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

From City Animals to Union Station

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

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

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

The research process: Data meets daily life

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

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

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

Five animals, five stories

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

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

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

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

Navigating the hard truths

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

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

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

Making data visible and inviting

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

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

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

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

The iconic title wall: Observing Union Station

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

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

The massive 3D map: Placing yourself in the data

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

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

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

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

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

Adding Sound: Activating the Space

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

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

Iconic cutouts and LA signage culture

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

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

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

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

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

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

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

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

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

The internet “reads” your interests, and gradually your network expands. At some point, I became part of the Flowers and Figures community, joining others who are interested in data art and passionate about creative projects.

My path

At first, I was just studying the works of others, hesitating to try it myself, though ideas for data encoding had been circling in my head. As is often the case, my first attempt happened by accident. A few members met offline at a café just before Christmas to get to know each other and to attempt creating a data art project right there, on paper, using pens or markers, based on each person’s own data.

I loved it! Both the process and the result, even if it looked quite simple.

After that, I was eager to create something of my own—something real, not on paper.

As with any data visualization project, the key steps are:

• choose a topic
• find data
• and, in this case, invent a way to encode the data into visuals—the most creative part, in my view

An important decision is the path you take in encoding and presenting data art:

• One way is symbolic—any geometric figure, flower, or petal can mean anything, depending on what data value or category you assign to it
• The other way is to keep it as close as possible to the real object in the data, which isn’t always feasible.

The first step for every new community member is entering their data into a shared Google Sheet. This forms a dataset that can be used to create a full-fledged piece of data art. A community portrait—sometimes called data badges—is a popular format in data viz spaces, especially within communities or at conferences and events.

The range of themes for data encoding in the community is wide: geometric shapes, flowers (matching the community’s name), even bugs, and coats of arms. I wanted to create a community portrait that stood out from the gallery. And this time, I really wanted the project to exist physically; to photograph it and then compile a digital version.

According to the format rules, the portrait had to show:

• skills—drawing, crafting, data, data viz
• proficiency level in each skill
• name, gender (optional)

Dataset: Google Sheet where each new participant fills in a row about themselves.

The idea of physical data art

For representing the skills, I chose colorful plastic cable ties—commonly used to bundle wires and cables. They’re a simple and effective way to connect elements, widely used in construction and daily life. My daughter, a student, used longer ones to secure her rolled-up architectural drawings. These ties can withstand quite a bit of stress.

I wasn’t interested in technical specs though—the important factors were color and minimal size. There wasn’t much variety in colors—most sets offered standard combinations: red, blue, green, yellow, and orange. So I worked with what I had.

The encoding took shape

• Colorful ties = skills: drawing, crafting, data, data viz
• Proficiency level = length of the tie: trimmed literally to 1, 2, 3 cm or fully cut off if the level is 0

The hardest part was figuring out what to attach the ties to. I tried wooden coffee stirrers—too long. Chopsticks—needed cutting or grouping data for 2–3 people, which felt clunky and undermined the clarity of the concept.

I browsed various craft supplies, school kits, and eventually found counting rods—those plastic sticks used in early math learning kits for first-graders. Perfect for the project: 6 mm in diameter, 6 cm long—exactly what I needed.

One stick = one participant.

Everything else fell into place: gender represented by the color of the stick—black or white, painted with gouache. Labels repeated this info. At first, I tried writing names with a gel pen, but eventually moved to printed labels.

The result

I tried different layouts for the finished sticks. You can’t twist them too much—names become unreadable, lighting matters, shadows too. The final shot of the stick layout became the data art piece. The legend was made in Figma, and the whole composition was assembled there too.

The data art includes information from just a portion of the community—it’s grown a lot, and photographing the full dataset in one frame was technically impossible at home. I really didn’t want to use photo compositing. I added numbers to the printed name labels so participants could find themselves quickly, since names repeat. The whole process took about two weeks.

The sticks themselves turned out charming, and during our offline meetups I can hand them out to participants. They’re nice to touch and sort through—each with its own texture. Honestly, I didn’t want to put them down. But all things end—and the data art now fits neatly into a small box from a gadget.

I’ve seen breathtakingly beautiful projects shared in our channel—complex constructions from paper and thread, beads, even 3D-printed pieces, and what amazed me most—made of clay.

I couldn’t wait to share my result. I didn’t expect the post in the community to get so many comments and positive feedback on my modest effort. A short moment of fame—delightful and inspiring.

Now I’m thinking of making something material on a socially meaningful topic. To do that, I’ll need to: find the data, come up with an encoding in a specific material, and bring it to life. And in my most ambitious plans—participate in a data viz competition!

The post Plastic Portrait: Visualizing Technical Skills Through Cable Ties appeared first on Nightingale.

Before becoming a mother, I had this vague idea that, since I identified as a “data person,” data would help me parent. As a chronically online millennial, the path to parenthood was paved with content about wake windows, developmental leaps, and tracking apps. After my daughter was born, it turned out this vague idea was both completely wrong, but also completely right in a completely different way.

As I tried to navigate becoming the mother of my newborn baby girl, it turned out that recorded observational data was of almost no use to me, and in many ways actively detrimental to my well being. In her very early days, we meticulously tracked her milk intake and nappy changes as part of our efforts to problem solve our way out of slow weight gain. The creation of that dataset was a factor in a complex constellation of stress and misery. Despite all the helpful apps, collecting this data was frustrating and difficult. I vividly remember crying in frustration at 2 a.m. because the app wasn’t configured to handle the draining “switch-feeding” breastfeeding strategy I’d been advised to use. I found myself snapping at my husband (many times) because he’d forgotten to log a dirty nappy. Bowel movements had become a metric for success, and if we didn’t write it down, had she even pooed?!

Of course she had. As our tiny baby grew, so did my confidence, and I was able to let go of the tracking apps. This letting go allowed me more space to grow and trust the infinite wealth of qualitative data that I was amassing in my mind, body, and heart. The unrecorded data of my lived experience has turned out to be invaluable in guiding me through this turbulent phase of my life.

As my daughter got a little older, I found myself starting to yearn for the languages of data visualisation to help me express what I was experiencing. At heart, I see data visualisation as a mechanism for exploring change, contrast, and difference. As a data practitioner, charts are an important part of my sense-making process. My matrescence has been one of the most transformative experiences of my life. It felt completely natural to reach for this toolkit to help me make sense of this profound change; but I wasn’t sure how.

I’ve long admired Dear Data and the field of autobiographical data visualisations it inspired. I’d previously used an autobiographical data visualisation to help me process my grief after my father’s death, painstakingly piecing together a dataset of memories and emotions. But in this new context of motherhood, I was deliberately bereft of observational data to work with. I had consciously banned myself from the act of observing and collecting data about the topic I now desperately wanted to explore and understand.

