborative archive called the COVIC database, and what the data visualization community can learn from this kind of reflection.

JF: Why did the team write this essay?

Paul: I was looking regularly at The New York Times and The Washington Post and was impressed by the drama and ingenuity of the visualizations they published. The death milestones were, intentionally, the most dramatic. I started subscribing to the Financial Times at the start of the pandemic. The FT’s view of the world from The City in London was so entirely materialistic, it was a balance to reading the US news. That is how I discovered John Burn-Murdoch’s work and saw how influential his line charts became during the ‘flatten the curve’ phase.  It was Liuhuaying’s contributions that really gave us a sense of the visualizations coming from Singapore and China. And Hugh was analyzing the way The New York Times was devoting blocks of front-page space to pandemic visualizations week after week. We were sending each other examples and commenting on them for many months.

I saw there were great examples coming from many different news organizations that focused on the quantity of loss. The emerging pattern of strategies to visualize death milestones struck me as something worth writing about. Once it started, it was natural for it to become a collaboration project. It was possible because the three of us had views, ideas, and experiences that complimented each other.

JF: What is a death milestone? Is this something unique to the COVID-19 pandemic? 

Paul: In the essay, we say these milestones are visualizations of big numbers and points of reflection. In our initial email discussions, we were comparing the online visualizations to the physical COVID-19 memorials that were popping up in different countries, and comparing those to war memorials. Along with the online visualizations, we collected links to stories and videos about physical memorials. There was A World Remembers in New Zealand’s STUFF that described memorials in the US, England, Germany, Italy, Argentina, South Africa, Indonesia, India, and Russia. We learned from this and decided to limit the essay to comparisons of the online examples.

I do think the visualization of death milestones is unique to the COVID-19 pandemic because they appeared while the event was happening. The Oklahoma City National Memorial or Mamayev Kurgan Memorial to the Battle of Stalingrad or The Nanjing Massacre Memorial Wall is created to remember events that happened in the past. Some of the visualizations we present in the article were certainly reaching for closure, but closure wasn’t there. 

Liuhuaying: I was inspired to search for examples of death memorials. How do we link to what visual representation? What do we think of death? I still remembered Paul’s comment on those physical representations: “a lot of flags, a lot of melting ice figures, a lot of nails, a lot of names, the wall of faces”. It seems that we have a kind of “convention” when we memorize deaths. Then how is it different in a pandemic context, particularly in COVID-19 one? Also, compared to physical representations, we master other kinds of techniques, such as data visualizations with interactive web pages and animations. How would this make things different?

JF:  The introduction to Unimaginable Death mentions the COVID-19 Online Visualization Collection (COVIC). Can you explain that collection and how it relates to this project?

Paul: COVIC began in March 2020. That was a special moment for many people around the world. I participated in a Center for Design conversation with Dr. Isabelle Boutron from the University of Paris and this is when she started what became the Covid-19 – living NMA initiative to map all the COVID-19 clinical trials. Last week at a talk at the Rumsey Map Center, Jessica Martin from Bloomberg CityLab recounted how it was that moment when she and Laura Bliss came up with the idea to ask readers to submit homemade maps of their lives that became The Quarantine Atlas. It was a moment when everyone had to stop and look around for something to do. That gave birth to many projects.

From the beginning, COVIC was a collection of visualizations. Initially, it was a spreadsheet of articles that contained visualizations, with metadata about the publisher, language, country, date, and subject. I sent an email to a network of friends, colleagues, and former students in many countries asking them to send in links, which seeded the international nature of the collection. It was Hugh who looked at this and said: This is about the figures. It was at that point, when we had a few hundred articles, that we went back to record and classify each individual figure. We tried to brainstorm a way to automate this, to extract and clip the visualizations, but we did it ‘by hand’ so to speak. We had many undergraduate and graduate students from Northeastern helping with support from the Center for Design, along with volunteers who contacted us.

Liuhuaying: I gathered examples for COVIC whenever I saw them, especially those from the Chinese community. They are numerous and many are fantastic. It’s also interesting for us to see how different or similar the visual strategies can be by comparing it to the English-speaking community.

