But if I were to ask you to picture something from the 2010s, that answer might range anywhere from colored leggings to checkered Vans. That range gets even wider when we look at the 2020s so far.

We used to have a very clear idea of which styles belong to which decade, but that distinction has gotten increasingly muddy in the last fifteen to twenty years. We’ve lost the pattern of an iconic style or two defining each decade, and it’s affected our favoritism when it comes to fashion. The 80s, 90s, and 2000s—decades with only a handful of predominant styles—rank highest when respondents are asked for their favorite fashion decade.

On average, 9.5% of respondents favored the 80s, 11.25% favored the 90s, and 8.25% favored the 2000s. 

Even when we abandon the idea of “favorites,” those decades still rank the highest when respondents were asked how fashionable they found each decade. Repeatedly, the 2010s and 2020s rank lowest on average when it comes to being fashionable decades with a defined sense of style. 

Fashion trends are increasingly speed-running their usual five stages: introduction, rise, peak, decline, and obsolescence. Instead of the usual fifteen to twenty year cycle, we’re now seeing trends rise and fall within a matter of months. What happened?

The Internet.

Internet usage has increased across all generations over the last 25 years, and with it, our access to fashion inspiration outside of current pop culture. Instead of fashion trends being born on a runway and trickling down through magazines, movies, and music videos to the general public, modern teenagers and young adults are finding their new favorite styles on their For You and Explore pages, with 42% of Gen Z listing social media as their main source of fashion inspiration.

While expressions of individuality and personality have always been a priority when it comes to fashion, younger generations now feel that burden more acutely due to their exposure to the world online. Pre-internet, you knew the people in your town, and you knew the familiar movie and music stars. It was normal for everyone to take fashion inspiration from the screen, like when Dirty Dancing had everyone in leotards, or when Top Gun boosted aviator jacket sales. In the digital era, you have access to the whole world. 

That’s not an exaggeration, either. Out of the 8 billion people on Earth, more than 5.17 billion use social media and spend an average of over 2 hours scrolling every day. Instagram and TikTok are the top platforms for young adults, with 89% of Gen Z users on Instagram and 82% on TikTok. 

Breaking those audiences down makes it even more jarring to realize how many people we’re seeing on our screens now. Instagram alone has 3 billion monthly active users, and nearly a third of them are 18-24 year olds. TikTok is no different, with a majority of its 1.9 billion monthly active users being Gen Z. Additionally, out of Pinterest’s 553 million monthly active users, 42% of them are Gen Z, often searching specifically for style inspiration.

With these sorts of numbers, it’s not outrageous to assume that a young adult in 2026 will see thousands of strangers online every day. More often than not, they’ll see these strangers jumping onto the same trends they are, but when the whole world is following the same trends, how is anyone meant to feel like an individual? How are the 71% of Gen Z’ers that prioritize personality in their style meant to feel like they’re unique?

It seems their answer is a wider range of hyper-specific aesthetic niches. Now, to a reader who isn’t chronically online, you might think the word aesthetic is an adjective describing something “concerned with beauty or the appreciation of beauty” or maybe a noun for “a set of principles underlying and guiding the work of a particular artist or artistic movement.” In the modern online fashion world, it means something a bit more distinct.

A clothing aesthetic in 2026 can be defined as, “your personal style or the overall vibe your outfits create. It’s the visual theme that ties your wardrobe together, from colors and patterns to the types of pieces you wear,” according to Copenhagen Fashion Summit. Included with the site’s definition are no less than 42 different aesthetics, such as Soft Girl, Clean Girl, Streetwear, Fairycore, Cottagecore, Witchcore, both Light and Dark Academia, and many others. 

Cottagecore might be one of the most popular, emerging back in 2019, and is essentially a romanticization of rural life. Cottagecore styles include warm and earthy colors, flowy dresses, puffed sleeves, and cardigans, while activities include gardening, crocheting, and baking bread. Overall, it’s a cozy, peaceful aesthetic that prioritizes comfort. While the general trend might’ve died a few years ago, Cottagecore has quietly lived on past its hype like many of these aesthetics tend to do.

Since Cottagecore’s heyday, aesthetics have gotten even more specific. Depop, a popular clothing resale platform, posted their 2024 Trend Report, and the following ‘core’ styles had some of the highest search volume increases: “Contemporary classics,” “Minimalist renaissance,” “Retro sportswear,” and “Indie vanguard.”

Contemporary classics is defined as an “updated take on ‘old money’” in the report, reviving preppy styles by blending Ivy League style with countryside vibes. Brands like J.Crew and Ralph Lauren are named as the leaders here, with Depop saying the aesthetic “reflects a yearning for stability and reliability.” 

The Minimalist renaissance is a return to “understated elegance,” according to Depop, and is focused on clean lines, neutral colors, and classics like cashmere and tailored coats. This aesthetic has a specific focus on craftsmanship and dedication to timeless taste.

Retro sportswear follows the more traditional trend pattern of recycling from decades prior, and pulls from 80s windbreakers and 90s athletic styles, combining them with modern flair for nostalgic yet practical outfits. This specific style’s increase could be attributed to the rise in popularity of casual sports like pickleball in recent years. 

And finally, Indie vanguard is described as “bold reimagining of 2010s indie sleaze and hipster culture,” combining grunge and punk styles with the early 2000s. Think band tees paired with knee-high boots and boas. Even better, think Charli XCX’s style during her “Brat” era from the summer of 2024.

Now, is the rise of aesthetics a bad thing? In a general sense, I don’t think so, but there is an important caveat. Younger generations not having an agreed “uniform” of sorts in favor of having specific, sometimes eccentric wardrobes is completely fine. What we consider to be “normal” changes constantly, and what was normal for trends thirty years ago just isn’t normal anymore.

However, with trends moving as fast as they do, there are significant production concerns, especially the effects on the environment. Fast fashion—the manufacturing process concerned with mass-producing clothing to keep speed with trends—eats through fossil fuels with its use of polyester and contributes up to 10% of annual global carbon emissions, only for the clothes to end up in landfills at best, and our oceans at worst.

This issue, though, might be reaching its turning point. Younger shoppers are beginning to prioritize sustainable clothing practices, and the secondhand clothing market value is going up. Even when we look at Shein, one of the most notorious fast fashion brands, its downloads were cut nearly in half between 2024 and 2025. This in no way diminishes the threat and consequences of trendy and unsustainable clothing, but it might be the beginning of the way out. 

Trends have always been part of the fashion world, but once we got the internet, they became something entirely new. When nearly everyone on Earth is able to search for fashion inspiration online, you trade a handful of decade-defining styles for a thousand niche aesthetics that live on beyond their trend cycles. The earth might not have ended with Y2K, but a new world of fashion and individuality were certainly born.

The post Trends, Aesthetics, and Individuality: How the Internet Irrevocably Changed Fashion appeared first on Nightingale.

The idea

Anyone who loves their job has probably wanted to share it with their kids—get them excited about it, show how cool and meaningful it is. Even if they don’t follow in our footsteps, maybe they’ll at least respect and appreciate what their parents are passionate about.

Sometimes it’s just a dream, but we want to find a bright and engaging way to talk to our children about what we do for work.

That’s how it was for us—Alex and Natalia—working in the field of data visualization. We really wanted to share our world! Data visualization is amazing: it’s full of beauty and logic, sleek designs, a variety of charts, fascinating topics, and the chance to work with important data.

Moreover, working with data and visualization is not just interesting—it’s useful! Especially in our fast-changing world. We wanted to give children valuable skills early on so they’re ready to face the grown-up world.

We want to create shared, precious memories: to capture that magical moment when a child is still curious enough to wonder, “What does Mom or Dad do at work?”

So we thought: let’s tell and show them!

With these thoughts in mind, we started exploring the idea.

Natalia already had experience creating data viz characters and telling stories about them, but now she wanted to make stories not for adults; but for kids. Still all about data visualization. Alex already had experience writing books!

And we wanted to bring this story to life as a book!

We agreed to start the project and went off to brainstorm, sketch, and imagine!

Characters and first sketches

What’s a book without characters? Natalia decided it’d be better not to make them diagram-like people, but cute monsters or creatures. This way, they’d be easier for kids to tell apart—and we’d avoid having a big crowd of kids running around the book (great for comics, but not ideal for a storybook).

Naturally, the prototype for the girl character was Natalia’s own daughter, Maya—a curly-haired girl with red pigtails who loves bunnies. Over time, the character changed—her hair, color, and age evolved, which is completely normal.

We decided to name the girl Christine!

Then came the pie chart character. In the data viz community, the pie chart is often viewed with skepticism due to its limitations and how easily it can be misused. It’s unfortunate, because people do love the bright, round pie chart—it’s just part of reality. The trick is learning to use it well.

Our first diagram character was a pie chart, and we called him Piechi. Since pie charts need careful handling, Natalia imagined Piechi as a kind of dog that needs to be trained—not to overeat!

Everyone who learned about the book instantly loved Piechi. He became the mascot of the story and our favorite character—just like pie charts: lovable, though not always easy to manage.

Later, we started developing the Dad character, bits of the plot, and other chart-characters.
We tried several versions of the Dad—he’s a tired, somewhat sad data professional. But (spoiler!) this is so he can become joyful again by the end of the story.

At this early stage, the other chart characters were still not fully formed. But we did keep some early sketches of them too.

The plot

So, you have an idea who this book is about—but what actually happens in it?

We decided to go with a plot as old as time: a girl travels into a mysterious world of data to rescue her father, who’s gone missing within it!

Alex worked on the twists and turns of the plot, inventing obstacles and adventures, vividly describing the challenges on Christine’s path to save her dad. He also dreamed up the mysterious chart characters who not only help Christine on her journey but teach her how to use each chart properly!

