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 recently undertook my very first project in the field of Data Humanism some months back. I saw the workshop called Visualizing Complexity Science in the Data Visualization Society’s event calendar. After reading through the overview and going through the research projects at The Complexity Science Hub (CSH), I decided to apply. It felt like an opportunity that was in line with my quest to learn about this interesting field of Complexity Science and deep dive into the world of Data + Art. I was then selected and joined this five-day immersive workshop in the beautiful city of Vienna, Austria.

During the workshop, I worked on one of the presented research projects, Forests, which was based on the scientific paper Growth, Death, and Resource Competition in Sessile Organisms (Edward D. Lee et al., 2021). The dataset that I worked with was the Southeast Alaska (Schneider et al., 2020) forest dataset. Exploring the dataset, I thought, how could I convey this complex information about these trees in a simplified manner to a diverse audience that was not necessarily in my same profession? Yes, the workshop participants included artists, journalists, scientists, designers, and software developers; what connected us was that we shared the same passion — data visualization.

I had a eureka moment when the concept of Data Humanism came to my mind and I decided to put my knowledge to the test. The fractal patterns of trees and that of the human nervous system have striking similarities. The context of humanizing data therefore fits perfectly with nature — a living ecosystem and more specifically in our use-case here with trees. 

Let’s now deep-dive into my work in four phases:

Brainstorming & Prototyping Phase

To convey to the audience the results of our data analysis, we have three contexts in data visualization namely, Presentation, Dissemination, and Exhibition. For the Southeast Alaska dataset, I decided to use Exhibition, which focuses on a visual installation to educate and engage a broad audience. Precisely, I thought of implementing this through an interactive artwork. I started by studying and exploring the dataset and learning about its features. This was followed by brainstorming, feature selection, and sketching my ideas. 

Once I had the initial structure in mind, it was time to move to the implementation phase.

Implementation Phase

I selected three metrics from the dataset to visualize: the species, the tree number (which is a unique identifier present in the dataset for each tree being surveilled), and the tree height. Next, I laid out the poster with descriptive elements like the title and subtitle and then continued to define the context of how to read the data visualization as shown below:

• For species, I assigned each of the six species a different color
• For tree number, I used the assigned number in the dataset, or a “?” if none was present.
• For height, I assigned slanting lines to correspond to the height, or a green dot sticker to represent a sapling

I selected a triangle to denote each data point as it best represents the structure of a tree and started to cut out the shapes. This was the most time consuming process I must tell you! Luckily, the tips and tricks learned at arts and crafts classes in school came in handy to speed up the process!

I then started to map each of these triangles into rows with the selected features from the input dataset. 

For example, consider an entry in the dataset where the tree had species TSHE (Western Hemlock), was number 15, and height of 4 feet. I would first create a pink triangle or a triangle with a pink border (the color for that species), then use black ink to write down number 15 for the tree number, and, finally, draw four slanting lines in brown to represent the height of 4 feet.

In the end, we have something like below:

I continued to map the remaining rows to the data points as triangles and pasted them on the poster. I clustered the trees belonging to the same species in groups, sketched the type of leaf of the particular species of tree, added data insights and other relevant information to aid user understanding. I purposefully did not complete the entire poster in order to add a layer of interactivity, where the audience could each pick a data point, transform it on their own based on the defined legends, and then map it to the relevant cluster on the artwork.

Isn’t this called enjoying the process of learning?

Once completed, we can see how Data Humanism:

• Goes beyond the technical approach of representing data.
• Personalizes design and display of complex intertwined data in a user-friendly way.

The result was a complete transformation of raw data into a well-presented and interactive artwork depicting data insights that can be understood by all.

Before:

After:

Presentation Phase

I presented the idea and concept behind the poster, explained to the workshop audience how to read and interact with the data visualization artwork, and encouraged audience members to try it out and map the data points on their own. Finally, I also explained to them the visible data insights, including the following: 

• Southeast Alaska has five identified species of trees including Red Alder, Alaska Cypress, Sitka Spruce, Popple, and Western Hemlock and one species is unknown.
• All the trees are surveyed and hence have a unique number assigned to them.
• All are not fully grown trees but there are also saplings present in the forest.
• All the trees/saplings belonging to a species are clustered for easy analysis. For example, the species Red Alder has a total of 17 trees out of which eight are saplings, and the species Popple has a total of nine trees out of which three are saplings, and so on
• The unknown species has only one member which is numbered and is a sapling

These results provided the audience with an initial analysis and overview of the dataset and at the same time paved the way for further deep dives and questions like: Can the unknown species be mapped with the existing species, or is it completely new? Did this species exist before? Is this species on the verge of extinction? Given that there is only one sapling in the species, should further measures be taken to safeguard it from extinction? and many more!

Feedback Phase

During and after the presentation, the feedback from the audience was very positive. They enjoyed learning about the process and conceptualization, and interacting with the artwork.  

Below is a snapshot of some of the feedback provided by the audience, coupled with the feedback about the other artifacts developed by my team members. Reflecting on the feedback, I found the audience appreciated the interactivity and the fact that they could touch the data; they liked this physical representation of the data simplifying complex datasets for all. Additionally, they felt that the illustrations chosen on the poster were full of character, and they also believed this artwork to be a great fit for a museum setting!

This article was edited by Catherine Ramsdell.

The post Art in Numbers: Crafting Insights Through an Interactive Exhibit 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.

As part of the conference planning team, we, Claudia Berger and Chris Alen Sula, wanted to produce a visualization of the conference work itself, while also reflecting the themes of the event in our work. Claudia is the Digital Humanities Librarian at Sarah Lawrence college and a Visiting Assistant Professor at Pratt Institute’s School of Information teaching digital humanities. Their research centers on how crafts can be integrated into digital humanities projects. Chris is Associate Provost for Academic Affairs at Pratt Institute and Associate Professor in the School of Information. His research is on digital humanities, information visualization, and the ethics of data/technology. He also was the conference chair for HASTAC 2023. As our responsibilities planning the conference did not give us time to present our own work, we wanted to find a way to take part in the content of the conference through our interest in data visualizations. 

Data physicalization felt like an appropriate method because it built off the work of Shubhangi Singh, a graduate student in the MS Program in Information Experience Design who made the logo for HASTAC. Her design invoked networks in the logo, which was then recreated in yarn at the conference. We decided to pair that installation with a network of session data reflecting keywords and tags submitted by presenters as part of their proposals to the conference. While neither of us had created an installation like this before, we knew it was going to be a lot of work, but we certainly learned firsthand just how much harder it was than we had anticipated.

This article explores our process of making the physical data viz, from initial conceptualization through installation. While some of the challenges we discuss here are particular to our network, others likely apply to other data physicalization projects. Perhaps most surprising is that we did not even finish the installation as planned, and yet the result still accomplished many of the goals we set for the piece. We hope some of the lessons we learned will be helpful to other folks looking to undertake similarly-scaled data physicalization projects in the future.