One morning, as I stood at the stove scrambling eggs for our breakfast, I mused to myself how I’d always hated scrambled eggs but now found myself eating them regularly. I poked the eggs with the spatula and turned them into a bar chart in the frying pan, then took a photograph. I had no way to go back and collect data about how frequently I’d previously eaten scrambled eggs. I had no intention of starting a log of each time I ate them; however, even though I didn’t have a dataset to work from, I’d still been able to create a reasonably accurate visual representation of the change in my scrambled egg consumption over time.

Around this time, I also had an opportunity to undertake an individual practice-led research project as part of my master’s program at the Australian National University’s School of Art and Design. I pitched to my supervisor that I wanted to try making “data-less” data visualisations. My project would be a practical exploration of whether I could use the language of data visualisation to communicate concepts without working from an actualised dataset. My supervisor was enthusiastic about the project, and also pushed me to try expressing myself in the native medium of social media—portrait video. I was way out of my comfort zone, but #MyMatrescenceProject was born.

I quickly settled into a workflow that involved physically piecing together a “sketch” of my concept using paper, yarn, and found objects. My personal creative practice has always revolved around making rather than drawing, so iterating ideas in this way felt both natural and like a way to reconnect with myself. I photographed each element on a desktop studio made from an old photographic enlarger stand and a make-up ring light, which gave me fine control of the photography. Then, each piece went through a digital editing, compositing, and animation workflow using Adobe Lightroom, Photoshop, and After Effects. This project has allowed me to use an intuitive visual development approach, rather the procedural programmatic approach to data visualisation design that I use in other contexts. It’s a very different space to play in, but it feels very liberating and I’m having a lot of fun.  

Working in video, I had to develop a sense of timing, which was challenging—particularly trying to get the transitions and reveals just right to land the jokes effectively. I also experimented with typography and explored using different typefaces to play different roles in the visualisations. Adding annotations in a script typeface was a breakthrough moment for the project, allowing me to directly reveal my authorial voice in the work. This voice is often deliberately obscured in data visualisations, but including it here felt critical. It is so important to me that this project shares my experience in a way that doesn’t suggest it is the experience. The discourse of motherhood is so often judgmental, comparative, and overly populated by the word “should” and other terms of its ilk. I wanted my project to create moments of connection and reflection, not alienation or shaming.

Through a happy accident, I finished the first set of videos and the project showcase website just in time for Australian Mother’s Day. I felt a bit weird and awkward about starting to share the work, which is a mix of joking and irreverent thoughts about parenting, and deeply personal reflections on my anxieties and struggles as a new mother. I started by sharing the project with other mothers that I know, and then more publicly on social media. The response the project has received has been incredibly affirming. It has meant so much to me to hear from people how they connected with the project and what they found meaningful. Many people sent me screenshots of pieces with which they particularly connected, as well as their own comments and stories. A common thread through the responses was how chart forms are a powerful way to encapsulate an idea that can be difficult to express in words.

These “data-less” data visualisations seem to work, even though the use of quantitative forms for non-quantified data feels like it might be breaking “the rules.” At the start of the project, I had wrestled with a concern that my experimentation was in some way misappropriation or an illegitimate use of the chart form. Discourse around data literacy frequently calls out the problems with non-existent, faked, or misrepresented data. Is a chart made with what I’m now fondly calling “spontaneous post-hoc data” or “speculative data” inherently dishonest? Randall Munroe has been comedically employing the chart form for nearly two decades. Observe, Collect, Draw popularised autobiographical or personal data visualisation. Although these are typically predicated on the visualisation of contemporaneously recorded observations, data feminist or data humanist perspectives explicitly make space for a broader definition of “data” that embraces the qualitative lived experience I am seeking to express.

#MyMatresenceProject relies heavily on my position as the expert in my own experience to establish the legitimacy of the work. Are these charts accurate? Mostly. Are they mis-leading? Not intentionally. Are they honest? Absolutely.

The post MyMatrescenceProject: Using “Data-Less” Data Visualisation to Explore My Experience of Motherhood appeared first on Nightingale.

A moment of wonder: Photography of the Matterhorn

The piece’s starting point was my photograph of the iconic Swiss mountain, the Matterhorn, viewed from Schwarzsee above Zermatt, captured in late autumn (Fig. 1). That day, the mountain stood silent under the early snow, bathed in sunlight. The stillness, the softness of the snow, and the power of the alpine peaks came together in a moment of awe and peace, a memory deeply cherished.

A dialogue with the machine: Diffusive reinterpretation

What draws me to machine learning is a fascination with how simple building blocks, based on mathematical concepts, can be assembled into increasingly complex architectures capable of learning and mastering intricate tasks. I’m especially captivated by the elegant mechanics of generative AI, particularly diffusion models. These models, when used in text-to-image mode, begin with pure noise, and guided by a prompt, gradually transform it into coherent imagery through a step-by-step denoising process. It feels like a mathematical meditation on emergence: a journey from randomness to form.

In my project, however, I explored text-guided image-to-image mode, steering the generation using both the photograph and a simple text prompt:

“Artistic wavy version with subtle pink and violet tones.”

The model began by adding noise to the photograph and then iteratively removed it until a reimagined vision emerged (Fig. 2). To make this transformation feel alive, I also captured the denoising steps and wove 30 interpolated frames between them, revealing a seamless, almost dreamlike unfolding from the original photograph to its AI-crafted interpretation (Fig. 3).

A celebration of the nature: Diffusive Alpine Metamorphosis

Inspired by the concept of diffusing particles from my physics lectures, I sought to express the breathtaking beauty of the mountain and its artistic reinterpretation through a joyful, dynamic spectacle. This led me to code an animation where each image disintegrates into spheres, which subtly recolor during diffusion and regather to form the subsequent image in the denoising sequence. The spheres become messengers of transition: first chaotic and dynamic, then gradually converging into defined forms. Later, I added music to amplify the light and vibrant atmosphere supporting the piece’s emotional core. Figure 4 presents the resulting animation—I warmly invite you to witness the transformation as it unfolds.

A harmony: Nature, machine’s ‘imagination’ and human creativity

At its core, this piece began with a single photograph—a data point constituting a structured array of color values, capturing a fleeting alpine moment. Through the diffusion model, this data underwent a transformation: noise was algorithmically added and then removed, guided by the prompt, resulting in new visual interpretations rooted in probabilistic learning. The final piece emerged as a collaboration between nature, machine, and human creativity; bringing together the serenity of the original landscape, the generative potential of machine learning, and a personal creative vision expressed through photography, prompt design, code, and music.

As generative AI becomes more intertwined with data analysis and visualization workflows, it’s reshaping the boundaries between factual and creative representations. We’re already seeing AI generate basic charts from datasets, enhance visual metaphors, suggest stylistic treatments for dashboards, and assist in code generation for custom visualizations. In this broader context, projects like this one explore how to reveal the internal workings of a model, its interpretation of data, and the layered steps of its computational imagination. This expansion of the designer’s palette, through AI-augmented visual storytelling, offers new ways to make meaning, provoke curiosity, and connect audiences to the stories behind the numbers.