Each chart has its own personality and unique “diet.” They’ll share those secrets in the book, too!

Christine bravely journeys toward her goal—a mysterious Data Tower always shimmering on the distant horizon—accompanied by her loyal chart friends, overcoming tricky challenges to discover what happened to her father and to rescue him!

Illustrations

Of course, making a book isn’t easy. We started with the plot and text. We outlined the key story points and structure. Afterwhich, Natalia did a storyboard while Alex finished writing all the text. That’s how we finally understood the storyline, the placement and meaning of illustrations, and completed the manuscript.

Then came the time to draw!

Natalia can draw, but mainly in small formats. She didn’t have experience with book illustration, and creating book artwork takes a lot of time—especially while working and raising a small child. It became clear we wouldn’t finish the illustrations in a year… or even two. So we decided to look for help and find ourselves a wonderful illustrator!

This too was a challenge—we needed a style both authors liked, someone with experience in children’s books, available time, and ideally some familiarity with data visualization.

All the illustrators were incredibly talented, though we couldn’t work with everyone. But it was amazing to see different takes on our characters—Piechi in particular got a lot of interpretations!

We tried out a few spreads with different illustrators before finally choosing Anastasiya Lykova as our lead illustrator. She has a young child herself, so the story resonated with her—and we loved her soft and expressive illustration style.

We didn’t want the book to be just a story—we wanted it to be useful too. So we included a chart chooser, and pages with profiles on each chart-character at the end of the book.

What’s next?

To start telling the world about the book, we put together a website introducing the story and its characters—the charts! Now this website has grown into a full-fledged data project for kids: Data2Kids! It includes a children’s competition, educational materials, merch, and of course, this book.

We even want to bring together a local community of data-parents and try out this format all together!

And we wanted to create more opportunities for shared activities between children and parents.

We decided to make a little workbook for kids: with fun, simple data visualization tasks, drawing prompts, unusual challenges, and ways to spend time together collecting data and making charts. The workbook is currently in development, and we’re testing the first version with our local community!

With the book finally published and a growing local community of parents and children learning data visualization alongside the book’s characters, we’re excited to launch an international children’s data-visualization competition—Data Kids!

Dates will be announced soon—meanwhile, you can already explore examples of children’s data-viz projects and educational practices from our local contest and subscribe to the project’s newsletter! 

We’d be happy to see you there! And we really hope to run more data-visualization activities for kids this spring! We also decided to create a themed workbook where the book’s characters will help children practice creating and using charts.

If you’re interested in the Data2Kids project, and want to help introduce kids to the world of data and dataviz, check our book Christine and the Magic Charts!

Thanks for reading!

We hope that, like us, you want to pass on the magic of this unusual but fascinating profession to the next generation!

The post Christine and the Magic Charts: A Data Visualization Book for Kids appeared first on Nightingale.

In the months leading up to the 2024 eclipse, experts predicted that millions of people would migrate to the path of totality to witness this extraordinary event. Tiny towns across the continent braced themselves for tourists, advising residents to stock up on food and gasoline in case of shortages. Highway signs warned travelers to prepare for extended delays. Some people who lived on the edge of totality drove two or three hours from their hometown just to experience one extra minute of darkness.

Part of the beauty of a modern solar eclipse—indeed, the only thing that makes it possible to travel to the center line—is that we understand the science behind the phenomenon. Knowing exactly where and when the darkness will hit, we can anticipate it with excitement and pleasure.

Among ancient people, for whom the sudden disappearance of the Sun provoked fear and dread, those four minutes could not have passed more slowly.

—

More than three centuries ago, in 1715, another solar eclipse hit the scene smack-dab in the middle of the Age of Enlightenment. Just two decades earlier, Isaac Newton had published his Principia, ushering in the eponymous era of Newtonian physics. The Sun, Moon, and stars—once seen as mystical celestial bodies—had been reduced to mere balls of rocks and gas, subject to the same laws of motion and gravity as the rest of us on Earth.

Newton set in motion a reshaping of the universe: from a mysterious, unknowable cosmos into one governed by data. With enough data points, early Enlightenment thinkers hypothesized they could anticipate the future movements of every object in the universe.

The 1715 solar eclipse was noteworthy in many respects. It was the first eclipse to pass over London, England in more than 500 years. It was the first time the path of totality could be mapped in advance thanks to the new laws of astronomy and physics. It was, therefore, the first eclipse to attract tourists. And the first to inspire scientific investigation. 

Astronomer Edmund Halley, most famous for his discovery of Halley’s Comet, was also a data visualization pioneer. He published the world’s first weather map, which depicted trade and monsoon wind patterns across the globe and was subsequently used by sailors as a navigational tool. He is also recognized as the first to plot two variables against each other on a Cartesian plane (as seen in his bivariate plot of barometric pressure and altitude) and the first to use contour lines on maps.

Halley saw the upcoming solar eclipse as a chance to test out Newton’s theories of gravity and motion. He published a pamphlet that claimed the darkness was neither an evil omen nor a divine event, but in fact the “necessary result of the Motions of the Sun and Moon.”

Halley’s pamphlet included a map that depicted the path of totality as seen from above—the first of its kind ever recorded and one which sparked a “golden age of eclipse maps.” 

Halley also kicked off the first citizen science project in modern history. In his pamphlet, he addressed the “Curious” people of England, urging them to watch the sky during the eclipse and record their observations: “The Curious are desired to Observe it, and especially the duration of Total Darkness, with all the care they can; for therby [sic] the Situation and dimensions of the Shadow will be nicely determin’d…”

In the end, about 25 people answered Halley’s call, sending him the times that totality began and ended in their specific location, along with a short description of what they saw in the sky. Halley himself wrote about his own experience in a mix of both scientific and poetic observations: “by Nine of the Clock . . . the Face and Colour of the Sky began to change from perfect serene azure blew [sic] to a more dusky living Colour having an eye of Purple intermixt, and grew darker and darker till the total Immersion of the Sun…”

Halley used the data he collected to correct the path of totality on his map, setting the stage for countless future scientists and eclipse chasers.

—

Leading up to the 2024 total solar eclipse, I prepared myself as best I could. I booked a weekend cabin along the path of totality, bought eclipse glasses for my whole family, and stocked up on Moon Pies, Sun Chips, and Cosmic Brownies. I vowed not to take pictures during totality, desiring instead to stay fully present and “in the moment.” After all, the eclipse would likely be the most photographed astronomical event in human history; there would be plenty of opportunities to download iconic images later.

But nothing could have prepared me for the experience of totality: four minutes of darkness, of disorientation, of complete awe and wonder. Four minutes of walking a strange, fine line between science and mysticism. Four minutes of feeling connected to birds and squirrels, to everyone else who was watching the sky at the same moment, and even to the ancient Vikings, who believed eclipses resulted from a monster devouring the Sun.

I took pictures, of course: terrible, blurry, amateur shots from my iPhone. I couldn’t stop myself—I felt an overwhelming compulsion to capture the strange sights and sounds around me and to document that I was there. 

Afterward, I couldn’t help but wonder whether other people felt that same sense of connection… or that same compulsion to take pictures. These weren’t questions of physical science, of course; nonetheless, they were questions that could be answered with data. Following in the footsteps of Edmund Halley, I sent out a call on social media, asking people to share their own photos and stories from the eclipse. Naively, optimistically, I hoped to receive hundreds, if not thousands of responses. But I’m no great social media influencer, and after posting my Google Form link everywhere I could imagine, I ended up with 62 responses—a tiny fraction of the total population who watched the eclipse. But to my delighted surprise, they represented a broad swath of locations along the path of totality and contained all the depth and complexity of a strong qualitative dataset.

Initially, I created a Google map of the responses I received, a nod to Edmund Halley’s original visualization. But I couldn’t help but wonder if there might be a different way to present the data, one that might capture what the experience felt like.

So, I set out to analyze the rich mix of words and images that comprised my dataset. Using poetic inquiry, a qualitative process developed in the 1970s by multiculturalist and feminist researchers, I engaged in thematic analysis of respondents’ written submissions. A few themes that emerged in this process included feelings of transcendence (including connectedness to nature, humanity, and God), descriptions of the weather (especially the cool temperatures that accompanied the darkness), changes in animal behavior (dogs barking, birds roosting), and a communal feeling of celebration (gathering, cheering, public festivities). I highlighted certain “poetic turns of phrase” that appeared in participants’ responses; then I cut and pasted words and phrases to create 10 found poems that each represented a shared theme from participants’ experiences. (A condensed version of the poems, entitled “Six Ways to View an Eclipse,” appears in the online literary journal Unlost). 

I also coded the photos that I received. Most people submitted some version of the Moon covering the Sun during totality; these photos were coded based on the size of the Moon, whether it was in the foreground or background, and what other elements appeared in the photo (such as people, buildings, or trees). Some photos depicted a photo from before or after totality, featuring a “crescent sun,” and a few photos included people without the Sun or Moon appearing at all. In the end, I selected 20 photos that collectively showcased all the different visual elements that appeared in the dataset.

In thinking about how to visualize this data, I wanted to create an opportunity for viewers to interact with the photos and poems in a novel way. After brainstorming several installation ideas with the team at Fusiform Props and Exhibits, I finally settled on the idea of printing the photos and poems using a special technique called lenticular printing. Lenticular printing is a technology that uses plastic lenses with ridges on top to display multiple, interlaced images at one time. The different images float in and out of visibility, depending on the angle from which the print is viewed.

Each of the final lenticular prints consisted of two photos and one poem, thereby displaying the words and images from multiple participants at one time. From April to June of 2025, the 10  prints appeared as part of a larger exhibition, entitled “Data Is Poetry,” at Artspace in Shreveport, LA.