The first example I contributed to COVIC was my own project. In early Feb 2020, I created an interactive website titled “Dynamic tracking of COVID-19 in Singapore” for zaobao.sg, the digital platform of the Chinese dailies in Singapore. We visualized the daily situation reports released by the Ministry of Health to inform and communicate with local audiences about the COVID-19 situation in Singapore. As the situation evolved, we kept updating the data every day until June 2nd. The aim of the visualization is to help better picture the relationship between cases within a cluster, how these clusters are interrelated and activities-based, and how cases are disseminated to various hospitals.

I presented this project in the Summer school course and on other occasions to share my visual strategies, intentions, and challenges. A comment I heard most is how unique or uncommon it was that we visualized individuals. I thought it was attributed to data availability until I saw how other examples in COVIC managed to visualize the trees in the forest.

Paul: In May 2020, I taught remotely a summer school course about collecting visualizations during the pandemic. I was in France, my students were in their homes around the US, and my guest speakers were in many parts of the world.  The discussion with students and presentations by Liuhuaying and others helped to solidify the ideas. We wanted to create something that could be used for teaching and research when the pandemic was no longer happening. We could see the pandemic was inspiring and challenging the data visualization community, it was changing data journalism, and it was producing elaborate yet ephemeral results. The data was changing, things were appearing and disappearing, paywalls were opening and closing, and vaccines, mutations, and social and political issues were morphing every few weeks. I thought then, and still think, it is important to gather with an open mind about the role visualization plays, then classify to support later search and filtering.

COVIC is an opportunistic collection. I am not a social scientist, so I had to learn that this is a ‘thing’ or at least a recognized method. We have collected what was presented to us, what people sent us, what we came across, and what was linked to what we read. COVIC is a large and organized sample from an infinite set.

Our version 1.0 was made with Google Sheets, an Amazon S3 server for storing images, and custom code to visualize the figures. That version worked but didn’t scale. We had no idea how big it would get. When we approached 10,000 figures we had to migrate to the current version 2.0, which is managed in Airtable bases and visualized with a custom Javascript SAP. The public COVIC Visualizer is available to everyone and documented on the COVIC website. The metadata can be shared on request in CSV format.

Hugh: Early discourse on design history tends to focus on individual artifacts, this poster, that book, or perhaps a particular subway signage system. But design practice explores ‘spaces of possibilities’ or ‘solution spaces’ with any designed artifact being just one of many possible choices considered. Design history, particularly as it relates to visualizations, is beginning to recognize this fact. Comparative histories (and critiques) require collections of related work; collections, however, require databases for managing and for accessing works.

So, in addition to recording a specific set of visualizations of COVID-19, we also saw the COVIC project as exploring an emerging approach to comparative design history. From that frame, the article on “Unimaginable Death” might be seen as an example of the sort of comparative histories that COVIC supports.

Paul: Exactly. “Unimaginable Death” is simply an example of the kind of analysis and reflection that can be built up from the collection. We came up with a subject – death milestones – and we found a rich set of examples to discuss and learn from. Now we’re thrilled that Nightingale is giving us the chance to present this work to your readers. We hope it will inspire not only discussion but more articles and projects that use this material and this methodology.

JF: One of the challenging aspects of the pandemic was our inability to control the spread of the virus and the resulting waves of infections which lead to inevitable milestones. Did your team have some kind of reaction to the nearing of the milestones? Were you following certain papers and search results or was it more anticipatory?

Paul: I don’t think milestones are inevitable. We mention examples of countries where one-hundred-thousand deaths, half-a-million deaths go by and no data visualization milestone appears online, as far as we can tell. 

I went through my emails to Hugh and Liuhuaying and found that the first set we shared in December 2021 started “from Italy in March 2020 through Germany in November 2021”. Once we had that set I began to anticipate later milestones that would occur in the US and Spain the following year. Liuhuaying found the Chinese examples that were a response to the first surge of deaths. I did search for visualizations in the Spanish media when that country crossed the hundred-thousand mark. And like everyone in the world, we anticipated the one-million event in the US. We all anticipated that we could not complete the essay until after they appeared.

JF: The dataviz world isn’t that big, did your team have a direct dialogue with any of the journalists featured in the collection? 