The “Diffusive Alpine Metamorphosis” piece is part of my broader journey at Data Immersion, where I create data-driven artworks that honor nature’s beauty and life’s vibrancy. I warmly invite you to visit the Data Immersion blog to explore more work inspired by these themes.

For an extended, more technical article about this data art piece, please visit Generative AI for Data Art & Visualization: Diffusion Model.

The post Creating Data Art with GenAI: Diffusive Alpine Metamorphosis appeared first on Nightingale.

When any traumatic or sad event happens to us, we have two choices: either strive to heal or surrender ourselves to the arms of rumination and be devastated. Of course, it is normal to experience a shock and let ourselves go for a while when we first encounter the event. However, afterward, it is very important for our mental health to prepare an action plan for ourselves and act accordingly.

Such an incident happened to me that truly shook me to my core. I felt a pain so intense that I was forced to choose one between healing or surrender. I am pursuing a Ph.D. in psychology and I knew if I closed in on myself and went through the process in this way, it would be very harmful to both my mental and physical health. Before this article progresses, I should mention that I will intentionally avoid detailing the specific event that happened to me, as it is more important to focus on how I processed the grief caused by it. The steps I took during this time can be applied to any situation that causes intense pain, such as earthquakes, losses, etc.

As one of my initial steps, I started keeping a diary a few days after the incident. This was important to me for two reasons: first, my journal was a partner in commiseration I could reach whenever I wanted to; second, if one day I fully recovered from the impact of the event, this diary would give me retrospective information about how I managed the process. In fact, it was like collecting qualitative data about my own life. I remember my tears falling on the table in the first weeks while writing. During that period, I started to write down my feelings, emotions, and disappointments, as well as new memories that I had just started to collect.

The second important step I took was to socialize more and start participating in activities I had continuously postponed before the event. As I mentioned, I was feeling pain so intensely that I needed to channel this emotion into something in a healthy way. In fact, it was a catalyst for me; I was in so much pain that I felt I had nothing left to lose, so I started trying everything on my list—being involved in the development of two game designs, tutoring low-income students voluntarily, attending social events, making new friends, etc.

The third step I took was to start my day by getting up before sunrise to go for a walk, taking more walks every chance I could throughout the day. This was a very powerful way for me to channel my pain. In the first weeks, there were times when I walked about 34,000 steps a day. Along with the steps I took, my tears also flowed. I could track all these walking performances using an app on my phone.

As the days and weeks passed like this, I noticed that the pain I felt gradually transformed into a sense of fulfillment. Slowly, as the effect of the event faded, I started to look back on the past and say, “there was something good in it after all.” It became clear that I needed to bring closure to the event. Of course, just as the effect of the event did not pass all at once, achieving closure wasn’t instantaneous, either. While there was very little time left before I completed my Ph.D., I decided to bring closure to the event within myself by visualizing the quantitative and qualitative data I had gathered about myself up to that moment. I would have the closure through data visualization.

For the visualization, I made the bag—the picture of which you will see below. The bag consists of two parts: (1) the number of steps I took while recovering from the impact of the event and (2) the physical objects associated with new memories I formed and received from events I attended, new friends, etc. 

In the end, we are all human, and as a part of being human, painful and sad events happen to all of us. This is an unavoidable reality of life that we can’t control. What is partly in our control and willpower is how we react to them. If the time is used wisely after sad and shocking events, it is a wonderful occasion for personal growth. I feel much more peaceful and mature compared to myself about a year ago. This data visualization project helped me a lot to reflect on my healing and growth over time and have closure. I hope what I shared is helpful to everyone who reads this article. 

I hope to have many more days with data visualization!

The post Healing Through Data Visualization appeared first on Nightingale.

Join us for a brief retrospective of our six-month journey into the world of data art and physical data viz.

Background

Data Through Design (DxD) is an independent collective which organizes an annual art exhibition during NYC Open Data Week, featuring works that use data from NYC’s Open Data portal. Each year’s exhibition has a different theme, and 2024 had the theme of “Aftermath”.

Several of us had been to previous DxD exhibitions, and we were excited to potentially participate as artists this year. Typically, DxD begins accepting proposals in the fall for the exhibition the following spring. Once the applications opened up and theme was announced, we immediately began brainstorming.

Brainstorming

One early topic we discussed was the lanternfly infestation that plagued New York City in recent summers. Unfortunately, the lanternfly phenomenon was too recent and there wasn’t granular, city-level data available so we had to abandon that idea for now.

Following that line of thinking, we immediately thought of rats. They were pests that were part of the zeitgeist in New York City, and thanks to their ubiquity, we were able to track rat complaints using a publicly available dataset from the Open Data portal. We used a subset of the 311 calls dataset that only contained complaints mentioning the term “rodents”.

With a potential dataset in hand, we moved on to some exploratory analysis. When we plotted the data over time as a simple line chart, we saw that it had several interesting properties:

1. The number of complaints followed a cyclical pattern that corresponded with the seasons. We thought this reflected the seasonal fluctuations in the rat population, and also potential changes in human and rat behavior that reduce the chance of rat encounters in colder months.
2. The number of complaints generally increases over time, with a large increase around 2020. Some of this increase can be attributed to the natural population growth of the city, but we thought that the big increase in 2020 might be due to changes in human behavior during COVID-19, particularly the rise of outdoor dining.

Due to the strong seasonality of this data, we saw a potential connection between this data and the well-known NASA global warming spiral. Plotting our data in polar coordinates achieved a very similar visual: there was a clear seasonal trend with a spiral growing outwards over the years.

From the beginning, we didn’t have specific plans to make a sculpture but we knew we wanted to avoid just making a picture on a screen. When we extruded our rat population spiral into three dimensions with years as the z-axis and visualized the results with D3.js, the outwards spiral made the top of the chart much wider than the bottom, making the render appear like a low-poly tornado.

These exploratory renderings live online, if you want to see them more closely—you can click and drag to spin them around!

• 3D Wireframe Exploration
• 3D Surface Exploration

We were very excited about the artistic potential of this chart; the 3D polar plot perfectly emphasized the explosion in rat population in recent years while evoking imagery of a natural disaster.

Typically, 3D charts are cumbersome to read and manipulate on a screen; however, as a sculpture, this design had several interesting properties. The chart’s silhouette looked different from every angle because each vertical silhouette represented the change in rat sightings over the years for a particular month. While following the trend around the spiral, the viewer only needs to look up or down to see what the data looks like exactly one year earlier or one year later.

This meant that including the third dimension had actual value (v.s. making it 3D just for the sake of being 3D), provided a meaningful perspective for gallery visitors when they circle the sculpture and investigate the data from all angles.We also had to pick a material for the sculpture, ideally something hollow and light for ease of transportation and assembly. After some discussion, we decided on a paper surface over a rigid frame, with an internal light source inspired by Chinese and Japanese paper lanterns and Isamu Noguchi’s light sculptures.