Concept

Our concept for “Crafting Connections: Creating a Network of HASTAC 2023” is reflected in the following statement, which was displayed alongside the network diagram at the conference:

Networks express connections, associations, and communities. Here, topics of the conference (via author-selected and author-generated keywords) have been carefully detangled and placed into conversion with each other and made physical in form. This social object can be viewed and interacted together with others, making the conference community tangible.

Each thread represents one work in the conference, its color reflecting format: papers, panels, and roundtables (green); workshops (orange); exhibitions (red); performances (purple); activities (blue); and multiple formats braided with their respective constituent colors. This design reflects some of our earliest thinking of the conference, which is also embodied in the HASTAC 2023 logo.

We invite you to identify and locate different work from the conference in this social object and to trace its lines in connection with others.

Data and Network Design

We used ConfTool, a conference and event management software, to manage submissions to the conference. Authors were asked to provide keywords for their abstracts and to select from a list of academic disciplines that best described their work, which allowed us to download a CSV file of all of the metadata for the accepted submissions. Using OpenRefine, we merged and normalized keywords for accepted submissions (e.g., combining “map,” “maps,” and “mapping” into a single term). We were able to whittle down the list of 900+ tags/keywords to 347, which created more meaningful groupings even if it flattened some nuance in how those terms were used. 

Simultaneously, we had to decide how our network would be physically constructed: would each session be a node with the keywords serving as the edges that connected them, or vice versa? We took a small sample of data—four sessions that shared four keywords—and oriented the network in both ways. In order to make a quick mock-up, we used the three-digit ID numbers ConfTool assigned to each session in place of the full session name. 

Ultimately, we decided to have the keywords serve as nodes. It felt like viewers might recognize topics faster, and we liked how quickly this network represented the scope of the conference. It showed how interdisciplinary the work was, and making each session an edge reinforced the metaphor of conference work as holding the network—and our community—together. 

Based on this approach, we processed the session data using an R script to pair each keyword with other keywords for each session, creating an edgelist for the sessions as a whole. Using Gephi, we generated a network layout and filtered  out nodes with fewer connections (i.e., lower degree), ultimately landing on 87 keywords in our network. This number balanced detail with legibility and would still be feasible for us to construct. We re-processed this data several times as session data changed based on conference registrations and withdrawals.

In addition to layout, we also needed to decide how color would work in this network. We knew that we wanted to use the colors from the visual identity of the conference, but how the colors would function in the network was not yet clear. In the sample networks we generated, color was used to highlight clusters of nodes based on related topics. There were many other options, and we weren’t sure how we wanted to proceed: color could represent location of where the presenters were from, or presentation themes, field, or format, etc. Eventually, in order to further represent the range of work and format at the conference, we landed on using colors to represent the different types of sessions (paper/panel/roundtables, workshops, exhibits, performances, and activities), leaving the nodes as black text on white background to enhance readability. Later, in setting up the conference program in Sched, we used this same color coding on the calendar of events to help attendees differentiate the different offerings.

Install

Our installation was located on a set of bulletin board walls in the campus’s Student Union. This backdrop limited what materials we could use to create the network. We wouldn’t be able to install hooks or nails that other data physicalizations have used. Instead, we glued pushpins to the nodes, which were printed on bristol board and cut before being attached to the wall.

At this point, we noticed that the proportions of our digital layout did not match the width of the wall available to us. If we had kept the original square layout, some nodes would be too high and too low to work with, or for viewers to see and interact with. To adjust the layout, we moved some of the more central nodes on the bottom and top of the network and also moved other nodes horizontally to spread them out across the space.

Instead of making a true keyword network (connecting keywords each time they co-occur in a single session), we used one piece of yarn for each session and created more of a circuit, traveling to a particular keyword each time it was used by an author. Initially, we followed the order of the tags as entered by the authors—preserving any importance the authors may have felt about early tags—but this sometimes required crossing the entirety of the installation multiple times if nodes were on opposite ends. Early in the process, we pivoted to rearranging the order of the tags to create a more optimized route, keeping the author’s first tag intact in case it was some sort of primary or important label. While it took a little time to go through the lists, this made installation easier, faster, and used less yarn. 

To make the installation interactive and explorable for conference attendees, we added a tail to the start or end of each circuit, to which we attached a label with the session title. This design allowed participants to navigate the network and find related sessions, as well as giving visual weight to keywords that were more frequently the first keyword used by an author. Nodes with multiple labels hanging from them stood out from other nodes that were of equal or similar size. Whether or not the authors intended for the first keyword to be the most central one to their work, it highlighted patterns; for instance, when “digital humanities” was one of the listed keywords, it was usually the first one. 

Lessons Learned

If we were to do this style of data physicalization again, there are a few things that we would do differently. 

Layout

Even with the changes we made to the layout, some of the nodes were too high to work on comfortably. It was hard to firmly secure them to the wall, and some fell off as we tried to install the yarn. Even the tallest member of our team had difficulty reattaching them securely. 

There are a few ways this could be addressed in future installations. First, when designing the initial network, we could ensure that the digital layout proportions match the actual space being used. That way, fewer changes would be necessary. Second, when we attached the nodes, we started with the center of the network (arts, community, design, and participatory methods) and then worked our way out. This pushed the periphery too far out and created a lot of empty space in the center of the network. We could’ve had those four nodes closer together, but we didn’t notice that until much later. To counteract this, we could start by placing outer nodes first and work inwards. This would ensure the upper and lower bounds were reachable and legible while minimizing the wasted space in the center. While there is a risk that this would cause the center of the visualization to be cramped, it prioritizes making the installation physically less taxing on those working on the project.

Construction

In addition to our difficulty reaching the nodes, it was sometimes hard to attach the yarn to them. We started by wrapping the yarn around an individual pushpin on a node, but quickly found that some pushpins were too close together to wrap them effectively. After a time, we started wrapping the yarn around the circumference of the node, which took more yarn and sometimes led to awkward connections between nodes. Our process could be improved by using fewer pushpins, creating more space between each pin. Or, instead of gluing the pins around the outer edge of the node, we could move them closer in, making it a smaller circle of pins closer to the center of the node, making it easier to wrap the yarn around the entire node. Both methods would also make it easier to attach the node to the wall, as there would be fewer pins to line up and push in. 

Time versus labor

The biggest thing that we would change is the installation of the piece itself. Part of the reason we were unable to finish the work was because it was our first time making a physicalization project, and we didn’t budget enough time for installation, along with our other conference responsibilities. In the future, we would start earlier to allow more working time. 

More workers wouldn’t necessarily speed up this project. Having even two people attach yarn at the same time created maypole-esque tangles, and installation worked best when one person read off the order of the nodes and the other attached the yarn. Still, the installation took a physical toll—reaching, stretching, manipulating yarn with fine detail—and having multiple workers in shifts might lessen the burden on individual bodies. 

Another way to address installation, now that we have a sense of the labor that goes into it, might be to simplify the design of the network as a whole. We could’ve used a random sample of sessions instead of trying to represent every single one. Or, recognizing that we would not finish all of the sessions, we could have cycled through colors so that the overall look we wanted could be achieved, even if the data was left incomplete. By striving for partial completeness representing all of the papers/panels/roundtables in a single color, we ultimately produced a flatter visualization than we had intended.