During the opening reception, I watched as people walked past the prints on the wall. At first, most people strolled past casually at first, then did a double-take after realizing that the prints contained “hidden” images and words. They proceeded to adjust their own position, moving forward, backward, and side to side as they tried to see (and read) all the layers in the image. 

I was reminded of my own experience from a year earlier and how earnestly I had watched the sky through my eclipse glasses, looking for the slightest changes in the Sun as the Moon passed in front of it. The data visualization, therefore, mirrored the eclipse itself—an astronomical phenomenon that shifted with mathematical precision based on angles and movement. 

But the visualization also effectively symbolized our shared experience of the eclipse. Though all of the participants in the project had shown up for the same event, their view was necessarily determined, and limited, by their specific location and context. Only by compiling multiple viewpoints could we see the composite: a collective phenomenon that was as human as it was cosmic.

The post In the Shadow of Edmund Halley: Solar Eclipses, Citizen Science, and Qualitative Dataviz appeared first on Nightingale.

I lived in London for a decade, the final three and a half years spent in Brixton. There, I would frequent two distinct green spaces. Brockwell Park, a large public park where the neighbourhoods of Brixton and Herne Hill meet; and Brixton Orchard, a small community garden situated directly opposite Lambeth Town Hall. It was during this period that I developed a keen interest in community-focused urban green spaces. In the winter of 2023, I left London for the densely populated city of Taipei, the Taiwanese capital. I moved into an apartment within walking distance of Da’an Park, the city’s central green expanse, but the true joy was the city itself: greenery along every street and tucked into every corner with plants of every species tumbling down off balconies. From my kitchen window, I could see the man who lived in the top floor apartment opposite had cultivated a veritable jungle atop his roof, in which he would emerge daily at sunset to water his plants and sit quietly on a bench he had nestled under his palms. This set-up is quite common in Taipei. As Clarissa Wei described the city for The New York Times, it is “a literal urban jungle—ferns and large elephant ear plants sprout through the crevices of roofs and sidewalks with wild abandon”. Across the street from my apartment, a pocket-sized neighbourhood park was a constant theatre of intergenerational life, teeming with both children and the over-seventies. I was surrounded by nature in the heart of a capital city, and I loved it. 

Urban greening, or green infrastructure, is the deliberate integration of vegetation—street trees, parks, green roofs, and living walls—with urban development to provide ecological, environmental, and cultural benefits. The protection of nature in urban spaces is essential for sustaining natural ecological cycles, and provides crucial cultural ecosystem services: it softens the harshness of urban infrastructure, ensures a critical connection to nature, improves general wellbeing, and fosters social interaction. Taipei relies heavily on green spaces of all types for public life. The city serves as a compelling case study for urban greening in compact cities, defined by its “top-down” planning approach born of its limited land area. A study by Peilei Fan, professor of urban and regional planning, and colleagues, found most neighbourhood centres and one subcenter in the city “exhibit both high compactness and good green accessibility”. The central city rests on the ancient Taipei basin, bounded by major rivers (like the Tamsui) with steep, mountainous terrain rising abruptly on most sides of the basin. Other examples of compact cities in East Asia include Hong Kong and Singapore. London, by contrast, is a city that grew organically over centuries, resulting in its pattern of “urban villages”.

Since 2010, Taipei’s urban planning policy has shifted its focus from a broad, visible green strategy toward practical green landscaping schemes. This strategy gained momentum around 2014 with the emergence of an urban regeneration programme and the prioritisation of the development of small green spaces, such as river corridor greens and pocket parks. Through government funding and the lease of state land, the Taipei Beautiful Programme and the Open Green Project have delivered the development of small green spaces across the city. A complementary effort, the Garden City initiative further promoted urban agriculture, including citizen farms (allotments), community plots, and rooftop gardens, particularly on school buildings. I used open source data from the Taipei Government Open Data archive to illustrate the development of green spaces in Taipei City over the last fifty years. I first translated the raw data from Mandarin, followed by exploratory analysis using Python. I plotted the time-series data as a raster-style graphic, aiming for an aesthetic that evokes an interconnected urban ecosystem. I was interested in mapping the locations of developed green spaces, and I relied on data from the Taipei City Water Green Space Atlas. I manually classified all the green spaces listed in the atlas according to the definitions of the Garden City initiative.

Effective data visualisations and well-planned urban spaces seamlessly blend aesthetics and utility, aimed at enriching public experience and understanding. In data visualisation, this means selecting graphs, layouts, and interactive elements that communicate the underlying data with clarity to engage the target audience. Similarly, in urban greening, this translates to designing spaces and infrastructure that provide ecological benefit and prioritise the needs of the local community. Form follows function. Munari viewed the designer as a mediator, bridging the gap between expert knowledge and public life: “The designer of today re-establishes the long-lost contact between art and the public, between living people and art as a living thing.” Without effective design in data visualisation, the data remains inaccessible. With it, visualisation can empower the public to understand, question, and engage with critical issues. Urban greening, by its very nature, is public-facing. The design of urban green spaces is about making a city a usable and sustaining environment.

Of course, data visualisation can also act as a bridge between academic research, expert knowledge, and public understanding within urban greening contexts. As someone whose academic background is in Neuroscience, and not Urban Greening, I write this from the perspective of the public that these policies need to engage; the public must be integral to the decision-making process for the planning and design of their local green spaces. The ‘Citizen Dialog Kit’, an open-source toolkit, was developed to leverage situated visualisation within public spaces through a set of interactive, wirelessly networked displays. By making local environmental data visible and understandable, it invites discussion and feedback from diverse community members who might not typically participate in traditional planning meetings. Residents can see the tangible results of past efforts and then contribute to ongoing dialogues about future greening priorities. In a recent study, socio-environmental scientist Thomas Mattijssen and colleagues presented a participatory application of GIS that bridges the gap between data-driven and citizen-centred urban greening. The authors used spatial modelling in community workshops where residents contributed local knowledge to enable researchers and local citizens to jointly identify greening criteria, translate them into indicators, and pinpoint potential greening locations. This accessibility fosters democratic participation in environmental decision-making.

Visualisations serve as a powerful tool to engage community members and translate complex datasets into compelling narratives, increasing both understanding and acceptance of environmental initiatives. For example, RisingEMOTIONS, a data physicalisation and public art installation situated outside the East Boston Public Library in 2020, aimed to engage communities directly affected by sea-level rise, encouraging their participation in planning adaptation strategies. Looking ahead, emerging technologies offer significant opportunities to enhance the role of data visualisation in urban greening, and more broadly, climate policies. Innovations such as AI-assisted personalisation, mobile technologies for context-aware experiences, and real-time environmental sensing can amplify its impact, creating tools—imagine an app that allows a Taipei resident to visualise the projected, real-time impact of a new pocket part on local air quality—that make planning tangible. For a comprehensive look at how data visualisation can be leveraged to address sustainability goals, a recent article by social computing professor Narges Mahyar provides an excellent review.

In adhering to Munari’s principles, where design prioritises functionality, clarity, and intrinsic beauty, the design of data visualisations and urban green spaces finds its highest purpose in its utility to the public: “Art shall not be separated from life.” At their best, both disciplines demonstrate that “beauty arises from functionality”—whether in the efficient form of a bar chart communicating a climate trend or the purposeful design of a pocket park managing water runoff—both are functional in their communion with the public. Moreover, data visualisation can become an indispensable tool for urban greening. By effectively communicating complex planning concepts and policies, visualisation fosters community involvement and informs policy decisions that directly benefit local communities.

All images designed and provided by the author.

The post From Pixels to Parks: The Intersection of Data Visualisation and Urban Greening appeared first on Nightingale.

I was 14 then, and football was more than a mere sport. I vividly remember my mornings began with practice before school, lacing up my shoes. Yet, in those training sessions, I often felt the weight of being different. Few girls around me cared about sports, but even fewer about football. I envied the boys, who seemed to belong to a world I was desperate to be part of. Hoping one day I’d witness women players in the limelight.

Jump to the year 2025, women’s football is gaining its momentum. The current UEFA  Women’s Euro is  the largest event as the attendance has been record-breaking. Over 600,000 tickets were sold, setting the stage for the most attended Women’s Euro ever.

Before it was Messi, Cristiano, Maradona, now young football enthusiasts can look up to Aitana Bonmatí, Alessia Russo, Sam Kerr, Alexia Putellas, Patri and a whole bunch of amazing women footballers.

Decoding Aitana Bonmatí

Football is a sport that requires immense strategic, physical, and mental endurance. One of the most prominent names in women’s football is Aitana Bonmati, who began her career at the age of 17. Aitana Bonmatí won the Ballon d’Or, which is the most prestigious individual prize in both men’s and women’s football, not one but three times in a row. Aitana Bonmatí grew up in a home where standing up for what she believed in was simply part of life. From the start, her parents made a bold choice to challenge tradition, placing her mother’s surname before her father’s. In doing so, Aitana became one of the first in Spain to carry her mother’s name first—a quiet but powerful statement of equality that would shape the person she was destined to become. She was the lone girl in the boys’ team.

Bonmatí exhibits extraordinary technical finesse, seamlessly maneuvering the ball through tight spaces while maintaining constant awareness of her surroundings. Her vision and spatial intelligence empower her to dictate the tempo of matches. Moreover, her relentless off-the-ball movement and impressive stamina consistently create opportunities for her teammates and drive Spain’s dynamic, high-intensity style of play.

“We need to have women in more powerful positions that are making decisions, so when that 10-year-old girl is looking up and wondering, ‘What can I do and what do I want to be when I get older?’ She has the opportunity to do and be whatever she wants.”