To emphasize the rat theme, we planned to cut out rat silhouettes to cast shadows on the exterior surface of the lamp. To help viewers correlate the trend with the seasonal temperature fluctuations, we planned to use multi-colored LEDs inside the lamp with each light mapped to a specific month and its color representing the average temperature for that month. A quick research trip to the Noguchi museum in Astoria finalized this decision, and we submitted the proposal to DxD.

Prototyping

Around the end of the year, we were thrilled to learn that we had been accepted for the exhibition. There were only three months till opening day, so we got to work immediately.

While we had an idea of what the installation would look like, we didn’t have any prior experience in making physical sculptures. We were also working within some financial limitations—the exhibition provided $900 materials stipend, and we didn’t want to go over budget too much.

At first, we tried to replicate the traditional construction techniques of paper lanterns by using thin wooden sticks temporarily supported by a central frame, but we found it difficult to produce precise sharp angles and strong concave joints.

We also experimented with metal wire, but the material wasn’t rigid enough to support the lamp and prone to deformation when bumped.

Our final material exploration included a set of 12 radial foam board frames extending from the center, held together by multiple gear-shaped brackets.

In traditionally constructed lamps, the center supports are removed after assembly to avoid casting shadows of the interior, but we soon realized that we couldn’t produce a structurally sound sculpture without them. We still wanted to minimize the shadows, which meant using a transparent material for the frames, and in the end we settled on laser-cut acrylic.

To generate the laser cut schematics, we used Python to create SVG outlines of each frame and Adobe Illustrator to place the frames on the laser cutting vendor’s template.

We contacted a few different local vendors, but ultimately were limited by pricing and size. Most vendors did not have a laser cutting table large enough for our design, and the ones that did were out of budget. In the end, we worked with NYCLaserCut and reduced our schematics by 30%, which ended up cutting our fabrication cost in half.

We purchased kinwashi paper for the sculptures’ surface from TALAS, a local archival and bookbinding vendor in Brooklyn. This paper had thicker fibers embedded inside, and created a very nice texture for the surface of the lamp.

Assembly

After building several prototypes, we knew that constructing the final version of the lamp wouldn’t be an easy feat; however, we didn’t anticipate just how many complications we would encounter during the process. In true New York fashion, we were building this fairly large art installation in the confines of an apartment.

Very early into the assembly process, we came across our first obstacle. The gear-shaped brackets that were supposed to hold the vertical frames together didn’t have the correct dimensions; the spacing in the brackets was almost twice the thickness of the frames! With some creative thinking, we decided to pad the holes with foam tape and leftover wood from our prototypes. The combination of these two materials turned the oversized gear holes into snug fits for the vertical frames.

We ran into our second complication after finishing the skeleton of our lamp installation. We had initially acquired some mystery LED light strips and breadboards from the depths of our abandoned projects closet, but we didn’t know how to wire them up. It took several trips to the only electronic store in the area – when did electronic brick-and-mortars become a sign of the past? – and long nights researching in makerspace forums for us to figure out the correct materials and methods needed to wire up the LED strip.

Once we had the LED lights wired up and the base code installed into the microcontroller, we quickly realized that we had overlooked one important detail. We were using a LED strip with four pins, and the lights couldn’t be programmed individually. This meant that we wouldn’t be able to display the individual temperature data with our current set-up. At this point, we were running close to the clock, but we wanted to stay faithful to our original proposal. We decided to swap the 4-pin LED strip with a 3-pin version (with the help of some quick Amazon priming) and finally, wrapped up the final step of our assembly process.

A few days before the opening night of the exhibition, the LED lights suddenly went out and no amount of tinkering would ignite them. We were stumped because nothing in the set-up had changed, which made narrowing down the cause of the issue difficult. We dropped by a local makerspace NYC Resistor, and the helpful experts there identified the issue. We were using a power supply with an output of 5 watts when we needed a minimum output of 45 watts! The underpowered power supply could have worked for a short period of time, but it was certain to overheat in the long run.

Finally, it felt like we were so close to the finish line. With the LED lights installed and running properly, we glued the washi paper panels onto the acrylic skeleton. We also constructed a wooden base for the sculpture, which allowed us to draw axis and grid lines, as well as attach a guide explaining how to read the installation.

Construction was finally complete and only one final obstacle stood in our way: we needed to transport the 5ft-tall lamp from Bed-Stuy to BRIC in Downtown Brooklyn, where the exhibition was being held. The paper panels were fragile so moving it by car was out of the question, and we were worried that the lamp would topple over if transported on a moving truck. In the end, in true New York fashion we brought our sculpture onto a local bus for 45 minutes, and carried it through the streets for the final few blocks to the exhibition.

Given all the complications during our prototyping and assembly phase, the installation process went by quite smoothly. As we were leaving the exhibition space, we turned back to take one final look at our sculpture. Often in the development process, the original design can get lost in the flurry of fabrication limitations and physics laws and time constraints. But the lamp looked just as we had envisioned, with its tornado-shaped silhouette and soft warm glow, obstructed by the small rodent shadows.

It was such a surreal moment—just six months earlier, our team had taken on this project with a little more than a paper drawing of our vision and now that vision was mirrored before us. All of this couldn’t have been done without the support from local NY vendors, DxD organizers, and NYC Resistor members, and we are immensely thankful to all of them for helping make this possible.

Conclusion

It’s been some time since the exhibition, and our team is finally able to look back fondly at this experience. The Data Through Design exhibition—and on a larger scope, all the events held during NYC Open Data Week—is such a unique event that creates space for data enthusiasts from all walks of life (students, engineers, artists, public officials, industry partners) to mingle and get inspired by the civic tech space.

Nowadays, data visualization discussions are commonly focused around the digital space, with major innovations happening around accessibility, novel interfaces like “scrolly-telling”, and precise mappings of data to visual elements. There is not a lot of discussion about data visualization in the physical space, where viewers can feel the scale and size of the dataset and be forced to reckon with the trends emphasized in the visualization.

Physical data visualizations are still few and far between, but we hope that there will be more opportunities in the future to explore new forms of storytelling with 3D visualizations.

For now, the lamp has found a happy home in Danny’s living room.

To see more photos of our project, you can visit Pia’s website.

The Data Through Design exhibition is returning with the theme “Corpus: Bodies of Data.” If you are interested in seeing more data visualization installations like ours, make sure to check out the exhibition in March 2025.

The post From Data to Art: Making “Rat Revolution” appeared first on Nightingale.

How to understand it?

Imagine a table with various data of all RPL teams. Each indicator characterizes the club and its performance over the season (number of points, goals, dry matches, cards and so on).

Now imagine a crocheted footballer doll dressed in the team’s uniform. 

Combining the two, you create a unique object that clearly shows the statistical metrics of the club. I designed sketches and then crocheted 16 of these footballers. Each of them can be interpreted using a legend or decoding scheme.