Unfinished, and yet…

This project was about representing the community of the conference, and in the end it both represented and helped foster community at the conferences. Attendees explored the installation together as it was located in a key area of the event that served both as registration and the location of food and coffee throughout the three days. This was a place where people gathered and there were couches nearby where people could rest between sessions. While the plan had been to have the installation completed before the conference began, installing it during the first day of sessions actually helped to encourage conversation about the piece. We were present to answer questions and talk about it while it was under construction, and attendees enjoyed seeing the process and checking in over the course of the day on how the work progressed. 

While the final result was not a technically accurate network of the data, that wasn’t the goal of the piece. We were using data physicalization and introducing art into the process of our network to help us tell a story about the data. Some of the “inaccuracies” like having to move the nodes helped emphasize the human element of literally having our hands on the data, and in some ways the installation turned into a performance of the labor of datawork itself.

The post Crafting Connections: Lessons from Installing a Data Physicalization appeared first on Nightingale.

A few steps away is the footprint of a tree, replicated in stone. Dozens of feet across, it conveys the scale of these colossal trees in a way that you otherwise can’t get, even standing next to one.

Both of these exhibits are examples of physical visualization — data represented in space beyond the constraints of paper or screen. Together, they demonstrate many of the technique’s benefits.

By expanding into an additional dimension, the map allows direct comparison of the size and shape of the most prominent trees. In a flat map, those qualities would have to be encoded with abstract visual parameters (shape, size, color, etc.). This encoding increases the amount of thought and attention required to parse the information in the map. Direct representation leaves more mental bandwidth to explore and understand a visualization, rather than using it on interpreting symbols or abstractions.

Also noticeable are the bright and shiny tips of the most prominent trees — polished smooth by curious visitors. The tactile display draws attention and even encourages direct interaction. People who may have glanced at or totally ignored a traditional map of the grove are drawn to interact with a physical one.

Likewise, the full-size representation of a sequoia’s base encourages engagement, inviting viewers to step into the footprint. This gives a sense of scale that is absent even from the base of a living tree. Our limited stereoscopic vision just isn’t up to the task of evaluating the size of an object that rivals monumental sculpture for size (the largest giant sequoia are roughly the size of the Statue of Liberty). But by stepping into a full-scale representation of a tree’s base, one can better conceive the bulk of a living tree.

Our limited stereoscopic vision just isn’t up to the task of evaluating the size of an object that rivals monumental sculpture for size.

Despite often relying on industrial techniques for their construction, I consider many examples of physical data visualization to be handcrafted because of the thought and attention required to design them. Every detail is intentional, and can be used to better convey the information contained within.

Consider the subtle traces of trails in the bronze map, or the different materials used to indicate a tree’s bark and heartwood in the giant sequoia’s footprint.

Another feature of physical representations of data is the ability to convey three spatial dimensions. I won’t say “without distortion” since we only see in something like “2.5-D” (a term often used to describe vintage games like Doom, where there are x, y, and z coordinates but no overlapping spaces). In general, our brains know how to accurately interpret shapes based on visual stimulus. But any 2-dimensional representation (a screen or sheet of paper) of a 3-dimensional surface will be incomplete.

Breaking Boundaries with Globes & Models

This limitation applies acutely to maps — all maps will distort at least one of these four properties: area, distance, angle, and direction. For large scale maps (maps that show a limited area) these issues are usually moderate, but for small scale maps (those showing a wide area, like a continent or even the entire Earth’s surface) the distortions can get out of hand.

A time-honored solution to the constraints of flat maps is the globe — wrap a map around a sphere and area, distance, angle, and direction are all well preserved (leaving aside the fact that the Earth isn’t quite a sphere).

Globes are created by printing gores — slices of the Earth surface between lines of longitude — that are then pasted onto a substrate. The image below shows 24 gores used in Adolf Henze’s 1891 globe (the “the largest printed globe produced in the 19th century” according to the Rumsey Map Center). Unfortunately they don’t have any photos of an assembled globe made from these gores, but they do have a spiffy interactive 3D render.

Compare the projection used for the gores with the same map in a geographical projection (a simple grid of latitude and longitude, also known as a Plate Carrée, rectangular, or simple cylindrical projection) shown below. Shapes, angles and distances are all better preserved by the gores, but only after they’ve been assembled in 3D space.

A final advantage of a globe over a flat map — there are no edges! Sail as far as you want, you won’t fall off.

Mapping the surface of the Earth onto a plane is hard enough. Mapping a complex of tunnels and subterranean mining claims beneath a mountain in desolate western Nevada is another challenge altogether. This particular scenario was faced by the owners and operators of the Comstock Lode mines, a rich silver and gold deposit located beneath Virginia City, Nevada. Knowing the exact location of the mine workings was critical for construction of the tunnels, safety of the workers, and (of course) to ensure the claim owners profited.

To get a sense of the challenge of interpreting a three-dimensional network in the two dimensions of paper, take a look at the following two maps. The first is a cross-section through Mount Davidson, showing the vertical location of ore veins and shafts, paired with a top-down view of mining claims. 

The second is a top-down view of the complex of tunnels deep within the earth, with colors indicating depth.

Having trouble re-creating a mental map of these tunnels? Yeah, me too. And I suspect the original engineers who built these tunnels had similar issues, so they built a model, which is now located at the The Way It Was Museum in Virginia City. In this model spatial relationships which are nearly impenetrable on paper come to life.

3D Models of Abstractions & Microcosms

The examples I’ve shown so far have all been one form of map or another, but multi-dimensional data doesn’t need to be spatial, and physical visualizations aren’t limited to representing places.

This visualization of electricity usage in the city of Manchester (known as a “load model”) shows three abstract variables. Each card represents the amount of electricity consumed in Manchester, England every 30 minutes over 24 hours. Look closely and you can see that each day is carefully annotated with details like min and max temperature, times for sunrise and sunset, and details of the weather.

Stacked together, the cards transform from a two dimensional representation to a three dimensional one, adding a day of year axis to time and power (in Megawatts). This allows viewers to see seasonal patterns of energy use in addition to the rhythms of night and day. (Thanks to Valentina D’Efilippo for making me aware of this amazing model.)

Sometimes, a physical representation of data isn’t meant to communicate, but is an essential part of the interpretation of experimental results. For example, crystallographers — scientists who study the arrangement of atoms in matter — used models to unravel the structure of complex organic molecules like penicillin, insulin, and DNA.

X-ray crystallography is an essential technique used to determine the arrangement of atoms within molecules. The process involves beaming x-rays, which have a wavelength similar to the size of a single atom, through a crystal. Since the atoms in the crystal are arranged in a regular pattern, the x-rays diffract (just like ocean waves will diffract around a pier or visible light waves will diffract through a narrow slit) as they pass through it. This creates a pattern of amplified and attenuated x-rays which is then captured on film. By recording these patterns taken from different orientations of the sample, a crystallographer can calculate the pattern of “electron density” in a crystal, which corresponds to the molecular structure.