Paul: I know Liuhuaying and Irene de la Torres Arenas from teaching at the IDV program. They were both kind enough to speak to my class during the first pandemic summer. Irene was working at BBC doing COVID-19 visualizations, though now she is designing for FT. We didn’t talk about death milestones or the specific challenge of representing large numbers at that time. Six months later Irene led the team that produced the 1-year milestone that visualized COVID-19 data as flowers.

Liuhuaying: I had asked Spe Chen, a data visualization designer at The Straits Times who designed the floral icons for the example “Remembering the 5 million lives lost to Covid-19” about her inspirations, especially whether she was aware of the BBC “petal” design. 

She was impressed by Poppy Field – Visualising War Fatalities and searching for visualizations representing the death toll. Inspired by Japanese altars, she decided to make flowers and tried many types of flowers. It was only in the middle of her flower ideation that she saw the BBC “petal” design.

Paul: We heard from several designers indirectly. We learned the backstory of The New York Times examples from the Times Insider series, The Project Behind a Front Page Full of Names and On the Front Page, a Wall of Grief. Later there was Clare Santoro’s Nightingale piece about Alyssa Fowers’ “Cut Short”, and the PolicyViz podcast interview with Aliza Aufrichtig about “Voices of a Grieving Nation.” I also learned about “1 million U.S. COVID-19 deaths” from another PolicyViz interview with Danielle Alberti, and it is also discussed in Behind the Scenes with Axios Data Visualization in Nightingale.

JF: After collecting so many charts about our inability to visualize such huge numbers of human loss, do you each have examples that you find effective?

Liuhuaying: It depends on how we define the effectiveness in this context. I love the three projects in flower examples because of their high emotional values. The flower element linked to funerals and memorials across cultures provides an extra function to the charts: compassion and consolation.

Those examples of dots also provide emotional values by triggering our awe of life: so many lives passed away, sorrow, life is vulnerable, and we need to be more serious about the situation. And aesthetically, both expressed the beauty of lives. However, as always expected in war memorials, we would like to send a bouquet of flowers, and may the deceased rest in peace.

Paul: When Hugh introduced the metaphor of the Forest and the Trees into the essay, it helped me see the fundamental design question that everyone had to face. The designs are not about death and loss, per se. They are about communicating quantities and what quantities are made from. This can be done by carefully labeling the axis of a line chart or area chart. That thought process disengages the visualizations from the emotional experience. And then I recognized that the ones that moved me personally were the designs that re-engaged my emotions by playing with expectations. The designs that ‘violated the grid’ either literally — the death spike on the US map that went through The New York Times masthead — or figuratively by shapeshifting from bars to dot swarms — both the NBC “Seeing the Scale” and The New York Times “How America Reached One Million” do this while engaging the user in parallax scrolling – or animating changing numbers to mimic organic growth – like the unfolding flower petals of the BBC and The Straits Times animations.

Hugh: Building on Paul’s comments, part of what intrigued me about this project — looking at the ways COVID-19 deaths are represented and the ways milestones were marked — was how this type of situation has been addressed before. The AIDS Memorial quilt is an amazing example. Also, the Vietnam War Memorial.  And the Stolpersteine (stumbling stone) memorials in Germany, more than 90,000 small brass plaques place on or near the homes of victims of the Nazis. (See “How Germans Remember the Holocaust” in The Atlantic) These memorials make each individual uniquely visible while also placing the individuals (and the viewers) in the context of unimaginably large numbers. They evoke a mixed set of emotions: awe, wonder, horror, despair, guilt, and more.

JF: How do you want our community to use an essay like this? Did you have any design intentions as you were writing it?

Paul: We want designers to look at these examples and ask themselves hard questions. Should visualizations of death milestones afford mourning? Should they be vectors of social change? Should they impress us to behave in ways that reduce the spread of COVID-19 death? I think the examples in COVIC demonstrate that the data visualization community has collectively said ‘yes’ to all of these questions. 

I watched a talk by Mushon Zer-Avi last night. Mushon gave this keynote, Friction & Flow — a Design Theory of Change, at the Better World x Design conference in Providence RI. He makes interesting use of ‘lessons from the pandemic’ in his talk and builds on the distinction between affordance and signifier.