 — Abby Wambach

The debate over gender in sport is nothing new, and football is no exception. For decades, women have been underrepresented, underpaid, and held back by systemic barriers. While the popularity of women’s football has surged in recent years, it still doesn’t rival the global dominance of the men’s game. But on a positive note, female footballers are becoming global superstars and commercialisation is growing in all areas of the game. The 14-year-old girl in me would be overjoyed to see her favorite sport shattering barriers and achieving unprecedented growth.

The post Breaking Stereotypes in Women’s Football appeared first on Nightingale.

The experiment

The goal of the experiment was to find out if age impacts engagement in interactive data stories. We recruited 2,400 participants from the UK and split them into four age cohorts. We did not use names typically connotated with generations to avoid stereotypes. Instead, we called each age cohort by their age range: 18 – 27, 28 – 43, 44 – 59, and 60+.

Three interactive designs

The researchers tested three interactive data story examples, each inspired by award-winning publishers:

1. “Make a Guess” (Design One): this design asked users to guess an answer to a question and then surprised the user when revealing the correct response. Inspired by the New York Times ‘You Draw It: How Family Income Predicts Children’s College Chances’ 
2. “Breaking the Fourth Wall” (Design Two): this visualization personalized the experience by asking users to input their name and then integrating their name into the narrative. Inspired by ABC Story Lab ‘See how global warming has changed the world since your childhood’ 
3. “Exploration” (Design Three): this reader-driven design lets users click through interactive cards, encouraging free exploration to create their own narrative. Inspired by The Pudding ‘A Visual Guide to the Aztec Pantheon’

Participants interacted with one randomly-allocated design, answered a 22-question engagement survey, and provided optional qualitative feedback.

Key findings

The study’s quantitative analysis confirmed a statistically significant difference in engagement scores (p = 0.03), with the 18 – 27 cohort (mean score: 112) more engaged than the 60+ cohort (mean: 109). 

• Younger Audiences (18 – 27): As digital natives, they noticed interactivity, mentioning it multiple times in their feedback. They comprehended the intent of engagement inside the data story where, for example, they observed that Design Three allowed for data discovery through exploration. Aesthetics also mattered; criticism of “dated” colors or “cluttered” layouts was common, with 12 instances for Design One alone. They preferred concise, gradually revealed text, making information digestible and intuitive.
• Older Audiences (60+): This group faced usability challenges, reporting confusion or distraction from interactive elements like scrolling. They desired more contextual text to fill knowledge gaps, with multiple instances requesting additional information. Their lower engagement scores reflect these barriers. Furthermore, this age cohort mostly did not observe the intent of the data story author to use interactivity to engage. There was comparatively little mention of interactivity with this age group.

Design recommendations

For authors, these findings translate into practical strategies to create inclusive interactive data stories that resonate across age groups:

For Younger Audiences

• Embrace Bold Interactivity: Leverage narrative design patterns like “Make a Guess” or “Exploration” to engage tech-savvy users. Explicit interactive elements align with their expectations, boosting engagement by making them active participants.
• Prioritize Aesthetics: Clean, modern interfaces with vibrant color palettes and minimal clutter are critical. Avoid outdated or busy designs, as younger users are quick to disengage when aesthetics fall short.

For Older Audiences

• Simplify Usability: Ensure interactive elements, like scrolling or buttons, are intuitive and clearly marked. Complex interactions can lead to missed data, undermining the narrative’s message.

Provide Context: Offer optional detailed text to provide background information, addressing potential knowledge gaps without overwhelming the primary narrative.

Why this is important for authors of interactive data stories

This study empowers authors to move beyond one-size-fits-all designs. By understanding age-related preferences, you can craft data stories that resonate with your target audience. For younger users, it’s about creating visually appealing experiences that leverage their digital fluency. For older users, it’s about reducing barriers and providing context to ensure accessibility. The study’s emphasis on inclusivity aligns with the broader push for universal design in visualization, ensuring no audience is left behind.

Moreover, the findings challenge assumptions. Younger users don’t just “crave” multimedia—they engage deeply when interactivity is purposeful. Older users aren’t inherently tech-averse; they simply need designs that prioritize usability. By applying these insights, authors can create visualizations that not only inform but also inspire, fostering engagement across generations.

N. Errey et al., “An Age-based Study into Interactive Narrative Visualization Engagement,” in IEEE Computer Graphics and Applications, doi: 10.1109/MCG.2025.3591817. Read a preprint available here: https://arxiv.org/html/2507.12734v2

The post Why audience age matters: The Influence of Audience Age on Engagement with Interactive Narrative Visualization appeared first on Nightingale.

In metropolises, socioeconomics, demographics, and the built environment shape geographic identity–even stepping across neighborhoods can feel like “the entire world [has been] remade.” In 2015, researchers also found that personality traits vary geographically across Greater London. Big Five personality traits, the most clinically accepted framework for understanding personality, clustered across neighborhoods and by proximity to the city center. These results beg the question, does New York, too, vary geographically in personality type?

To test this theory, I recreated the BBC London survey and shared it across every NYC neighborhood subreddit. Curiosity alone drove more than 2,500 people to take the survey, resulting in enough data to surface early insights. These results revealed two key takeaways:  (1) neighborhood personalities differ significantly from the citywide average and (2) neighborhoods are distinct from one another–though more data is needed to sharpen the picture. New Yorkers can see their results by taking the survey now.

The above map shows the correlation between NYC neighborhoods,  Big Five personality traits, and life satisfaction, with color signifying how far a neighborhood’s traits differ from the citywide mean. Note that (1) some, but not all, of these differences are statistically significant, and (2) the results may shift as the dataset becomes more complete. This early evidence encourages me to continue filling out the map.

When used ethically, data like this has a wide range of applications from location-based marketing, retail site selection, or even building a StreetEasy competitor that connects people with neighborhoods based on personality.

I’m keeping the survey open and actively seeking more responses. You can check back here to see how the results evolve over time.

The post What your New York Neighborhood Says About You (Backed by Data) appeared first on Nightingale.

Figure 1: (a) left: a solitary tree standing erect on a sunny day, (b) middle: a solitary tree swayed on a windy day, (c) right: a collection of trees on the same windy day

On our way to the university, we go past trees shown in Figures 1b and 1c. The other—featured in Figure 1a—was one one of us found while hiking in the high Himalayas. What differences do you spot? It is these differences—the way they stand (some ramrod straight, some swayed; some on their own, some along with friends) or the backdrops against which they are set (one was a bright, sunny day, the other gloomier in contrast) that could, if we are willing to notice, point to memorable peculiarities in more serious settings, such as creating a time plot. Tasked with depicting the evolution of variables, modern analysts’ default gravitation is towards line diagrams shown in Figure 2. The (fractal) trees we propose, by winning you out of that culture-based addiction which becomes tedious far too quickly, help you focus on the contrasts between two phases—adjoining or not—of the evolution, and retain that observation far more solidly.

Winds that whisper degrees of change

What are we offering? A platform on which the interaction between two phases in a time series may be staged through a tree. This tree is made using properties of both phases. The extent to which one phase is different from the other is shown by the way the tree sways. And the force that is doing the swaying—possibly a gust of wind—is suggested through the amount of leaves that get torn away. The backdrop expresses the storm that triggers this wind. And all this, collectively, showcasing action, lets readers feel the amount of difference between the two phases examined and the “winds of change” that are responsible, against their faces.

Figure 2: Time series showing usage of common idioms over the years. Structural breaks are marked by vertical separators. A tree is planted (in Fig. 3 below) at a “present”: a place where one phase ends and another begins. In general, these phases need not be separated by change-points.

To underscore a comic and impish possibility, we choose to demonstrate all this through n-grams made out of idioms involving “winds” or “change”. Please see Figure 2, but the methods apply just as well to any other time series. These are (scaled) numbers of times these phrases were deployed in books and media over the years. The task at hand could be comparing how the usage has changed over two sections of history. Some structural breaks are expected, that is, some upticks can be explained: the rise in “the winds of change” around 1990 correspond to Scorpions’ release of the song commemorating the fall of the Berlin Wall, the one in “blowing in the wind” to Bob Dylan’s one around 1963, the one in “candle the wind” to Elton John’s 1997 rendition, in loving memory of Princess Diana. There are weird people—and you are in the clutches of two of them—who nitpick endlessly on where these break points should be. We will spare you the technical details for now, but there is mathematical backing behind the vertical separators you see on Figure 2. Although the phases to compare need not be defined by change points, we choose two such phases to bring out the differences most glaringly. So, for us, the pre-change phase is 1975 – 1988 and the post-change one 1989 – 2000.

Figure 3: trees showing how the pre-change phase (1975-1988, on the left) clashes with the post-change phase (1989-2000, on the right) for (a) left: “winds of change” and (b) right: “blowing in the wind”.

The intent is to construct a tree that, through its very presence, would narrate both the extent and the type of shift a time series went through as it transitioned from the pre-change to the post-change phase. The placement of the tree would point out an imaginary present that would create a past-future split. Every shift in the ongoing intensity of idiom-usage may be thought to be caused by a breeze flowing from the region of higher intensity to the region of lower intensity, making the tree sway in the direction of its movement. How much the tree would sway depends on how strong the wind.