During World War II, Dorothy Hodgkin led a team of crystallographers investigating the structure of penicillin. X-ray diffraction patterns were deciphered slice by slice, with each atom represented by concentric rings of electron density — the more rings, the heavier the atom. (See Modeling the Structure of Penicillin for an excellent animation showing the process.) Hodgkin drew the contours onto sheets of clear acrylic, which she then stacked to create a 3D model of the crystal (below). The groundbreaking research earned her the 1964 Nobel Prize in Chemistry.

A more conventional representation of molecular structure is the ball-and-stick model (below, again by Hodgkin). Also a form of data physicalization, these models identify each atom and represent the bonds between them, at the cost of portraying atoms as solid spheres. In reality, atoms are fuzzy blobs of probability. A characteristic I find better represented by the hand-drawn electron density model.

Another application for data physicalization is as a means to preserve delicate samples. The Harvard Natural History Museum’s collection of glass flowers (the official name is “The Ware Collection of Blaschka Glass Models of Plants”) is one such example. These exquisitely sculpted models capture ephemeral objects — flowers in bloom, a blighted apple — in a durable form that’s able to be handled and examined long after the original specimens have decayed.

Even something as abstract as a statistical concept can be captured by physical models. The Eames’ Mathematica exhibit (currently installed in the Boston Museum of Science, New York Hall of Science, and Henry Ford Museum) includes a giant bean machine (also known as a Galton Board) that creates a real-life binomial distribution through balls bouncing off pegs and into narrow columns. Each column functions as a concrete histogram or bar chart, the more balls, the higher the column. Even though the fine details of each run differ, the overall shape — a bell curve — is re-created every time.

Despite the utility of conventional data visualization, there’s something uniquely powerful about data made tangible. Information presented in a concrete way invites viewers to interact with the data in ways that many visualizations on paper or screen do not. Viewers are free to change perspective to reveal something hidden, or pick out an interesting detail to examine more closely. Interactivity that is inherent in data physicalization needs to be deliberately added to 2D visualizations. I suspect that is a big part of the appeal of scrollytelling (another medium that takes a surprising amount of manual work to do well), and an avenue of exploration in virtual reality.

A downside of data physicalization is the expense of creating physical objects, both in material and time. On the other hand, the expense compels creators to think carefully about their work, which is a key ingredient in crafting compelling data viz.

NOTE: This is a reprint from the original article that ran here.

The post The Power Behind Physical, 3-D Visualizations appeared first on Nightingale.

Organised by a campaign group called COVID-19 Bereaved Families for Justice, the previously blank two-metre high wall was hand-painted in March 2021. Despite never being officially sanctioned and a permanent status remaining uncertain, volunteers continue to maintain the mural today. Personal messages written inside many of the hearts pay tribute to loved ones. 

Symbolising each individual death separately echoes approaches used elsewhere, which have represented lives lost with flags, chairs, wooden crosses, felt roses and origami triangles. However, a distinctive feature of the National Covid Memorial Wall is that the painted hearts occupy one linear surface, rather than being distributed throughout an open space. Limited scope for viewing at a distance means one encounters this visualisation by walking its length, each heart gradually becoming visible. Importantly, experiencing its full extent takes much longer than viewing a typical COVID-19 chart. The prevailing effect is a profound sense of the magnitude of the number who died. 

The notion of a massive number might arise from the considerable time taken to walk the length of the wall (half a kilometre). Disbelief about the duration of this journey ensues as one’s expectations about its extent are violated. Attending to names and messages forces a slower walking pace, extending the duration even more. This acute sense of time passing heightens one’s experience, contributing to the overall impression of a very large number.

Other effective visualisations also exploit their audience’s time to convey large magnitudes. In Wealth, Shown to Scale by Matt Korostoff, each pixel represents $1,000. Consequently, Jeff Bezos’ staggering riches occupy a vast amount of graphical real-estate. However, a computer screen can only show a tiny proportion of these pixels at a time. Therefore, a complete viewing requires lots of scrolling. Again, the sense of considerable time passing substantiates the enormity of this multi-billion dollar figure. The laboured progress of the scroll bar on its protracted voyage across the screen only adds to the effect. 

It’s easy to overlook temporal aspects of visualisations when visual aspects are more conspicuous. The National Covid Memorial Wall’s essential representational mapping, where one heart symbolises one life lost, is salient and easily understood. One might assume that this visible encoding is the only way the mural conveys its striking statistic. The invisible passage of time, on the other hand, is not perceived immediately, but experienced cumulatively. This makes its powerful contribution harder to recognise.

More generally, we’re often guilty of jumping to incorrect conclusions about how people experience visualisations. Robert Kosara’s empirical research illustrates this issue by shattering preconceptions about pie charts. Whilst many assume that people gauge the angle of each pie slice, they actually tend to gauge the area of each pie slice. The professed channels for representing numbers aren’t necessarily the true modes of communication. This results in misunderstandings about how visualisations work, and what makes them effective. 

Conversely, open-mindedness about how people might experience data can generate fresh perspectives on data communication and reveal new approaches for conveying large numbers. The National Covid Memorial Wall illustrates how this can go beyond the visual dimension. A sense of time passing, heightened by physical exertion and exposure to emotional stimuli, makes the six-digit death toll seem less abstract, conveying its true scale. Even seemingly simple representations can communicate in unexpected ways.

The post Taking Time Is an Effective Way To Process Large Numbers appeared first on Nightingale.

This was figure skater Nancy Kerrigan’s reaction to being attacked by an unknown assailant after a practice session at the U.S. National Figure Skating Championships in 1994. Kerrigan’s ability to compete in the upcoming Winter Olympic Games was thrown into doubt and her main rival, Tonya Harding, was revealed to be part of a coordinated plot to keep Kerrigan from the medal podium.

The month-long scandal was nothing short of a pop culture feeding frenzy. The proverbial Band-Aid was ripped from the knee of a sport widely considered to be genteel—if it was considered a sport at all. This story had it all: a heady brew of class, gender, athletic, and image politics all balanced on a quarter-inch blade. 

Fourteen-year-old me was hooked for life. 

Kerrigan may well be the most famous person to utter the phrase “why me,” but I definitely said it out loud (okay, I whispered it) when colleagues invited me to teach part of a Fall 2022 workshop about data visualization and data crafting at our library in Salt Lake City. 

I had recently taken up sewing and managed to finish a few small, dodgy-looking quilts, but nothing that I would remotely consider data visualization. If anything, they seemed like cautionary tales in how not to use colors and shapes together. I recognized that quilting is a good medium for data visualization and crafting, as it offers many ways to present information: fabric color and pattern, choices of shape, repetition of patterns, and finally, the stitching that holds all the layers together. My head swam with visions of crunching large, meaningful datasets and distilling them into some kind of beautiful fabric design that would no doubt be a complete headache for a beginner to sew.

Still, I agreed to participate alongside colleagues who knitted, cross-stitched, sewed, and wove. Each of us would offer a case study of a different data crafting project. I put off thinking about the data I wanted to visualize for months. I toyed with ideas of temperature quilts, showing water levels in the Great Salt Lake, or other climate or health data (COVID-19 is still with us, after all!). But each time I backed away with feelings of imposter syndrome. 