As Mushon built the distinction between activism that focuses on signifiers (occupy wall street) vs activism that focuses on affordances (canceling debt), I thought we could apply something similar to the death milestones. The visualizations that are clearly assembled from individual stories, whether those stories are represented by a single sentence, or a phrase, identifying a ‘type’ of person, or presenting an audio portrait, are affording our personal confrontation with loss.

The visualizations that describe what ‘the country’ or ‘the world’ has lost as a quantity, compared to a line of buses or the population of a city, reaching this point on the Y-axis, spread across time, distributed in our collective geography, are generating signifiers. They signify the quantities they represent. They do not afford our engagement in the loss of life they are made from. Or maybe, as Liuhuaying says, they do when the signifier invokes an affordance that we understand, such as placing flowers on a grave.

We want people to recognize each other’s work and build on the patterns we observed. 

JF:  How can readers find the examples you discuss?

Printing the essay as a supplement to Nightingale Magazine, Issue 2, has many advantages. It affords everyone the opportunity to see these images side by side on the printed page. But a disadvantage is that we could not print the links to the original stories for each of the 41 examples. This online interview affords us that chance. 

Let me offer everyone this table with the story titles linked. I hope people will use this to explore the examples in context and experience the interactivity, animation, and audio found in many of the pieces.

For more visualizations, please visit the COVID-19 Online Visualization Collection (COVIC). 

Other articles by the authors: 

• The Pandemic That Launched a Thousand Visualizations
• COVIC: Collecting Visualizations of Covid-19 to Outline a Space of Possibilities
• Trails, an interactive visual landscape in collaboration with Dario Rodighiero that combined the image collection into a 2-D space

The post Behind the Scenes of “Unimaginable Death” appeared first on Nightingale.

If you happened to tune into our Fireside Chat you might remember hearing our publications director, Jason Forrest, mention that we saved cover design until the rest of the magazine was complete. We started out thinking that we wanted to use the cover as a collage of the wonderful visualizations within. As it turned out, that was harder than we thought. Our central challenge was how to select from among all the amazing work inside. We decided that making that kind of choice was in opposition to our desire to celebrate the community as a whole. Luckily, Julie had some other tricks up her sleeve!

The entire magazine is chock full of fun fonts. For one alternate cover concept, Julie showcased one of the coolest fonts alongside a list of all the magazine’s contributors. In this concept, she also tried out a range of masthead treatments. Initially, we were partial to the vertical treatment, but once we saw the option on the right (below), we changed our minds.

The next series of designs prioritized both the launch messaging and the issue’s editorial theme of “culture.” By now, we were all drawn in the direction of a clever illustration. Note the subtle, but significant, difference in the two horizontal masthead treatments. The one in the middle is just a little bit cleaner—that’s what we preferred.

We knew we wanted to produce a visualization of the magazine itself (a meta viz!), but due to time and resource constraints, we didn’t collect data systematically throughout the publication process. The egg chart was meant to represent the magazine’s launch, but the hard-boiled version didn’t quite land. And, eventually, Julie designed three different visualizations for inside the magazine, so we didn’t need the cover to fulfill that function.

When we saw the last concept, we all knew it was the one. It perfectly communicated hatching and dataviz in a simple and clever way that made us all smile; plus, Julie was able to tie in the chick reference with a small image on the magazine’s spine. We conducted several quick surveys with folks in our immediate circles and the results were unanimous: this was the one!

During the many months of magazine production, the editorial team met every Wednesday morning (for Jason, Mary, and Claire) and evening (for Julie) to review the page designs that Julie had completed that week. These meetings were our absolute favorite part of the publication process, and we look forward to them in anticipation of Issue 2! Hopefully, this article gave you a little peak into what fun we had whenever we got to review Julie’s delightful interpretations!

And, if you haven’t ordered your copy of Issue 1 yet, you can still get yours while supplies last.

The post You Asked for It: Nightingale Magazine Issue 1 Alternate Covers appeared first on Nightingale.

In our last installment, we suggested that organizational data visualization work can be light on methodology and we proposed a high-level framework to break the silos between practices, roles, and the people that inhabit them. Here we examine data literacy as foundational to achieving Information Empowerment.