And the choice of colour and shape of the metrics is not random. It is based on the audience’s cultural code and association with the data. For example, a goal—a leg—a gaiter, a dry match—a goalkeeper’s glove.

Specific coding methods were selected for each figure and indicator, such as by rows, objects, or chains. For instance, one finger of the glove equals one match, and one loop of the shoelace equals the corresponding number of cards.

Special attention was also paid to legends, because each footballer is a dashboard that requires clarification. For this purpose, a card from the football simulation game FIFA was used and modernized. The familiar image fitted perfectly into the concept.

This is how we got an object of data-art, where every detail not just has a meaning, but carries information that was saved up for almost a whole calendar year.

Time and money

From conception to completion of the installation took more than a year. The active crochet phase amounted to six months of almost uninterrupted work. 

In total, about 17,000 roubles (USD$190) were spent on materials for the items and stands, as well as photography. It’s for about 40 pieces of yarn and many other things.

The hardest part, except crocheting, was to encode all the data in the right way. I mean, the data should stay correct and every doll’s piece should look normal at the same time. So it wasn’t easy to make it all together, especially with shorts and gaiters. But in the end was found the best way for each part and metric.
I explained more about the basis of the choice of colors and shapes in the longread. In the meantime, I am preparing for the defense of my work and presentation of the project to the commission at my university. Wish me luck in the comments and share your thoughts about my players!

Supervised and photographed by Tina Berezhnaya

The post From Data to Yarn: Crocheting the Russian Football League’s 2022/23 Season appeared first on Nightingale.

During the process, I faced various challenges and learned valuable lessons I’d like to share. Additionally, I want to highlight my creative process. I adopted the steps described by Herbert Lui in his blog post “This Four-Stage Creative Process Shows You How to Unlock Your Creativity.” It consists of preparation, incubation, illumination, evaluation, and elaboration. I recommend reading this article, as it has been very helpful in unlocking my thoughts at various stages of my projects.

Lesson 1: Establish habits for creative inspiration.

When I first heard about the project, I had many doubts. I thought, ‘I have never done something like this before,’ ‘I am not a craftsman person,’ and, ‘There are so many possibilities when it comes to creating a visualization using physical objects.’ I also wondered how this project would help me grow in my career since my focus was then on developing my technical skills using Tableau. And more importantly, I’d like to know what I would present at the end and how to make it as creative as possible.

Having already contributed to Viz For Social Good in the past, I knew I wouldn’t regret participating in this project. I struggled with creativity and only had a month to complete the assignment. I needed to start with PREPARATION and find something I could work on until I came up with a good idea. Over the past few months, I’ve learned that developing certain habits can help to inspire creativity. So, I compiled and added all of Viz For Social Good’s data and inspirational resources to a list. I set a daily alarm and started adding one resource every day on a board in Figma, taking a screenshot and adding notes about what I found interesting. 

At the same time, I started collecting visualizations related to climate change, which was related to the subject of the project. By doing this, I gained more confidence in data physicalization and began making connections for my project without realizing it.

Lesson 2: Remember to leave some room for incubation.

During this project, I was staying in Istanbul, Turkey, and one day, I needed to travel to the capital city, Ankara, to visit the Colombian embassy. I spent eight hours on the train (four going and four coming back). So, I decided to have a day off, free from work and projects. I made a rigorous schedule for the trip that looked something like this:

• 7 am – 11 am: Train to Ankara
• 11 am – 12 pm: Take a taxi to the spinning studio
• 12 am – 1 pm: Spinning class
• 2 pm – 3 pm: Take lunch and walk to the embassy
• 3 pm – 4 pm: Appointment in the embassy
• 4 pm – 5 pm: Find a cafe and read
• 5 pm – 6 pm: Walk to the train station
• 6 pm – 10 pm: Train to Istanbul

As you can see, I wasn’t even thinking about physicalization that day — or so I thought. It turned out, this day ended up serving as my INCUBATION stage. It was not deliberate, but certainly fortuitous. How did it happen? Coming from an equatorial country, it was my first time in a four-season country during the autumn. The day was so beautiful; the blue sky was very sunny. I had an excellent spinning class, and I was listening to my favorite songs. On my way back to the train station, I was amazed by all the colors of the leaves, from green to orange, yellow, and sometimes red. Then, ILLUMINATION struck! What if I use all of these colors to make a visualization? Yes, I could use the leaves as the colors in a heatmap! I was coincidentally in the right place, in the right season, with the right weather and mood, and was working on the right project. Why did I not play the lottery that day?

I truly appreciated the preparation process while writing this article, and it made me pay more attention to the stage I usually overlook — the incubation. I find the incubation to be the most challenging part of the creative process since it’s not logical for me. I’ve often heard that you must “not consciously think about the problem to find the solution.” It seems contradictory, but that’s how our brains work. Since I usually focus on my work all the time, this experience made me consider scheduling more space to rest my mind and let the unconscious do its work. 

Lesson 3: Embrace the unconventional.

With this idea, I could move to the EVALUATION stage. After four hours on the train, I arrived home that night and got the global land and ocean temperature data from the NOAA National Centers for Environmental Information. I quickly made a draft of my visualization without thinking much about it. Later, I realized that this idea is quite wild and unusual.

With that concern, the following day, I approached one of the project organizers, Aida Horaniet from Viz For Social Good, to get her opinion on my idea. I asked her if it was too crazy to pursue. She gave me the best response: “Nothing is too crazy in this project!” Her encouraging words gave me the confidence to proceed with my idea. She is remarkable, and we need more people like her in our social circles!

I only had one minor issue to resolve. In all my adult life (even when I was a kid), going to a park and collecting leaves never crossed my mind. I am too shy to do that. I was afraid of what people might think of me, but please notice I couldn’t understand what others were saying about me because I don’t speak Turkish. Then, I realized that sometimes, we limit our creativity based on what others say about us. It was also amusing to see how my fears changed when I realized that I couldn’t understand the language spoken around me.

I also remember one of the goals of this project was to connect with your inner child. I decided to take this project as an opportunity to “leave” my comfort zone. I couldn’t remember the last time I did it, and these are the experiences we will never forget.

Lesson 4: Stay open to uncertainty.

I decided to go ahead with my idea, and as a meticulous planner, I began to carefully plan every detail for collecting leaves for my project. I chose to visit Atatürk Arboretum, one of Istanbul’s largest parks, on a day with perfect weather and researched the best way to get there. I was excited to find out that I could use the same train I used every day and only needed to go to the final station. Everything was set, and I was ready! 

So the day came, and I was very excited. I got to the last station, Hacıosman. I have to admit that I am always very impressed by the tile panels with Iznik patterns in the Istanbul stations—giant murals inspired by the city’s story with an ancient and characteristic style of this region. I usually stop to admire them, and that day, I was amazed to see one with a giant fig tree, my favorite fruit! 