Then it dawned on me, sitting here in a city that hosted yet another Olympic skating scandal: I should craft something about figure skating. When I considered it more closely, I realized World Championship medalists could reveal more about trends in the sport. Unlike the Olympics’ four-year cycle, World Championships are held annually, and its medals are considered more prestigious. They’re also often a better predictor of who will medal at the Olympics. Consider the 1993 Worlds podium: gold went to Oksana Baiul, silver to Surya Bonaly, and bronze to Lu Chen. This mirrors the podium at the 1994 Winter Olympic Games, where Baiul won gold again, Kerrigan got silver, and Chen bronze—a 66% predictor! At the 2021 Worlds, we see the same phenomenon: Anna Shcherbakova won gold, Elizaveta Tuktamysheva silver, and Alexandra Trusova bronze, all Russian women. After their teammate Kamila Valieva faltered at the 2022 Olympics, Shcherbakova and Trusova won gold and silver. 

I began looking at SkatingScores, a fan-run database project to “collect and re-publish all of the scoring data from major international competitions made public by the ISU [International Skating Union]”. As a metadata librarian, I also started thinking about data quality hallmarks like consistency, quality, and coverage, and recognized this site had what I needed. 

Figure skaters compete in four disciplines: men’s singles, women’s singles, pairs, and ice dance. Medals are awarded to the top three skaters  (gold, silver, bronze), who represent their home countries. While SkatingScores offers results going back to 2005, I decided to limit my scope to the past decade, 2013-2022. I used the data available to build a table in Google Sheets showing the countries on the podium in each discipline for this period:

I built a list of unique countries in the table and discovered there are only 12. But the colors in each country’s flag surfaced a challenge: many of them use red, white, and yellow! Adding a dimension of other features such as stars, stripes, and other shapes gave me more options for fabric choices. I ended up with this key:

What was I trying to show with these data, and how to best represent them? Was it the total medal count by country? Total medal count by discipline by country? Was a pie chart showing percentage of medals by country adequate, or would a bar chart be more revealing? For example, some countries are very strong in certain disciplines but not others (Germany, Spain), while some countries have a large number of overall wins (Russia, USA), and others only a single medal (South Korea, Belgium). How could I show 2020’s canceled competition due to COVID-19, when no one won anything?

Another factor was my limited sewing abilities: the idea of making some kind of chart that required sewing on a curve was breaking my brain. 

In the end, I decided on a tabular design made up of individual blocks (the smallest design unit in quilting) each representing one discipline per year. It would be four blocks wide, one for each discipline, and 10 rows tall, one for each year.

This design lent itself well to a temporal aspect that would’ve been lost with other chart designs, and also highlighted the differences between disciplines. I sketched it out using colored pencils and thought, “Boy, this is going to be ugly.” But soon enough I was off to the races (Etsy) to find specialty fabrics.

I settled on blocks measuring 8” wide by 4.5” tall, with half the width given to gold, and the remaining half split between silver and bronze (a ratio of 4:2:2). This would make the finished quilt 32” x 45” (81 x 114 cm)—roughly big enough to pull over my face in anguish while watching skating the following season. 

Fabrics poured in from shops near and far and, by August, I was busy slicing them into pieces for assembly. (Since the average summer temperature in Salt Lake City hovers around 90°F (32°C), I didn’t mind working in the cool  indoors with my sewing machine.) The process was not, shall we say, smooth sewing. With 120 small pieces of fabric to keep straight, I definitely sewed a few in the wrong order and had to reassemble with a closer eye on my key. 

I also switched sewing techniques part way through, which inadvertently misaligned a column and forced me to re-sew some blocks. There’s definitely a lesson here for data work: don’t change methodologies midway! 

Once the quilt was assembled at full size, the design revealed more than I expected.

Here’s some data observations you might make:

1. One team from France (solid blue), has dominated ice dance for the past eight years with five gold medals and a silver.
2. Japan (red dots on white) has been very successful in singles (men’s/women’s) but is barely represented in pairs/dance (the reverse is true of Canada for this period). 
3. Germany was well represented in the first six years by two pairs teams. (Notably, the teams shared a woman skater). 
4. Russia won five women’s singles gold medals in the past 10 years (four were under one coach, Eteri Tutberidze), and several silvers and bronzes.
5. Russia swept 75 percent of World Championship medals in 2021, the year ahead of the Olympics in 2022, where they won six of 15 medals in skating.

This visualization revealed a few shortcomings. First, some of the countries (Belgium, Kazakhstan, for example) are so infrequent it would likely be impossible to decipher who they were without a key. Second, with a small number of colors repeated in so many flags (hello red, white and blue), the color key might not be sustainable if rows were added. For example, Great Britain has an ice dance team, Lilah Fear & Lewis Gibson, on the rise in the 2022-23 season, but solid red, white, and blue have already been taken by Russia, South Korea, and France, respectively. If I were to extend the design, I might have to resort to kitschy flag fabric after all. Aside from the iconic Canadian maple leaf, I took pains to steer clear of busy patterned fabrics. Finally, the fabric I chose for China (red background with gold glitter stars) is very hard to distinguish at a distance from Russia’s solid red.

The painstaking process of assembling this quilt also gave me ample time to reflect on a sport that I’ve followed for almost 30 years now. My interest has shifted over time, from the women in the 1990s to a fallow period in the early 2000s, to men’s and ice dance in the early 2010s through today. (Sorry pairs, I’ve never liked you!) Shallowly, I admit this corresponds somewhat with American skaters’ successes. But nothing defines sports like a good rivalry and ice dance has delivered that in spades over the last decade. Rival teams training side-by-side under expatriate coaches, teams coming out of retirement to prove naysayers wrong, close competitors claiming to be best friends off-ice, top teams breaking up and reforming to suit romantic relationships? Sign me up.

And what of data crafting? 

I learned that with the right topic, work can feel effortless. It turns out I’m a better crafter than I gave myself credit for, and I have since moved on to personal projects where I feel confident mixing designs that speak to me. I’ve considered making another skating quilt that visualizes this data in a different, more aesthetically pleasing way. Given numerous criticisms of the sport’s training techniques and younger and younger competitors, another data set to visualize might be the average age of competitors at certain intervals, say Olympic Games since 1994. 

No matter what I choose to work on next, the most important lesson is: just start and you will find your way through.

The post Why Me? Or, How I Learned to Sew a Quilt from Ice Skating Stats appeared first on Nightingale.

The survey questions included, “What would our students want from their courses? How can we help them perform their best? How can we keep them engaged and promote a sense of community?” While the university’s learning and education committee learned through experience which teaching and evaluation styles work for students both individually and collectively, we wanted to know the students’ thoughts and what they want. In particular, we asked what would be the best way(s) to assess the students?