A look at the first natural tree (Figure 1a), for instance, would suggest the absence of a wind, making the past and the future this tree was trying to separate, more or less equal, in properties. In contrast, the second natural tree (Figure 1b) would suggest a strong wind blowing from its left, or the past, implying a higher intensity on the pre-change phase as opposed to the post-change one. In our n-gram examples, the average usage of an idiom over a period may serve as its intensity. These are marked for “winds of change” and “blowing in the wind” on Figure 2. For both those cases, if we focus on 1988, the post-change intensity was more dominant. This is why both the resulting trees in Figure 3 sway to the left under the influence of winds blowing from the right. The post/pre-change intensity ratio guides the way the trees grow. If this ratio is more than one, the twigs twist to the left, giving off the impression of a breeze blowing from the right. If the ratio is extremely more than one, the twigs twist quite a lot. Similarly, they twist to the right in case the ratio is less than one. For instance, for “winds of change,” this ratio is 2782/1764 = 1.577 while for “blowing in the wind”, it is 999/618 = 1.616. “Blowing in the wind”, therefore, endured the bigger change, as shown by tree 3b, through its stronger tilt to the left, in comparison to 3a. Regardless of the type of wind blowing, the trees, through their structures, can reveal fluctuations among the numbers in each phase. In case the fluctuations are similar—such as for “winds of change”: 361/249 = 1.449, Figure 3a—a twig, at each point of split, will not deviate too much from its sibling, suggesting an overall solidity, condensing the similarity of variation among the pre- and post-change values. In case the fluctuations are different—such as for “blowing in the wind”: 237/109 = 2.174, Figure 3b, the deviations will be more, suggesting a structural instability, or a flexibility, representing the unsureness the tree has, in deciding how to sway.

These trees, and their positioning, therefore, arrest drifts over non-participatory or uncontrollable time domains, freezing temporal flows (Figure 2) to geographical invariables: the past fixed to their left, the future, to their right (Figure 3), making obvious, through repetitions (next section) small shifts that may go unnoticed in ordinary line diagrams.

Repetitions, refinements and absurdities

A tree could, therefore, be an apt instrument to witness first-hand both the extent and nature of turmoil phases of a time series tolerate. Once a simple tree is erected, principles of good visualisation may be deployed to better its potency. One such is the principle of repetition. The breaking up of twigs may be done many times—each time following the same rule of tilt and spread (outlined in the previous section)—to stress the large-scale or eventual impact (through the leaves at the top that these subdivisions trigger) of even small differences between two phases. Here, they lead to fractal-type self-similar structures, but in our earlier essays, we have used the same principle of repetition to amplify or make blatant minor dissimilarities unearthing non-fractal settings. Robert Fathauer, a renowned sculptor based in Mesa, Arizona, is alleged to have quipped “Humans have a huge ability to get better at complex tasks with repetition”. We have paid heed. Those interested may bring in further tweaks or other modifications. The number of repetitions may be employed to suggest the difference in the lengths of the pre- and post-change phases.

Figure 4: Tighter (i.e., slender or narrower) trees result when the pre- and post-change volatilities are similar. Even against the same background, it is possible to think of trees of varying spreads.

We can exploit the background to incorporate another piece of detail. It could be something endogenous or internal such as the extent of change (the absolute or relative differences in the pre- and post-change intensities, instead of the ratios shown in the previous section, in case the extent of tilt is not revealing enough) or something exogenous or external such as the average number of times similar phrases were used in non-English texts, providing a context or reference, in a way. The darker the background, that is, the more ominous the sky, the stronger could be the breeze blowing. Much like the gloomier day shown in Figures 1b and 1c. We have sampled, in Figure 4, situations that show how different trees can sway in different ways on days that are equally sunny or gloomy. The difference between the background sky and the foreground tree-wind combination can be highlighted through the number of leaves being torn away. The higher the friction, the more the number.

Figure 5: On absurdity. Adjacent trees are expected to experience the same environment and consequently, sway similarly, in unison. This, however, may change when one tree condenses a specific change environment for one time series, the other, for another, with the two series sharing some underlying commonality (such as a grand average magnitude of change) shown through the common background or sky.

Absurdity is valued in many ways. Edward Tufte, in his work Visual Explanation, makes reference to  Mark Tansey’s 1984 painting The Myth of Depth, which shows many characters on a boat adrift amidst an ocean, while one, Jackson Pollock, walking, as if miraculously, on water, and, as if, stressing the title or the purpose behind the painting. In our context, once a sufficient definition of a background is worked out, similar phases from many different time series can be placed next to each other (much like the collection of trees in Figure 1c) against a common background triggering visual absurdities shown in Figure 5: neighbouring trees, though of potentially differing spreads or canopies, are expected to sway in the same direction much like they did on iFgure 3. This is contradicted when, even against a common backdrop (representing a pooled average change), the trees are employed to show not just differences between phases of the same series, but between pairs of corresponding phases of many.

When it comes to graphing time series, we believe it is not a failure of execution but a collapse of taste that brings an art to eclipse. The dashboards we offer showcase many alternatives, combinations and many ways of creating a tree. Each, through expressing motion, engineers a sense of reality. The backdrop changes. The sky brightens or darkens. A weak or a strong wind billows. The tree, swayed at times, however, stands resolute overall—a silent sentinel observing the switching of moods. And, faithfully, the foreground-background combination chronicles the unending skirmish between the fixed and the fugitive.

Acknowledgements

This work was supported, in part, by the Research Enhancement Grant awarded to the second author by the American Mathematical Society and the Simons Foundation for the 2024 – 2027 cycle. Bentley University’s summer research grant is also gratefully acknowledged.

The post Winds of Change and How They Sway Fatalist Fractalists appeared first on Nightingale.

On the day of the event, I arrived 10 minutes before the allotted time, parked my very reliable Honda CRV, and entered the school. The teacher greeted me at the door and we took the stairs to classroom 4-B. Just before entering the class, I said a little prayer and entered the room.

It was a different step, very much so from what I am used to in the ‘corporate world.’ There were no phones, no laptops; these kids were actually talking to each other. As I entered the room, the attention shifted and now all eyes were on me. I could also feel my heart beating a little faster.

“Are you Mr. Gulrez?” one kid asked.
I cleared my throat and said, “Yes, I am. Do you know me?”
“I have your book and it has your picture in it. You were young.”

I realized that picture was taken some 10 years ago (even before these kids were born..lol) at Microsoft when our leadership called a professional photographer for a headshot in Building 5. 

I laughed (at myself) and thought about shifting gears. “Should I open my laptop and walk through the slides I created?” I asked myself. However, “Wouldn’t that be boring for these little kids?”

“Achoo!”

The sneeze came from Anas, sitting in the second row.

Then, two more: “Achoo! Achoo!”

I walked to the whiteboard, and wrote…

I turned around and the kids were laughing. Anas looked at me and shrugged. 

“Do you think this is data?” I asked.

Laughter erupted. One student whispered, “That’s not data!”

“Why not?” I asked.

Just then, Anas (enjoying the spotlight) added one very obvious fake sneeze.
I turned back to the board:

More laughter. Now they were hooked.

I asked, “Is anyone here good at drawing?”

And everyone raised their hand…. (hmm interesting. When I ask the same question in my corporate workshops, adults often say, I used to draw as a kid. What changed?)

Little Sara came up and drew a stick figure of Anas mid-sneeze, complete with dramatic “ACHOO!” bubbles.

Then we created—Sneeze Graph, Snore Graph, Crying Baby Graph and so on—the classroom was alive with curiosity, and creativity. 

While I couldn’t capture the video of my session, I recorded this episode to share the topics I covered and how to make it engaging for the kids.

By the time the class was over I saw the little kids completely engaged. They didn’t realize it, but they were learning to think like little data scientists, asking questions, capturing observations, and telling stories through visuals.

At first glance, it may look simple—but this playful activity opened the door to some powerful lessons for both kids and adults.

We explored patterns in snore and sneeze sounds, forecasted the pitch of the next snore, and even spotted a few outliers. And the best part? The kids were completely hooked.

Data doesn’t have to start with spreadsheets. It can start with a sneeze…;)

Your turn to play

I hope this gave you a glimpse into how to introduce data to the younger audience…simply by using everyday moments and data from our daily lives.

In my book Drawing Data with Kids, you’ll find many more simple, playful activities that families can do together to explore data, ask questions, and learn in a fun, hands-on way.

Learning data doesn’t always need a screen. We can use just a little curiosity, crayons and make it fun.

The post Data Doesn’t Have to Start with Spreadsheets. It Can Start with a Sneeze. appeared first on Nightingale.

You have a clear purpose but the data isn’t quite right for it

I regularly walk through the Turnpike Lane Bus Station, there’s a pretty big sign pointing to it. It’s a major node for North London public transport and yet, a few years back, I found out that it did not exist… in the data, at least. I used to run the official data set of bus stops for the UK Government—a rather obscure dataset that made its way into powering a few popular journey planners like Google Maps and City Mapper. 

This was 2020 during COVID, and one of my colleagues wanted a list of all bus stations in the country in order to send posters which advertised social distancing. While the dataset contained over 500,000 points, it did not contain this bus station. The problem data definitions: the dataset listed bus stops, which were not the same things as bus stations. While the words “bus station” have a common sense meaning in our minds as a collection of bus stops, that meaning was not translated into the dataset. The individual bus stops making the bus station are all in the datasets, except there was no way to group them together other than trying to infer they’re part of the same bus station because of their proximity. 

I found other interesting issues in the dataset. Some were easy to spot, like bus stations in the middle of the North Sea. Other stations were a few meters away from their real location, which would not have a huge impact unless we were trying to use the dataset to get self-driving buses to park automatically. So, why weren’t these groupings captured in the first place? The process that created and populated the data never asked itself “are we capturing everything that we need about this bus stop?”. As a result, the dataset wasn’t quite fit for the purpose we were looking to deliver. The translated definitions of common sense concepts into data is a major element of making sure that a dataset is usable and stays current, and having a process that allows that question to emerge is an ingredient of good data management. At the time, to my surprise, we didn’t have either. 

Disappointingly, data that may appear suitable to your purpose is not always; and if you are in the fortunate position of being the owner of a dataset, always ask: are there any use cases that would be out of scope for this dataset, and is it worth expanding?