I continued my adventure to the park, and from the outside, I was confident that I would collect all the leaves I needed for the project. But there was a minor detail that I didn’t expect. When I received my ticket to enter the park, there were some rules, and one of them stated that it was forbidden to collect seeds, flowers, leaves, mushrooms, etc., even if they had fallen to the ground. 

Despite my disappointment, I took the opportunity to enjoy the park and connect with nature. It was a calm and relaxing experience away from all the noise in Istanbul. It was also a time to reflect on the challenges in data physicalization. I had planned almost everything and needed to change my plans. When working with data physicalization, preparing and having workarounds or backup plans is essential. If you have a workshop or a collaboration, be aware that something may fail; that is when you must become more creative.

Later, during the final presentation of this project, Aida from Viz For Social Good mentioned something that caught my attention regarding digital visualizations. She said, “We lose attention to the details because everything is always there, and you just refresh. But when you build it with your hands, you have to adapt to every error, every mistake, and that makes you somehow very close to the data.” I couldn’t agree more! 

The following day, I decided to visit a public park close to where I was staying. And being in a historical city, there were many landmarks around me, and this wasn’t the exception. I visited the Aqueduct of Valens, which the Eastern Roman Empire used to supply water to the capital in the 4th century AD. I don’t think the ancient Romans would have imagined that, one day, the leaves of the trees surrounding the aqueduct would be used to create a data physicalization project!

Lesson 5: Data physicalization adds another layer of engagement

With all the leaves, I could proceed with the ELABORATION. I started by sorting them into different bags according to color. Then, I created a grid on a notebook page and used it to cut the leaves into the pieces I needed for a visualization. At that point, my question was the number of leaves I would need for each color. 

To determine this, I used a histogram to group all the temperatures and assigned different colors to each bar. I then numbered the leaves from green to red. I also used a table of temperatures from the years and months, organizing them from lowest to highest. Finally, I assembled the visualization by fitting the pieces together like a puzzle. The materials I used in this project were a computer, tape, glue, a marker, a ruler, scissors, two pages of a notebook, and, of course, the leaves.

What I liked most about this project was exploring a physical, tangible dimension of data. Instead of waiting for a machine to show you the colors in the heat map, I needed to add each color myself. Each color represents a temperature for each year and each month, so the time it took me to stick each leaf to the page gave me a little time for reflection and questioning.

When it comes to attracting the attention of our final user, data physicalization offers another level of engagement. When I finished the visualization, I shared it with the host where I was staying. Before knowing the information I had coded, he was intrigued by the arrangement and textures. With his attention on the visualization, it was easy for me to explain that the columns represent a month, the rows represent years from 2013 to 2023, and each color represents a global temperature. Would he have been equally interested if I had shown him a heat map on my computer? I suspect not, and I also would expect that this experience will stay with him for a longer time. It certainly will stay with me — the ideation and creative processes, and, of course, the final piece!

I was also curious about the data. Despite having many leaves, I only used three red squares representing the highest temperatures. After some research, I found that the strong El Niño conditions in 2016 significantly impacted two months during that year. Additionally, even though the weather we experienced in September 2023 was also influenced by the strong Niño conditions that had begun in June, it is clear that temperatures have been increasing over the past ten years.

A picture speaks a thousand words.

I couldn’t resist taking a picture with my visualization. I took it outside the city of Istanbul on November 1st, 2023. Usually, the weather would be rainy on this date, and I should have worn a coat or sweater. However, the temperature was so pleasant that I didn’t need them.

Conclusion

Participating in the “Physicalizing Data for a Better World” project has been an unexpected and enriching journey. Reflecting on this article, I find new motivation to continue contributing to projects that aim to create positive social impact through platforms like Viz For Social Good. I am pleased with all the lessons I have learned throughout this experience, and I can’t wait to continue practicing them in my future work: establish habits for inspiration, allow space for incubation, embrace the unconventional, stay open to uncertainty, and realize the added layer of engagement in data physicalization. 

This project has opened my perspective on the potential impact of data visualization and reinforced the importance of hands-on, unconventional approaches. I am very grateful to Viz For Social Good for helping others find new ways to be creative and to the team for allowing me to share my experience at the last summit. 

Special thanks to Marian Eerens for encouraging me to share this story in Nightingale.

The post Lessons from Physicalizing Data for a Better World appeared first on Nightingale.

I’ve been personally and professionally interested in 99-cents stores for a long time. Growing up in New York City, 99-cents stores were part of my retail ecosystem. It was a place to pick up everything from duct tape to candles to random cleaning supplies. There’s a ubiquity to them (75% of Americans live within five miles of a Dollar General), but I didn’t know much about them. As part of an art project I started in 2021 with my collaborator Gloria Lau, I mapped 99-cents stores in New York City to visualize a kind of place that’s often invisible. 

As an urban planner, I’ve spent many hours in GIS/QGIS trying to figure out the best way to display information about neighborhoods, land use, climate change impacts, the list goes on. There’s a formality to maps that are created out of necessity: They have to be legible to a variety of users regardless of the person’s level of data literacy or map savviness. Maps also need to be visually consistent. Whenever I made a land use map, my color palette was limited to the standard colors used by the New York City government. 

Working on this art project, I got to merge my urban planner/mapmaker brain with my art-making brain to think about visualization outside of traditional mapping. Part of what made this project possible was my ability to access hard-to-find datasets through the Brooklyn Public Library and through free data visualization tools like QGIS and resources offered by BetaNYC, a civic data organization based in NYC. 

Defining a 99-cents store

Compared with much of the United States which have discount stores that are franchises, 99-cents stores in New York City are majority-independently owned businesses. Therefore, the stores don’t follow a consistent naming convention, and a simple Google search doesn’t produce an irrefutable list. To map these stores, I had to create a working definition of these places to parse through all the different kinds of retail in the city. I defined 99-cents stores as businesses that market themselves as discount stores (ex. “Midwood Discount”, “Discount Deals”) or that have “99 cents” or some variation explicitly in the name (i.e. “Dollar Tree”, “99 Cents and Up”). After doing a quick search of the North American Industry Classification System (NAICS) and Standard Industrial Classification (SIC) codes most commonly used for dollar stores, I used a business search engine through my library to find stores in the city.

With my rough list of 99-cents stores I used a NYC batch geocoder tool from BetaNYC to spatially locate all the results from the search engine. I used Google Street View to spot-check addresses and confirm that the retail spaces were discount stores. When all was said and done I had a list of about 1,300 stores using 2021 data.

Translating data into art

The map provided a straightforward view of 99-cents stores in the city. It also revealed what neighborhoods had the highest concentration of stores. In New York City and nationwide, there’s a documented prevalence of discount stores in communities of color and communities that are in food deserts or food swamps, so much so that some communities have organized to stop their proliferation. Looking at the data, I started thinking of the hills and valleys of 99-cents stores across the boroughs and how I might be able to represent them in 3D form. As part of a larger exhibit, my collaborator and I wanted to present items from 99-cents stores in such a way to have visitors critically look at objects they may not otherwise pay attention to.