Some lecturers in UK universities would argue that students usually prefer evaluation styles that they are familiar with. In our case, we thought that the pandemic-era students may want to be assessed differently. For example, when I was doing my A level equivalent in Greece, we had in-person exams, but these pandemic-era students were assessed using essentially multiple choice questions or online oral examination. All my current students, for instance, have no experience with written, pen and paper, in-person exams. When we asked them to attend such an exam, they were visibly distressed. Therefore, we felt we did not precisely know how these students would like to be assessed. 

“How would you like to be assessed?”

As the expert in data visualisation and data analytics among my fellow lecturers, I took on the task to ask our committee’s questions not only to the students but also to the lecturers. I started by looking into the methods the lecturers are currently using to assess students. I used already available data from the course descriptions and when not sure, I directly asked my colleagues who were teaching those courses.

I chose data physicalization to engage my study participants in the questionnaire about how they would like to be assessed. The exercise was inspired by the work of Matteo Moretti on providing information about cancer to a large and diverse audience. But instead of merely informing my students, I wanted to collect data about their opinions. I wanted to show the students that we care and that we take them seriously, and that we want to act on their opinions. With data physicalization, you act before you analyse as by touching the data you are acting upon them. 

“With data physicalization you act before you analyse as by touching the data you are acting upon them.”

Students at the University of Portsmouth are very diverse. One will find people from different  educational backgrounds, beliefs and literacy levels. So, instead of creating yet another boring survey, I invited them to actually touch and play with the data. This would also cater to anyone who is unable to take an online survey and it would boost engagement via play or gamification. After all, we don’t want to merely educate our students, but also to inspire them to think outside the box.

Physicalization to engage students in the survey

The exercise included two physical elements, three types of colour coded threads (blue for male, pink for female and grey for non-binary or do not want to be identified), and three types of colour coded stickers (pink for female, blue for male and orange for anyone who is non-binary or did not want to identify). The threading started from a separate area for staff and a separate area for students and each participant had to thread their preferences, until they reached the end point. 

One could participate multiple times if they wanted to provide more than one threaded answer, for example if their answers changed based on the age or in other words educational level of students they were answering for. The threading had the following questions:

• Who are you? (Staff, Student)
• What year group are you referring to? (1, 2, 3, 4, all)
• How many assessments would you like to see per module? (1, 2, 3, more than 3)
• What type of assessment would you prefer? (exam, coursework, both)

The stickers had the following questions: 

• What type of exam would you prefer?  (There were three options to choose multiples: pen and paper exam, online written exam, and online multiple choice test.)
• What type of coursework would you prefer? (There were 10 options to choose, and participants could choose multiple answers: lab report, written portfolio, written portfolio and presentation, lab report and presentation, oral presentation, offline tests, written report, written report and presentation, poster, and video)
• Would you like to create an artifact as part of your coursework? (Yes, No, I don’t know)
• Would you like to collaborate with the industry as part of your coursework? (Yes, No, I don’t know)

As the study designer, the most interesting thing for me was creating the material and imagining how the final visualisation would look like. I was able to see the trends developing in real time, along with the encouraging and supportive feedback from my colleagues. The students also liked the exercise and participated happily. The exercise ran during a research showcase of the final year undergraduate students, and we had 56 participants in total. 

The key points from the feedback we collected were:

• Very original data collection method. Many students appreciated the feeling of being invited to express themselves. Especially the new ones, who were visibly still adjusting to new in-person learning experience, expressed that they enjoyed stepping up to the board and physicalizing their own data
• The data analysis and collection took place immediately at the same time, we could see what people wanted even before analysing the data on the computer
• Engagement was huge for the scale of the event
• Low literacy audiences could hugely benefit from touching the data, instead of answering questions on a complicated survey

Throughout this data physicalization, I learned that passion is a great drive for any experiment or exercise to work. Students felt heard and staff had fun “playing at work.” 

The initial impact of this exercise was the realisation that most of our students don’t like poster exams, in which a student will make an oral presentation, presenting their poster. This is surprising to us since poster sessions have usually appeared to be the least stressful, interactive, and even ‘playful’ examination practices. 

There are also more students who liked exams than we thought. However, the exams that they like are computer-based ones rather than with traditional pen and paper! 

Many staff members were not surprised by the results, but for some, this exercise was eye opening. Its in-person nature especially promoted staff collaboration, and discussions about how to improve their assessment methods. 

Will we run this again? Sure thing! Is it data visualisation? Well yes, as we visualised data a different way—by physicalizing them—and we learned something new by doing it!

The post Touching Data: Conducting a Survey With Paper and Thread appeared first on Nightingale.

Physical representations of data connect with the human aspects of each data point, make data concrete and tangible, encourage interaction and reflection, and create more accessible and engaging experiences. Representations of data in physical form have been around for tens of thousands of years. Now, increased access to data and technological tools to create physical representations are providing people with more options and inspiration for creating physical data objects.

Making with Data “offers a snapshot of contemporary data-driven making—examining the stories behind the projects and unpacking the stories, tools, and decisions behind the design” (p. 22). The book is split into five sections based on the type of physicality, and each section provides a series of examples with the creators themselves explaining the project motivation and inspiration, their practices and processes, the materials and tools used, and a reflection. 

I particularly enjoyed the materials and tools section of each piece. It was neat to see the variety of tools—including clay, Excel, computer-aided design software, a pipe bender, laser cutters, and more—used across projects and to gain insight into exactly how each piece was created. The practices and processes section was also really interesting to read through to see how each piece developed.

The fives types of data physicality

The first section of the book explores projects in Handcraft, where objects created by hand are encoded with data. My favorite in this section was “Snow Water Equivalent” by Adrien Segal, a cabinet that is also a data sculpture of the snowpack at Ebbetts Pass from 1980 to 2010. I particularly liked this quote from Adrien’s reflection that shows the power of physical data representations: “Artifacts can be a creative expression that captures a particular time and place in the physical realm. They have the potential to raise awareness, bring about dialogue, tell a story, or change perceptions—they can be vessels embedded with layers of information, from which knowledge can be derived.” (p. 49)

The second section examines Participation, where people participate by providing data or being part of the creation process. My favorite in this section was “Let’s Play with Data” by Jose Duarte, an interactive data visualization kit, which includes stencils, shapes of different colors, tape, markers, and a board along with instructions. In the practices and processes explanation, Jose states, “It’s not just about visualizing information but making information visible” (p. 143). The participatory process of the projects in this section makes the information more visible.

The third section covers Digital Production, where digital fabrication is used to create physical representations of data. My favorite in this section was “Wage Islands” by Ekene Ijeoma, a topographic map of New York City based on housing costs that can be moved in and out of water based on wages. In his reflection, Ekene states “I wanted to bridge the gap between facts and feelings in a way that felt familiar and intuitive” (p. 209). This is one of the powers of making with data—encoding information in objects that are familiar and intuitive.

The fourth section provides examples of Actuation, where physical objects change based on changes in data. My favorite in this section was “Loop” by Kim Sauvé and Steven Houben, a moving series of rings that show daily steps for seven days in comparison to a goal. Based on interviews and user testing, they learned that the visualization “had to blend into the environment and be aesthetically pleasing” because it would be part of the home decor and that “it would be beneficial to create an abstract representation that could be ‘read’ by the owner, but provides privacy when observed by others” (p. 270). Each piece in the book included some sort of iteration on the design, and many included a user feedback component. At the end of the book, the authors reflect on the iterative process involved in making physical objects with data, and note that while we see the finished products and a more linear process, experimentation and messiness are key parts of the development. 