Sometimes the data is really incomplete or missing

W.E.B. Du Bois is widely remembered for his infographics about the conditions of African Americans at the end of the nineteenth century. What I always hail him for was having shown that a lack of data should not stop a good data project and that sometimes the hard work is putting data together. When he realised the US Census lacked data about African Americans, he assembled his own survey and team,collecting data that resulted in his now famous infographics. Incomplete or missing data is something that I’ve regularly had to cope with and decide whether to pursue the initial project or pivot to something different. Once again, during the pandemic, we were trying to see if there was a way to check the density of people on pavements, and entered a tunnel trying to find the accurate measurements of pavements for the whole of the UK—an impossible task. This is when I realised using a proxy would have given informative enough results, as did The Economist in the chart below, created by collecting, over time, Google Places “busy times” for major points of interest in major cities. Simple, effective, but not based on anywhere close to “complete” data.

Sometimes missing data should make us reflect. In one of my projects while working for public healthcare in the UK, a team of dermatologists came asking if my team could develop an AI algorithm to grade a type of skin condition. The intent was very positive: in their clinical research, they realised human medics were biased, resulting in less accurate grading for people who are not white, and were looking for AI to help correct that bias. We found that the collection of images we could find about this condition were themselves biased, so any AI model trained on them would have not addressed the issue. The image below captures what dermatologists call the Fitzpatrick scale—the official measure of skin darkness. 

We realised we had as many images as we wanted about Fitzpatrick scale I, and increasingly less as we went towards scale VI. Developing a model would have been unethical, and we and the team of dermatologists agreed to pivot and start collecting an unbiased dataset. This experience taught me to reframe success: we successfully detected bias and used the tools at our disposal to try and correct it. Unbiased data means more ethical and accurate models. During this project I learned a lot also about the concept of intersectionality, thanks to data journalist and humanizer Donata Columbro, who suggested in her book “Dentro l’algoritmo” (2022) that in order to understand and correct bias in data, you can apply intersectional thinking as the framework that analyses the embedded injustice in the administration of power.

Just use the official data and you’ll be fine…

…or so they said. One possible critique of du Bois’ work is that it wasn’t based—for good reasons, as we’ve seen—on the official data. We also use the words authoritative for data that comes from official administrations, suggesting it has a superior authority; yet that’s not always the case. A personal example of this is the chart below, from the Financial Times, representing how UK parliamentarians increasingly mention the words “NHS” (National Health Services) in their speeches. It’s personally interesting for me because if you look at the source, it references “Parli-N-Grams,” a website I built that allows that analysis. I always make the joke that I called my mum to say “mum, I’ve become a source.” What’s going on here and what’s the lesson?

Well, while I’m obviously flattered that the Financial Times used my website to make this analysis, it is remarkable that I’m not the official source of the data. In fact, I’m not even using the original source myself, as I used the easily usable extracts released by TheyWorkForYou, a project of civic tech charity mySociety. This is because the original data is really hard to use, so data journalists are discouraged from doing so. This is clearly a minor problem in an article like this, but it makes you think about the potential for news manipulation. In my job in the public sector, I always think about how I can make the data as usable as possible.

There are cases where it’s just simply wrong or misleading beyond authoritative data not being ideal. The most famous case is that of borders and their representation in data and maps. There are plenty of border disputes around the world, including in unexpected places. For example, France and Italy are broadly at peace but take different views on where the border around Mont Blanc falls, to the point that the official map of Switzerland is more accurate than those of the two countries. And Switzerland itself has a three-way border dispute with Germany and Austria which has reached several courts. Representing all these fuzzy borders in data and maps can be difficult and it can lead to litigation. Mikel Maron, once head of the Open Street Map Data Working Group, tried to address the issue of maps around Jerusalem, where there are obviously two famously different views about borders. His message “Jerusalem is an edge case of everything” sadly resonates, but I’d take the slightly less optimistic view that it is not just Jerusalem to have edge cases—our reality is full of edge cases, and representing it in data is difficult and imperfect.

Communicating data should clarify reality

We use a lot of data to back our statements with evidence. But is data easy to understand for people who are not trained? And as data journalists and advocates, how can we make it easy to understand? Often this is, again, a problem of clarifying definitions. I often challenge my audiences to raise their hand if they know how to calculate the average; most people will raise their hand. I ask some people how, and they will tell me the well-known formula of the arithmetic average—sum all items and divide by the number of items. And here’s the catch: in many official statistics, we don’t actually use the arithmetic mean. 

In the UK, the Office for National Statistics uses the geometric mean to publish the average house prices. Most people don’t remember the formula nor its meaning; some people might even be unaware of its existence. Other times, the definitions we use are disconnected from people’s reality and, while they make a lot of sense from a statistical point of view, they might not chime with people’s common sense understanding of a problem. For example, the official definition of “employed” in the UK is “a person aged 16 years or over who did one hour or more of paid work per week in the previous week”. Having worked an hour per week might not meet the expected definition of “employed” of someone without statistical training;  this can trigger confusion. The lesson I’ve learned through engaging with using these examples is that communicating data requires us to clarify definitions, explain why they were chosen & why they were better than the alternatives, and clarify the meaning of the data we are publishing.

This also means, sometimes, recognising that the data is not neutral or impartial. See the two maps below. Would you imagine that they are based on the same data?

They are both based on the data from the Italian General Election in 2018, which was a particularly contentious election with three coalitions, and all outcomes were possible according to the polls, due to the intricacies of the electoral system. 

The map on the left is one I made. It’s a dot-map, so it shows a dot for every several thousand votes—blue for the right-wing coalition, red for the left-wing coalition, yellow for the non-aligned “5 star” movement. The story I told with this map was that Italy was an electorally confused country; it does indeed look pretty confused on the map.

On the right you see the map that the Financial Times released, a much more beautiful map than mine! You see the stark difference—the FT team coloured the map based on the winner in each constituency, getting a country that is pretty much split into three parts: centre-right in the north, 5 star in the South, and the centre-left only featuring in its strongholds around Romagna, Tuscany, and less expectedly in Sudtyrol. This map illustrated a story about Italy being a divided country. 

So which is it, was Italy divided or confused? The lesson here is that data-driven doesn’t mean neutral or impartial. There’s an agenda in every data use, sometimes unwillingly. In this case, the “agenda” is the fact that the data is initially collected for one purpose which gives it an initial “spin”—administering an election—and then we make a choice to tell a story that we detect in the data, which gives it another spin.

Enter production: data-driven services

Creating live systems, where data or models concur in delivering a service, has been one of my areas of work. Here, the basic problems are the same: using the right definitions, making sure we remove bias, etc, but there are some lessons specific to the live nature of the data. For example, you might have heard of the concept of model decay—if you create a predictive model based on data and the specific problem is one where data is generated frequently, then the model will slowly lose its predictive power. Correcting this requires strong collaboration between experts of the problem, data scientists, and data engineers. I’ve seen this in an interesting project where we used neural networks in order to predict how long a patient would likely stay in hospital at the moment of presentation. While the model was pretty accurate at the point of creation, having been trained on over one million patient admissions and capturing over 300 data points per admission, it would have quickly lost its predictive power without allowing for re-training in the live pipeline. 

This project was also remarkable for another lesson that became apparent: asking “so what” about every data-driven project. The problem here was that, despite the model being very accurate, we had no idea how to use it in practice. What action would doctors and nurses take that was different thanks to the prediction? We couldn’t work it out. And there lies the lesson: any data-driven model needs to be built together with those who will use it. Capturing user needs with adequate user research, bringing subject matter experts together with data scientists, is the only recipe for success. 

This brings me back to my initial point: data is multidisciplinary. The last example would have been much more successful if we had, from the outset, worked not just with data experts but also with those running the hospital operations—nurses and doctors—who would have guided our development of the model more effectively. 

I hope that sharing these lessons will help. Working in data, whether it is data visualization, data analysis, or creating data-driven systems, is fascinating, but it really requires a lot of knowledge that comes from different areas: technology, maths and statistics, ethics, law. The best data practitioners use all these to challenge their assumptions and become better at their data uses.

The post The Bus Station That Didn’t Exist, and Other Data Epiphanies appeared first on Nightingale.

Expecting parents are inundated with information about newborn health and all of the potential maladies that can befall them. One of the many categories to worry about is the baby’s postpartum weight gain. Parents are taught the baby will need to eat every two to three hours around the clock and that they should return to their birth weight after about two weeks lest they head down the path of acquiring the heart-wrenching label “failure to thrive.” To try and head off any concerning trajectories, parents are advised to meticulously track every feeding session whether nursing at the breast or with a bottle.

Cycles

Our son A was born February 26th, 2023 at 2:43 AM. One of his first experiences in life after filling his lungs with air was my partner Becca nursing him on her left side for 10 minutes. Even to our new-parent eyes, we could tell A was an “active” baby. 

From that first day on, Becca would nurse A every two to three hours, and I would record the time and the feeding session duration or bottle amount on a clipboard. Each day was a churn of nursing, charting, and finding the time in between to fight the entropic state of our house. Each night we sat through late hour feedings, Becca coaxing our fitful baby to eat while I helped where I could and tried to share in the bleary-eyed routine. The clipboard chart filled with row after row of hand-written data.

As days turned to weeks, we spent many evenings standing exhausted in the kitchen straining to agree on the “strategy” for the next 24 hours. Wracking our sleep-deprived brains over the contours of the naps and nurses that happened earlier in the day (or was that yesterday?), we debated sleeping/feeding theories and strategies, our individual selective memories often retaining the details supporting our own narratives. The more exhaustion and “evidence” we accrued, the more entrenched we became in our respective realities. It would often feel like we were speaking across an experiential chasm, the specter of “failure to thrive” hanging over the gap, the clipboard with pages of detailed data sitting unconsulted on the end table in the other room.