Using materials sourced from my local discount store, I created a 99-cents store contour map. After converting my point data into a heat map, I used a contour line tool in QGIS to create a topographical-like map of 99-cents stores. Using the map as a template, I then cut out individual plastic elevations.

The final map was roughly 3-feet by 3-feet using placemats for elevation, a vinyl carpet runner for boroughs, and contact paper as a base layer. My goal in doing this project was to represent dollar stores in an unconventional way, but it also turned into a lesson on data storytelling. I didn’t have to present a perfect dataset but rather share my findings in a way that might make a viewer curious about 99-cents stores in their neighborhood. The barrier to playing with this data was low thanks to my library and open source tools that made my analysis possible. The project has made me more curious about the possibilities of blending data and art and ways to make opaque institutions or systems more transparent through art.

To learn more about 99-cents stores in NYC and nationwide:

1. “Commodity City” : A documentary exploring China’s Yiwu International Trade Market, the world’s largest wholesale market and major supplier for 99-cents stores.
2. The New York Times : A 2017 article highlighting the stories of immigrant owned 99-cents stores in New York City. 
3. “God’s Garage” : An essay covering the history and expansion of 99-cents stores in the United States.

The post Exploring Open Source Data to Visualize 99-Cents Stores appeared first on Nightingale.

And what better gift than a data viz made just for him! Over the past year I’ve found personal data viz projects to be incredibly rewarding and meaningful. From contemplating life’s chapters at the Eras tour to creatively charting my career path, data viz has gone hand in hand with personal reflection. All of these thoughts, plus the milestone of first meeting my boyfriend exactly 10 years ago this August, made me want to visualize our relationship somehow. 

I had a few stipulations for the design of this viz: 

1. The data had to be both meaningful to our relationship and relatively easy to compile. 
2. The visualization had to be a graphic that’s aesthetically pleasing from afar—you shouldn’t have to understand it to appreciate it… 
3. And at the same time, it should be easy to explain the underlying meaning. 

I decided to print my graphic on a t-shirt for him. Not only would this make my gift tangible, but it would also serve a practical purpose. (Let’s just say my boyfriend has a talent for wearing his tees down to their very last tattered threads.) 

The creative process

1. Gathering meaningful data

He may be my boyfriend now, but over the past 10 years, we’ve been lots of things to each other: friends, lovers, and even exes. This would make a relationship timeline pretty dynamic. To break up those long swaths of time, I decided to chart the trips we’ve taken together. These dates, easily plucked from my photo collection, became not only data points but also meaningful stops down memory lane—an unexpected quality of the data collection process.

2. Sketching out ideas

My initial sketches were simple–made with just colorful pens and paper, a surprisingly popular tool in the data viz world. When I settled on the final design, I recreated it in Figma. 

I loved how, from a distance, the final graphic resembled a whimsical, scribbled note–a nod to my boyfriend’s vocation as a writer. The minimal design and limited color scheme also complemented his understated fashion preferences.

3. Interpreting the design

The inevitable question, ‘Hey, what’s that awesome design on your shirt??’ comes with an easy explanation.

Each line marks a year of our journey, the pattern indicates our relationship status, and the thicker blocks represent our shared adventures, each color-coded by location.

His and Hers reactions

Looking at the final design gave me a newfound perspective. I saw patterns and made connections that I wouldn’t have otherwise discovered. For example, longer trips taken at the same time in consecutive years evolved from spring breaks to spring weddings! And as for our three-year break—which had seemed insurmountable when we began dating again in 2020—it now appeared like a blip, a short pause in a long and rich history. And with each passing year it will only represent a smaller fraction in an expanding timeline.

Equally telling was what was missing: In most of 2021, we barely took any trips. During this period I was in school getting my masters and living the frugal life of a student, while my boyfriend worked unpredictable hours at a restaurant. Financially, these were tight times, but I remember them now with a certain fondness.

Another realization was that we haven’t been to South America or Asia together (yet!). 

My boyfriend’s reaction upon receiving the t-shirt was priceless. When I told him I’d designed it, he connected the dots in record time! He guessed that it was a visualization of our relationship and deduced almost everything except the specific color coding of the trips. As I walked him through my process, his appreciation and his smile grew. By the end, he was deeply moved and proudly put on the shirt for our evening out together.

The data that keeps on giving

When I first embarked on this project, I didn’t realize that the final product would end up being a living, evolving chronicle of our time together. I see it as a ‘data album’ of sorts—a place where memories are not only stored, but also showcased and updated. I’ve already added forgotten trips to the initial design and can’t wait to update this snapshot of our lives as time goes on. The best part? I can keep showcasing our story in various forms!

As the holiday season approaches, I invite you to explore the idea of a personal, data-driven gift, maybe even one co-created with loved ones. Categorizing and contextualizing old memories can be a touching journey. Let data visualization transcend the realms of dashboards and scrollytelling; let it be a unique medium to encapsulate personal stories and ignite powerful emotions. Because if you’re a regular reader of Nightingale like I am, chances are that your love language is data, too.

The post A Decade Wrapped – Plotting Nostalgia for a Data Viz Gift appeared first on Nightingale.

For someone like me, emotions are as complex as a Gordian knot, but I’m certainly not a psychologist; I am a fellow human who experiences them in the typical human way. I’m learning to identify and embrace them, allowing them to shape my existence. It’s no easy task, but the rewards are immeasurable.

Emotions can be both overwhelming and vital. Without them, life would be devoid of color, a monotonous existence. When I think about emotions, it reminds me of color and temperature. This led me to explore how emotions are represented from the perspective of data science — the perspective I’m most familiar with.

Capturing emotions

My first question was how do we capture something as complex as emotions? I searched the web in an attempt to find datasets on emotions. I thought that emotion could be detected from facial expression but also from text. I also thought that one data science task particularly shares some similarities with emotion detection: sentiment analysis. Sentiment analysis could be seen as an extremely coarse grain emotion detection task with only three categories: neutral, positive, and negative.

After perusing the popular data science platform Kaggle, I unearthed an intriguing dataset: “Emotions Dataset for NLP” along with an accompanying article detailing its collection. The task was to classify sentences by six emotions: sadness, anger, surprise, fear, love, and joy, much like the adorable world of Pixar’s animation, “Inside Out.” Although these few emotions also seemed very coarse grained, I decided to give it a try and see how they look.

The journey begins

I became eager to “visualize these emotions” and gain a deeper understanding by organizing the data based on multidimensional representations of their corresponding textual descriptions. Thus began my journey across the emotion space.

How does one truly delve into the depths of this dataset? I was curious about the authors’ decision to select only six emotions out of the countless possibilities. Upon loading the data into a DataFrame, I set out to truly grasp what lay within. The dataset presented me with two primary aspects: textual descriptions (coming from tweets) and corresponding labels reflecting emotions. For instance, I stumbled upon an entry stating, “I feel strong and good overall,” labeled as “joy.”