The fifth and final section explores Environment, where pieces show information about the environment, are part of the environment itself, or use the environment as a setting. My favorite in this section was “Perpetual Plastic” by Liina Klauss, Moritz Stefaner, and Skye Morét, a data sculpture made of plastic retrieved from beaches that shows the fate of plastic created between 1850 and 2015. In the reflection, the creators state, “We deliberately chose to create a “data visceralization” from physical, known objects—not only because they were colorful and relatable, but also because they allowed us to encode data variables into a tangible, situated form, at human scale” (p. 330). Physical representations of data allow for a larger scale and also a more tangible creation than digital ones.

An area for improvement

The only critique I have of the book is that there could have been more diversity in the creators featured. Nearly 90% of the creators are based in North America or Europe and almost three-quarters are male. I also noticed that most of the creators in the book are white. 

I reached out to the authors about this critique, and they shared the following (plus a way to get involved and share your own physical representations of data!):

Thank you so much to Till, Lora, Sam, and Wes for sharing this response, and be sure to check out the chapter template to share your own work making with data.

Final thoughts

I absolutely loved Making with Data. It’s a beautiful book that was very enjoyable and inspiring to read, and I’m sure I’ll refer back to it for ideas. The variety and creativity of the pieces presented, along with the depth of explanation shared by each of the creators was truly fascinating. And I loved that the book ends with a message of encouragement to the reader to go out and experiment with data, share the process, and embrace the messiness. After reading this book, I certainly want to go make things with data! I highly recommend getting a copy of Making with Data and checking out the accompanying website or on Amazon.

Correction: An earlier version of this article misnamed the project in the fifth section of the book. It is “Perpetual Plastic,” not “Perceptual Plastic.”

Disclaimer: Some of the links in this post are Amazon Affiliate links. This means that if you click on the link and make a purchase, we may receive a small commission at no extra cost to you. Thank you for your support!

The post Review: Making With Data, a Fascinating Dive Into The World of Physical Data Design appeared first on Nightingale.

It all started with a Thinkering Grant. 

Thinkering is a word composed of the verbs thinking and tinkering which together convey a sense of playful experimentation. Each year the Luxembourg Centre for Contemporary and Digital History (C²DH) of the University of Luxembourg funds a handful of small-scale research projects that have a relatively high chance of ‘failure’ but foster ‘creative uncertainty’ and encourage researchers in different disciplines to collaborate. 

We—Daniel Richter, Aida Horaniet Ibañez, and Joëlla van Donkersgoed—are all researchers in the C²DH and wanted to try our hand at thinkering. We were inspired by the historical census data that Daniel had collected about Brill Street, a street in the former industrial city of Esch-sur-Alzette, Luxembourg. Daniel brought the data, Aida brought her expertise in data visualization, while Joëlla brought experience engaging city communities with their history. 

Together we ventured into a project that “physicalized” the census data of more than 100 households (almost 500 people) living on Brill Street in 1922, turning population records into a tactile, 3-dimensional, and interactive exhibit. In the end, the project gave us a glimpse into the individual houses and families who used to live there a century ago.

We hypothesized that experiencing the census data in a physical form would allow the current residents to engage with historical data about their neighborhood in a new, creative, and playful way. We wanted to design a data physicalization without statistical graphs that people of all ages could understand and recreate with simple materials such as paper, scissors, and glue—no computers, no QR codes, and no internet access required. And, most importantly, we wanted to develop a physicalization that would spark curiosity and invite them to explore the data, formulate questions, and discover a new part of the history of their neighborhood. 

On top of sparking the residents’ curiosity, the physicalization would, we hoped, lead to conversations that would help us (the historians and data visualization researchers) to discover details not shown in the raw data but known to the people living in the same neighborhood today. We hoped that by comparing residents’ knowledge of the past with the actual census data we could learn about who was included in the census (or not), which jobs were declared (or not), and where the blind spots were. Thus, the idea for “Brill Street 100 Years Ago” was born as an experimental interdisciplinary project that would foster public dialogue about history.

The data

The original data is in microfilms in the National Archive of Luxembourg (see figure 1) and contains personal information about the individual inhabitants and their living arrangements.

To understand how it was collected and interpreted at the time, Daniel prepared a set of documents to contextualize the data. He collected the blank census forms (figure 2), photographs of the street (figure 3), and a copy of the original instructions given to the households for filling out the form (figure 4). These provided insight into how the data was collected and standardized. For example: What were the criteria for how the number of rooms was counted? What kind of activities counted as an occupation, and which didn’t? Who could be counted as a member of a household, and who couldn’t?

After the collection and transcription of the data, we discussed which categories of the available data we wanted to physicalize in our design.

We intially focused on:

• number of rooms,
• number of inhabitants,
• nationalities, and
• labor categories. 

The labor categories represented a challenge since categories change throughout history – especially in regards to what had been classified as a “professional” or “technical” position in 1922. It felt important that we avoid suggesting any classes or judgements based on the labels for jobs or living arrangements because these do not translate well across time. For example, “miner” in 1922 would have been a well-respected and decently paid choice of profession, especially in an industrial town like Esch-sur-Alzette. The job lost its associated prestige when machines made the work easier in the 1930s. Therefore, we decided to show the exact profession recorded in the census and not undertake any categorization ourselves.

The interpretation of labor categories was not the only challenge in designing a physicalization with historical data. One of our initial ideas included creating a second physicalization that represented the households of Brill Street in 2022 to enable viewers to compare the data across time. Therefore, we wanted to ensure that our visual vocabulary would work for both 1922 and 2022 data. In the process, we were confronted with many definitional changes in the last 100 years – for example, back then, there were no formal concepts of dual nationalities or a gender spectrum, and since then the census has abandoned the idea of a “head of household” and the age at which one enters adulthood has changed. So, our visual vocabulary had to work for both 1922 and 2022 definitions of those variables. 

For these and other reasons, there was some information available that we decided not to include in the physicalization. This included house number, marital status, relation to the “head of the household,” active time in a profession, rental price, the existence of a living room, as well as specific information on visitors (e.g., period, usual residence) and on absentees (e.g., reasons for absence, duration).

Lastly, we wanted a way to encode two levels of data aggregation: 

• the household, and
• the people living in it.

The separation would allow us to include additional information about the place as a whole, such as the floors on which they lived inside the building, if there was a commercial space, and if they were the owners. Then, at an individual level, we could also encode variables like the gender of the inhabitants, and if they were minors or adults. 

The design and construction

With all these criteria in mind, we organized a student workshop to conceptualize potential designs. The aim of the workshop was to decide on a design and materials that would provide us with a balance between content, practicality, and visual appeal. Besides the analytical possibilities, important factors were the ease of handling the material by children and adults, cost, and the portability of the physicalization. 