Community research is a means of gauging data literacy–which we think may be an overlooked concept. Design assets like user personas and journey maps are tools that designers can use to link data collected from subject matter experts to development team activity. In this way, designers act as translators and even project managers. While it’s certainly possible to develop websites and digital applications without design and without community/user data collection, these efforts often miss their mark because they are not informed by context. Organizing around a specific initiative affords a literacy opportunity for both the community AND the practitioners. Roles like design and UX research can provide translation across the team. In the case study below, the designer, the UX researcher, the users, and the developers worked together to successfully deliver the creative brief.

What does it mean to organize around an initiative? As a case study, consider a municipal effort whose goal was to improve access to early childhood education and care for low-income residents. Pre-pandemic, one of Mary’s client projects was to develop a comprehensive resource for parents and caregivers in Detroit. The project involved streamlining the online financial eligibility inquiry and application processes. The visualization, in this case, was both a web interface and a mobile application. Early childhood education and care resemble a “marketplace” model in that stakeholders include parents and caregivers, service providers, and government funding sources. The project was conceived in response to nearly 60 percent of Detroit’s three- and four-year-olds who were not in preschool (source). A lack of awareness, complex eligibility requirements, and a burdensome application process were all contributing factors.

In an effort to improve our team’s data literacy, we sourced subject matter expertise throughout this process. We conducted three listening labs with Detroit parents and caregivers to understand what motivated or deterred them from seeking early childhood care and learning options, paying special attention to how they became aware of their childcare choices. In an effort to make it easier for our stakeholders to attend, these sessions were hosted at one of our client’s community care locations and we provided a child-friendly setting that included coloring books, crafts, toys, and refreshments. The sessions felt like a mix between a parent information meeting and a playgroup.

The listening lab findings were used to revise stakeholder digital journey maps, refine the tech stack, and identify data and operational infrastructure needs. By utilizing community-based participatory research practices, we were able to approach parent and caregiver participants with transparency and we revised our assumptions based on their input.

In the case study above, the data and development team had ongoing assumptions and knowledge that differed from the actual needs of the community. By finding a way to incorporate their users into the working methodology, the team was able to make a better product that spoke more directly to their needs.

Just as our data and development teams need to collaborate with subject matter experts – in this case, the caregivers directly – optimizing the impact of the communication also needs the expertise of designers to make the information accessible and meaningful. These are, in many ways, the most crucial steps in understanding, as it is the realization of the meaning of data and the relevance that allows for generating action. According to MIT, partnering with design and data visualization can help the 82 percent of organizations that struggle to harness their unstructured data sourced from text, audio, social media, customer reviews, etc. The project’s designer was able to source collected data to create the journey map, which was the basis for the eligibility model, and directed the backend development team’s activity. As such, we fulfilled the creative brief and streamlined the eligibility and pre-application process in a way that was inclusive, easy to navigate, and accessible.

Here are three steps to help break down functional silos:

1. Invite involvement from your subject matter experts on the collection methods, iteration, and interpretation of your data. Participants and community members can use this experience beyond the initiative and when consuming other visualizations or reports in the future.
2. Explain and educate about what you are trying to do and how you are trying to do it. Invite conversations on what you collected (and didn’t collect), how you analyzed it, and how folks can make decisions based on your findings or your visualization. This stance is essential to building trust.
3. Be flexible in your mindset so that you can incorporate feedback and iterate quickly.
4. Infuse patience and fun whenever possible – it helps us remember that we’re all trying to be respectful, mindful, authentically good humans.

Collaboration across your extended team, which can include the community you’re serving, helps to establish and reinforce project equity. Even the most skilled data practitioners can advance their own knowledge of data seeing it in context and by taking a more initiative-based approach to their work. In part one of this series, we asked: “If data is a currency, how do you spend it wisely?” In this example, the answer is by letting it compound.

What do you think?

1. Although this is a community-based example, how could you apply these same ideas in your work?
2. What are other mechanisms for developing and reinforcing in-context data literacy in your practice?
3. What are some of the barriers to this type of approach in your organization and in your daily work?

Share your thoughts with us at nightingale@datavisualizationsociety.org.

The post Information Empowerment: A Reciprocal Data Literacy Case Study, Part 3 appeared first on Nightingale.