Bloom

Despite the perpetual exhaustion, an underappreciated aspect of newborn care (at least to new parents) is how monotonous the daily work can be. Feeling a need to think about something other than surviving the next segment of the day, I started digitizing our hand-written notes and playing around with the data. My first pass at visualizing this data was inspired by the sense of sleeping and eating being completely detached from the normal rhythms of day and night. Nurses are represented through each smaller circle placed along the 24 hour ring of the day-night cycle.

Hardly intended for any sort of analysis, the inscrutable nature of the viz reflected A’s haphazard eating and sleeping schedule and the on-demand nature of newborns. By week three we were desperate for some regularity (and sleep). We started experimenting with different feeding cadences and a 2AM bottle that I would take on so Becca could get a few more hours of sleep.

As we recorded more data, I started bringing my laptop to the late night nursing sessions, tinkering with different potential visual encodings. My second take on the data borrowed from the long lineage of flower visualizations. Each flower represents a day in the first 28 days of A’s life. The angle of the petals represent the time of day while the length of the petal represents how long he nursed or how much of a bottle he took. The color of the petal represents whether A nursed on the left or right side with purple being a result of nursing on both sides. The green leaves were bottles I’d fed A.

I was quite happy with the end product. I imagined hanging a poster version in A’s room, a colorful memento of his first month of life (and early exposure to data visualization!). I even used the visualization as part of my personal introduction while presenting at that year’s Tableau conference—my first nights away from home since A was born two months before—as a way to broadcast my new status as a proud dad. I printed out a large scale image of the viz and made plans to display it in A’s room.

But as excited I was about the viz and as much satisfaction I took from people enjoying the playful representation of that challenging first month, the viz didn’t elicit the same joy in Becca. Every one of those nurses—288 captured in the first 28 days—was a sometimes challenging, often painful, physical experience with our fussy baby. The 278 minutes a day she spent nursing every 24 hours—10.3 nurses of 27.2 minutes each or 4.6 hours—was time spent sitting in a chair, not being able to sleep or do much of anything else. The 25 bottles I’d fed A over that same time paled in comparison.

If nothing else, the flowers were a visualization of the breadth of the experiential chasm between us. The very process of the viz’s creation—me sitting on the bedside with my laptop as Becca nursed A in the rocking chair across the dark room, the screen illuminating my face as I concentrated on my creative outlet— highlighted the gulf as much as the data did. There I was, visualizing her data and flying off to proudly display my creation at a conference while she stayed up late, now alone, caring for our son.

My enthusiasm for the viz turned to guilt and I stashed the poster in my desk.

Growth

Our daughter L was born May 10th, 2025 at 5:20 AM. One of her first experiences in life after filling her lungs with air was Becca nursing her on her right side for 5 minutes. It didn’t take our now two years of parenting experience to quickly realize how much more of an “easy” baby L was compared to her older brother.

While we tracked L’s sleeping and feeding, our greater appreciation for the wide range of “normal” in babies (not to mention the immediacy of managing the day-to-day logistics of two young kids) meant the newborn developmental targets that loomed so large in A’s first few months faded to the background. Determined to make life easier for ourselves this time around, we used an app with a shared account across our phones to quickly input nursing/sleeping times which the app instantly visualized in dashboards. This asynchronous input was critical since, as Becca’s dad likes to say, one kid can run in one direction, two kids can run in three directions.

In this way, the app’s visualizations populated by our data helped knit our experience together and ensure we didn’t find ourselves looking across a reality gap like during A’s first few months. We each contributed to a single source of truth. That source of truth could be instantly consulted to refresh our tired memories. The data could be viewed in different ways—weekly vs. daily vs. aggregate analyses—to suit our different modes of thinking. In essence, the data provided us the daily calibration and validation of our individual experiences that we weren’t able to provide for each other the first time around. Standing in the kitchen each evening strategizing over the next 24 hours, we could look back on a shared reality and realign.

(To be clear, the “easy” baby factor cannot be overstated.)

As we settled into something of a sustainable equilibrium, my mind wandered back to what to do with the rich data we were collecting. Perhaps inspired by the forested parks we now regularly take A to, I revisited the 24 hour radial approach, this time aiming to replicate the rings of a tree. Each ring represents a day while the color of a given segment of a ring represents sleep time, nursing time, or awake time. The spacing between each ring has a set amount of “growth” but for any time that day that was taken by sleeping or nursing, there is 50% extra “growth”. Over time, these patterns of extra growth across the rings create L’s unique tree cross-cut shape.

Iteration

I don’t think it’s a coincidence I was drawn to natural motifs for this data. Organisms are a series of complementary and competing systems, all absorbing and adapting to the myriad inputs of their environments to produce their final shapes. I would offer that the entire practice of data visualization—the collection of data, its transformation into a useful signal, the resulting sense-making, and iteration through deepened understanding—parallels this natural drive to create meaning and growth from our encounters with uncertainty. Thriving means being able to process the unpredictable and grow from it.

My own sense-making frames the progression from a nebulous form to a field of flowers to a single sturdy tree as a reflection of Becca and my evolving understanding of the data. Eating and sleeping are, all at once, vital yet ordinary and repetitive events, the markers of our daily rhythms, and blips within the days, weeks, and years we layer one on top of another ultimately creating the shape of our lives. I can’t help but see in these visualizations a cautious embrace of a central tension of parenthood—making sense of day-to-day uncertainty against the scale of our children’s lifetimes.

The post Flower Visualizations and the Uncertainty of New Parenthood appeared first on Nightingale.

Constructive visualization enables users to create personalized data representations and facilitates early insight generation and sensemaking. Based on NODKANT, a toolkit for creating physical network diagrams using 3D printed parts, we define a competitive network physicalization game: BattleGraphs. In BattleGraphs, two players construct networks independently and compete in solving network analysis benchmark tasks. We propose a workshop scenario where we deploy our game, collect strategies for interaction and analysis from our players, and measure the effectiveness of the strategy with the success of the player to discuss in a reflection phase. Printable parts of the game, as well as instructions, are available through the Open Science Framework.

Games have long been recognized as effective tools for engagement, learning, and problem-solving. In visualization, games and activities have been explored as methods for enhancing the understanding of complex visualizations or concepts and promoting creativity. Construct-a-Vis and Diagram Safari are noteworthy examples, which encourage participants to interact with data and develop insights through structured activities and play. Physicalization—the representation of data through tangible objects—has been gaining attention and promotes a method to deepen comprehension through active construction and manipulation.

Physical interaction with data representations enhances understanding, engagement, and long-term retention. Prior work suggests that actively constructing representations leads to more profound insights compared to passive observation. Studies indicate that people will naturally organize network structures in meaningful ways, i.e., enclosing clusters in hulls. Data Strings highlights the benefits of participatory physical visualization, allowing individuals to actively shape collective data through direct interaction. Further related work investigates interactive physical representations of networks, including NODKANT, suggesting that this added interactivity enhances perception, memorability, and analytical reasoning. WonderNet similarly explores the physicality of network structures, transforming them into tangible objects that highlight their inherent connectivity and spatial relationships. This approach reinforces the importance of physicalization as a method for deepening comprehension and engagement. Willett and Huron introduce the concept of input visualizations, highlighting how visual structures can serve not only as representations but as interactive mechanisms for data input. Their framework expands our understanding of how visualization can facilitate engagement and structured exploration, offering new perspectives on physicalization and interactivity. HoloGraphs demonstrates how dynamic networks can be represented physically and how they can raise visualization literacy by offering engaging interfaces and tangible interactions.

Inspired by such examples, we investigate whether competitive, hands-on network construction can enhance graph comprehension, memorability, and problem-solving in an interactive and playful setting. Here, we introduce BattleGraphs, a two-player competitive board game that integrates physical network construction with analytical problem-solving. Drawing from the findings of prior research on user-generated layouts and interactive physicalization of networks, with BattleGraphs, we explore how the familiarity of self-constructed representations of networks influences task performance, particularly in competitive time-constrained scenarios. The intended contribution of the game is to provide an experimental platform for studying network construction through physical and interactive means, as well as to investigate the benefits of engagement through competition.

BattleGraphs is designed to engage players in various levels of cognitive processes as defined by Bloom’s Taxonomy. The proposed game aligns with the following levels (progressing from easy to complex): (1) Remember—players must recall basic graph concepts such as nodes, edges, and connectivity; (2) Understand— Players need to understand the visual encoding to construct their graph and understand graph analysis problems; (3) Apply—Players apply their knowledge of graphs to construct a physical representation using the NodKant kit; (4) Analyze—Players must break down the structure of graphs to solve tasks efficiently; (5) Evaluate—Players evaluate the effectiveness of a graph layout, which promotes reflection on learning strategies and problem-solving approaches. They judge the speed and accuracy of answers against their opponents; and (6) Create—Players develop strategies for organizing and constructing their networks. We intend to further validate this alignment using the workshop setting as a platform to elicit feedback and reflect on our visualization game in practice.

Sources and materials

BattleGraphs is based on the NODKANT toolkit by Pahr et al. NODKANT is designed to be a simple, dynamic, and effective toolkit, specifically aimed at the construction of physical network diagrams. For BattleGraphs, we additionally introduce two types of card decks, representing the graph’s edge list and the graph tasks to be solved, as an aspect of gamification.

Toolkit NODKANT consists of two 3D printable parts (Figure 2), for which the mesh files are available on osf.io. Firstly, edges con- sist of two spools with a yarn in between them (Figure 2a). The spools can be rotated to alter the length of the yarn freely after placement (Figure 2b). Secondly, nodes are represented by cylindrical disks with labels printed on top (Figure 2a). Placing small magnets in the rotational center of the parts allows for quick assembly, while also ensuring the parts can be freely rotated individually (Figure 2c). Using a magnetic whiteboard as a base provides a 2D canvas to embed physical graphs.