Rather than embarking on a conventional classification task, where I would train a model to predict emotions based on labeled sentences, I sought to explore the dataset itself and its representation. Leveraging a language model with universal sentence embedding, I transformed each sentence into a long vector of numbers, positioning them within a latent space according to the language model. Employing a dimensionality reduction technique, I extracted the three most informative components from the extensive vector and plotted them. Admittedly, it didn’t offer any groundbreaking revelations. Dimensionality reduction techniques are commonly employed to gain insights into datasets, but even with the added labels, I wasn’t convinced that discernible patterns were emerging.

One lovely tool I really enjoy is tensorboard which lets the user visualize the latent space of the model. With its help I loaded the obtained vector-based representation of descriptions (also called “embeddings”) into tensorboard and applied UMAP dimensionality reduction out of the box. You can see it below.

Creating emotion cubes

I assigned labels and hover-over descriptions to each data point. Upon closer inspection, I noticed numerous clusters of points huddled closely together. Intriguingly, instead of running a cluster analysis, I became captivated by these individual data points and their neighborhoods. A thought struck me—if I could encapsulate neighboring points within cubes, it might yield fascinating insights when analyzed cube by cube or at least interesting mixtures of “color-emotions.” I wondered how to cover the neighboring points in cubes. Because I worked in a three-dimensional space, I imagined it as a huge storage room that I would fill with small cardboard boxes. Something like in the picture below. In each box we could place emotions that lie together. Of course there won’t be any gravity force to keep the full boxes on the ground but the idea seemed interesting enough to try it out. 

 Thus, I divided the latent space into small equally sized cubes and assigned colors to represent the emotions they contained. With a touch of transparency, the cubes came to life — how exhilarating it was. It took me a while to realize that the majority of cubes that filled the space were empty and thus they were covering the emotion cubes, so I decided to remove “empty boxes” and leave only boxes with emotions.

Initially, I contemplated coloring each cube with the most dominant emotion it contained. However, as I immersed myself in the project, I realized that certain emotions are more intricate, often comprising a mix of several emotions. I thought, why don’t we look at the six labels as basic building blocks and see what will happen when we blend the basic emotion colors together? Perhaps this blend could offer us unique insights into the emotional landscape. Consequently, I experimented with blending colors in proportion to the composition of emotions within each cube, settling on translating colors into CIELAB space and mixing the dimensions there.

And there we stood, surrounded by multiple little cubes brimming with colors—anger, sadness, joy, love, and everything in between. Inside these cubes, I placed the corresponding data points, allowing the chart to rotate and facilitating exploration of each cube and its contents. You can access it here.

I searched for other data as well, and came across the GoEmotion dataset. This dataset looks at the more complex emotions, it lists 27 of them and a neutral state, as well. We can see below how mixing the colors by emotions makes each cube like no other.

Going a step further I thought, what if we use these emotion datasets and basic building block emotions as the initialization and we let the data mix the emotion-colors in the cubes and extract the emotionally inspired color palette at the end? See below for the extracted colors.

After thinking about palette color I thought about the sound of emotions; one way to expand upon this project would be to add a layer of sonification to the cubes. 

Challenges and reflections

Working on this emotion problem made me wonder about all the different aspects of capturing the data and especially how simplified our models are. I explored individual data points (as “above all show the data” following Edward Tufte) and sometimes wondered how someone labeled them with such emotion. Also, how can you capture such complexity by flattening the emotion to just one sentence, when you cannot hear the voice or perceive the “emotional state” someone was in while uttering the sentence or a sound? I suppose George Box was right again when he said that “all models are wrong, but some are useful” and we should always have that in mind while looking at models.

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Martina Morini: “Mourning You”: Every day hundreds of people attempt to cross the Mediterranean Sea in search of a better life. Some of them succeed; some do not. In February 2023 I was onboard an NGO vessel that found a boat that had been adrift for six days. This viz tells this story.

Núria Altimir: This piece uses embroidery as a metaphor for beauty and interconnection in nature. I embroidered a flower with 18 petals, one for each 5-year period from 1937-2022. Each petal carries as many strands as the percentage of remaining wilderness at that time. The loss between periods is represented by falling strands.

Parvathy Raju Arangath: The Binge Blanket is a hand-woven blanket embedded with data regarding my Netflix viewing activity for 2021. The Data is embedded into the strands of each yarn used to weave it. Each thread of the yarn depicts an hour of the binge from a day in 2021 and the colour depicts the genre of the media viewed. Beads on each end of the Blanket denote the date of streaming. Using the medium of Blanket to depict this data was apt since I almost always Binge watch Netflix from the comforts of my bed, wrapped under a fluffy blanket!

The main aim of this tangible data visualisation was to understand and see patterns relating to my viewing behaviour, which genres and media I view often, and so forth.

Ellen Bechtel: My best friend wore these sequin pants to their wedding! I helped them hem the pants to the right length by cutting off 1 inch of fabric, which got me thinking… sequins are are a great metaphor for messy data. What can we learn by unraveling this sequin fabric?

• By counting the sequins 1 square-inch sections, I learned that the average sequin density is about 19 sequins per inch.
• By ripping each sequin out of the fabric, I learned that each sequin was silver on one side and colorful on the other.
• By sorting the sequins by the colorful side, I learned that most of the sequins were split-colors, but that the most single-color sequins were warm colors (yellow, orange, and pink).
• By clustering the split-color sequins, I learned that the color splits weren’t random – there were only ever a few combinations. That suggests that the sequins were punched out of a single sheet of sequin paper that had rainbow stripes on it in the pattern of pink > orange > yellow > green > blue > purple.

Elsie Lee-Robbins: “Canaries in the coal mine”: Coral reefs are the canaries in the coal mine, and they are dying. Warming oceans caused a global bleaching event from 2014 to 2017, which resulted in bleaching-level heat stress for 75% of global reefs; with 30% of them dying (NOAA).

Luciana Brito: Ten are the letters of the word Felicidade. In the ten days portrayed, Lu Brito immersed herself in her Ph.D. research, analyzed data, wrote part of an article, and explored new knowledge. Each geometric square in the work symbolizes her activities: yellow, reading articles; purple, thorough data analysis; pink, dedication to writing an article; green, incessant search for new knowledge. The iconography portrays what the author shared in Instagram stories during the ten days of research: it includes cats, dogs, a parrot, some pictures (camera), songs (piano), and a palette, reflecting her connection with nature and different types of art, where she seeks inspiration and joy.

Kathryn Hurchla: A knitted row determines the next and becomes a chain not easily raveled. Undoing a row may switch a pattern, but if you never cast on new stitches, your make is bound by those threads you fold back in. Markov Chains can model the probability engines of artificial intelligence but are also a way to peek into AI’s demise if models become consumed by a monolithic flood of synthetic data of its own making.

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