Cards The Edge Card deck serves the construction of the graph, each card containing an edge of the graph. Pahr et al. propose to use an edge list, sorted by associated node degree, to provide users with step-by-step instructions during construction. For BattleGraphs, we decide to gamify this aspect by providing each player with a random initial sorting (in the form of a shuffled deck of cards, each representing a particular edge of the graph) to create their own construction strategy. We propose an Assembly Phase of about 30 minutes, similar to Pahr et al., choosing the same dataset of animal interactions, mammalia-raccoon-proximity-50 to produce comparable results. The Task Card deck contains textual descriptions of the tasks used by Pahr et al. for their study. Each card presents a (low-level) graph task, derived from Lee et al.’s graph task taxonomy, on one side, and solutions to the question on the other.

Replay Value and Difficulty Selection In order to ensure BattleGraphs can be enjoyed multiple times by players, the game can be played in one of three difficulty settings: easy, medium, and hard. Each difficulty setting corresponds to a graph of increasing complexity, i.e., easy difficulty corresponds to a graph of lower complexity (i.e., few interesting structures, low density, low number of nodes and edges), whereas high difficulty corresponds to a graph of high complexity (many interesting structures, motifs, and a larger number of nodes and edges). Depending on the difficulty selected, i.e., graph data selected, a different set of Edge Card decks is selected, shuffled, and given to players. Conceptually, players can easily create their own decks of cards, based on their own selection of graph data, in order to replay BattleGraphs at their preferred and custom difficulty setting.

Battlegraphs: Game

BattleGraphs is a two-player, competitive, educational board game centered around the construction of one’s physical network layout (see Figure 1). Subsequently, players answer a set of graph analytical questions faster than their opponent and, in doing so correctly, gain a point. Intuitively, the more readable one’s constructed network layout, the faster and more accurately one should be able to solve graph analysis tasks. Broadly speaking, this roughly 90-minute game consists of four distinct phases, namely a 15-minute (instruction and) Setup Phase, a 30-minute Assembly Phase, a 30-minute Battle Phase, and a final 15-minute Discussion Phase. To play, the following materials are required: i) a countdown timer to keep track of time, ii) two magnetic whiteboards, iii) two NODKANT kits, iv) a physical divider to visually separate each player’s whiteboard, v) two identical, but shuffled decks of Edge Cards, each representing the edges of the graph to be assembled, and vi) another shuffled deck of Task Cards, in which each card is a graph task to be solved (Figure 3).

Setup Phase At the start of a game of BattleGraphs, each player receives their magnetic whiteboard, a well-shuffled, face-down deck of Edge Cards, and a NODKANT kit. Each player places the whiteboard and their deck of face-down Edge Cards in front of them. A physical divider is then placed between each player’s whiteboard such that the view of the other’s board is obstructed. Place the well-shuffled deck of Task Cards out of view for now. Finally, set the timer to 30 minutes and place it in an area visible to both players. Once the timer starts, the Assembly Phase begins.

Figure 2: The NODKANT toolkit. (a) Each node is represented as a 3D-printed “puck” with a magnet fitted underneath. Edges are represented as two such magnetic “pucks” connected by an adjustable length of yarn. (b) Edge length, i.e. the length of yarn between an edge’s endpoints, is adjusted by turning the endpoints’ spools until the desired length is achieved, (c) To construct a network, edges and nodes are stacked vertically on the magnetic whiteboard surface. Reprinted, with permission by Pahr et al.

Assembly Phase During the Assembly Phase, each player has 30 minutes to construct their physical layout of the graph represented by the deck of Edge Cards. Each card, in the currently face-down deck of Edge Cards, represents one edge of the graph to be assembled and contains the start and end nodes of the edge. In essence, the shuffled Edge Cards are a random arrangement of the graph’s edge list. Each player may now flip the deck of Edge Cards face-up in order to view, re-arrange, and organize the entirety of the deck as they see fit. Using the provided NODKANT kit, each player, following their organization of their Edge Cards, now constructs their graph on the provided whiteboard. The NODKANT kit consists of physical representations of both nodes and edges (Figure 2). Nodes are represented by black, magnetic, labeled disks. Edges are represented by two white, magnetic, unlabeled, connected by a length of adjustable string. To represent a basic graph of two connected vertices, one edge is magnetically placed on the whiteboard, and each node is magnetically placed atop each end of said edge. Once the timer notifies both players that 30 minutes have elapsed, the Assembly Phase has concluded, and the Battle Phase begins.

Figure 3: Example of task cards used during the Battle Phase.

Battle Phase During the Battle Phase, players compete for 30 minutes to answer a set of graph analysis questions as quickly as possible using their own constructed network layout. Each question (task) is represented by one of the Task Cards. These cards are two-sided, one of which features the graph analysis question, the other the answer (Figure 3). To start the Battle Phase, set the timer to 30 minutes. The well-shuffled deck of Task Cards is placed question-side-up in an area visible to both players. Once the timer is started, the Battle Phase has commenced. During each round of this phase, players read the question of the currently revealed Task Card in silence and subsequently attempt to answer this question as quickly as possible using their own constructed graph layout. The first player to call out an answer checks the correctness of their provided answer using the back side of the current Task Card. If correct, they keep said card, thereby gaining a point. If incorrect, the opponent has a chance to answer to answer said question correctly to gain a point. If neither player is able to answer the question correctly, neither gets to keep the card. Players continue to answer questions in such a manner until either the 30 minutes elapse, or all Task Cards have been answered. The player with the most Task Cards, i.e. points, wins the game.

Reflection

Discussion Phase During the final 15-minute Discussion Phase, players remove the physical divider in order to reveal their network layouts to each other and discuss strategies for both graph layout and graph analysis. Questions worth asking include, but are not limited to: What was learned about the graph? What strategies did players utilize when organizing their Edge Cards and subsequently building their networks? What strategies did they employ to answer task analysis questions? Why did one player do better than the other?

The game’s design aligns with Bloom’s taxonomy on the six cognitive levels–Remember, Understand, Apply, Analyze, Evaluate, and Create. However, we aim to explore and validate this aspect further to investigate how effectively the game supports each level in practice and whether it facilitates meaningful cognitive engagement across these domains. We aim to use the results of the discussion phase from the workshop to perform a brief qualitative analysis of construction strategies and interaction techniques with the NODKANT toolkit. Prior work emphasizes the role of interactive physicalization in supporting deeper comprehension of visualization concepts. By actively constructing representations, players are expected to develop a stronger understanding of network structures, reinforcing prior findings on hands-on engagement in visualization tasks.

Evaluation Plan We aim to assess how players construct and interpret network structures, as well as the level of engagement facilitated by BattleGraphs, using the VisEngage questionnaire. Engagement is a complex construct encompassing multiple dimensions, including captivation, discovery, and challenge. The questionnaire provides a method for assessing interaction-driven immersion in visualization, aligning well with BattleGraphs’ goal of increasing engagement in network visualization tasks. We will assess these aspects through post-game discussions and ethnographic observations throughout the workshop to determine how BattleGraphs encourages deep engagement and involvement. Participants’ reflections will be transcribed and analyzed qualitatively. The qualitative coding will cover observations of player interactions, including strategic adaptations. This process will result in higher-level sentiments that form the basis for our analysis of how players approach network construction in BattleGraphs. Our focus will be on (i) Graph comprehension (i.e., players’ understanding of network structures, like cliques, clusters, and bridge/hub nodes); (ii) Engagement factors (utilizing the VisEngage questionnaire); and (iii) Impact of physicalization and interaction (i.e., comparing to the workshop results to the interactions identified by Pahr et al.—–see Figure 4a-d).

Figure 4: Different interactions with NODKANT: (a) Wiggling to reveal adjacency; (b) & (c) Pulling to reveal common connections; (d) Pushing nodes together to show their degree. Reprinted, with permission by Pahr et al.

Preliminary Expectations We anticipate that, by the end of a game of BattleGraphs, players will have a better understanding of graph (sub)structures, such as cliques (a complete subgraph within a larger graph), clusters (a set of highly interconnected nodes in a graph), bridges (nodes that connect to otherwise disconnected subgraphs), or hubs (highly connected nodes). Through interactive physical construction, we expect participants to actively manipulate these structures. We expect this process to lead to an improved comprehension of network structures compared to passively observing them on virtual screens.

The game mechanics encourage problem-solving under constrained conditions, requiring players to analyze graph connectivity while applying strategic decisions in real-time. Depending on the set of (correctly answered) questions asked, players might also develop an understanding of more abstract descriptive graph metrics, such as a graph’s density, diameter, or average degree.

Furthermore, engagement indicators, such as captivation, discovery, and challenge measured via the VisEngage questionnaire are expected to correlate with effective learning outcomes. Observing how players approach network construction provides valuable insights into the cognitive benefits of actively interacting with tangible objects and being part of the construction and creation process. BattleGraphs will also explore emerging strategies in graph construction, identifying key approaches in layout design, optimal edge placement, and adaptive problem-solving that are still ongoing problems within the broader field of network visualization. Preliminary findings will contribute to research on interactive visualization literacy and network physicalization, establishing a method for engagement-driven learning in network visualization.

Acknowledgements

This work was funded by the Austrian Science Fund (FWF) projects ArtVis [10.55776/P35767] SANE [10.55776/I6635], [ESP 513-N], Vis4Schools [10.55776/I5622] (in cooperation with the Czech Science Foundation [No. 22-06357L]). The financial support by the Austrian Federal Ministry of Labour and Economy, the National Foundation for Research, Technology and Development and the Christian Doppler Research Association is gratefully acknowledged. The authors acknowledge TU Wien Bibliothek for financial support through its Open Access Funding Programme.

The post BattleGraphs: Forge, Fortify, and Fight in the Network Arena appeared first on Nightingale.