After briefly sharing some examples of data physicalization, we started brainstorming ideas that inspired different prototypes using materials such as felt, wood, beads, buttons, cardboard, or pegboards (see Figure 5).

The selected canvases were 3mm thick transparent plexiglass plates with two holes at the top for hanging so they would be visible on both sides. We used colored cardboard, scissors, and glue to encode the data. 

The material’s transparency allowed us to paste on one side a square of a size equivalent to the number of rooms, and on the other side, i.e., “inside each household,” a triangle for each person with personal information. This created a striking visual effect of “crowded” versus “empty” households. The readers could walk around the plates to explore the information on each side. 

The size of the squares was defined by calculating the minimum size of each triangle with all the personal information to be readable in the most occupied household. The result was two plates of 1m² to visualize all households. One side visualized the information about the household (occupation ratio, ownership, use for commercial activity, inhabited floors), and the other side visualized the information about each of the inhabitants of the household (nationality, if employed, profession, gender, adult or minor) (see figures 6 to 8). 

The use of data glyphs allowed us to see many variables in a single view, from which we could discuss different topics with the residents as they noticed new things. It also allowed us to zoom in and out from general topics (e.g., the predominance of nationalities, ownership) to specific details (e.g., professions), and then intuitively look for relationships (e.g., professions in households with lower occupancy ratios, property ownership and inhabited floors). 

We designed the visual vocabulary in such a way that it would trigger questions about nationality, professional occupation, gender, and other issues related to social affluence, and hoped that people’s guesses and solutions would give rise to surprise and intrigue them to learn more about those who lived here 100 years ago.

The final design and construction of the physicalization were finished before the public community workshops. Initially, we had wanted to invite families to reproduce the visualization for their current households – this is one reason why we had also selected easy-to-use materials. But when planning the public events, we realized this was beyond our logistical limitations. However, this exercise could be part of a future event in an educational setting or similar, where we could ensure adequate space and time for the activity. 

The construction of the physicalization was a team effort with highs (e.g., discovering the visual impact of the encoding) and lows (e.g., realizing that there were mistakes that needed to be reworked). We are enormously grateful to the students who participated in this challenging exercise.

The public workshops

To test the physicalization, we organized two workshops with residents. One was in a dedicated cooperative community space, and the second one at a bakery where people could spontaneously join the discussion (see Figure 9), both in the area near Brill Street. 

During the workshops, we learned several interesting things. 

Nationality

For one, we realized that the migration history of Luxembourg was blurred in the participants’ memories. Their perceptions of the predominant nationalities in various migration waves did not match the census data for Brill Street. The blue and purple triangles predominate in the visualization, which led to most people suspecting that at least one of the two colors must represent Portuguese nationals, who in 2022 made up a third of the population of Esch-sur-Alzette. However, at the beginning of the 20th century, there were no Portuguese people on Brill Street at all. Instead, at that time, it was known to the locals as the center of “The Italian Quarter.”

Around the turn of the century, a new boom of the local iron mills and mines attracted a large number of workers and their families from all over Luxembourg (purple in the physicalization) and the bordering regions in Germany, France, and Belgium, but also from Italy (blue in the physicalization) and Austria. While the Luxembourgish and German nationals made up the largest portion of the workforce, Italian miners and construction workers were also needed for the quickly expanding town. But in contrast to other nationalities, Italians favored living in a small selection of streets away from the city center. While nine out of ten Italians arriving in Esch by 1900 were found in Brill Street or one of its adjacent streets, only half of the street’s population consisted of Italians, sharing the street with other newcomers to the city from elsewhere in Luxembourg, Germany, Belgium, and France.

Another topic of discussion was the mix of nationalities in the households. It was very interesting to see how some of the participants projected some generalized opinions, misinterpreting the color coding defined for nationalities, and only realized that they were misconceptions when we mentioned it. For example, when there was a mix of nationalities in the same household, it was more likely to be in a house with Luxembourgers, contrary to the projected belief.

Occupancy

We talked with residents about the temporary workers who traveled alone for short periods, and who were not always present in the census, because they would have left the city before December when the census was conducted. Some residents also mentioned that we should not only talk about a room occupancy ratio but a bed occupancy ratio. They recalled that beds were rented out to multiple people; one would sleep in the bed and work the night shift, and the other would work the day shift and sleep in the bed at night. This practice was kept alive for several decades but became visible in the census data only through vastly overpopulated apartments. 

Patterns of Rental and Ownership 

One neighbor mentioned that their family had been homeowners in the past but had then sold the properties to buy another house in their home country to return to in old age or for their grandchildren to inherit. Yet, even at a very old age, they had remained in the street or adjacent streets of the Brill quarter, renting for generations. This is a behavior quite common among immigrant families who want to keep the connection to their homeland and their relatives and see their stay in Luxembourg only as temporary, even in cases where they had spent most of their lives in Esch-sur-Alzette. 

Another interesting discussion arose when neighbors were talking about the exchange of rental housing within the same building. The houses had two or three floors plus the attic, and some neighbors mentioned that when families grew bigger or children got older, sometimes neighbors exchanged apartments within the same house. We do not know if these changes were recorded in the census.

These and other conversations with the residents of Brill Street and the surrounding streets around the physicalization allowed us to give them back a bit of their neighborhood’s history and more deeply connect the past and the present. 

Lessons learned

If you want to use data physicalization to make a dataset more accessible to your audience, this is what we have learned in the process:

• If you work with historical data, understand the historical context and make it explicit. This is one of the strengths of interdisciplinary work – do not assume that the interpretation of variables and categories is the same over time. Collaborators can help us see the context. 

• There was a leap for us between visualization and physicalization, which included experimenting with structures, materials, and construction. It was an exciting process (especially for those of us who spend the day designing visualizations on the computer), but do not underestimate the complexity of physically building something. Not all materials are easy to handle. For example, we had to learn how to cut and drill Plexiglas, which turned out to be more difficult than we initially anticipated. 

• As with any data visualization where you define a custom visual vocabulary, do not forget the “how to read” section. Luxembourg is a plurilingual country where it is a challenge to organize an event in one single language. Here, our physicalization has an advantage: we could print the instructions about how to read it in English, French, Luxembourgish, and German. The only text in the visualization itself was the professions, which we decided to leave as-is to respect the language of the census form (and translate it upon request during the events).

• If your design is attractive, everyone will want to know what it is—not only during the events, but in between. We stored the physicalization in our office, and in the end, we decided to hang a copy of the “how to read” on the wall, because everyone passing by was curious about what it was. Use that interest to deepen the conversation.

• Take the physicalization to everyday places, where people stay for a while (e.g., markets, stores, waiting rooms), and informally start the dialogue. The physicalization can be used as a tool to initiate an informal conversation about historical data, as it encourages curiosity about visual imagery, rather than to confront people with abstract data. Organized workshops might work best in educational, research, or professional settings.

• When you build something physically, you must plan in detail, because there is no “refresh” key. If you miscalculate the number of repetitions in a category, you run out of materials; if you do not see an outlier in time, you have to recalculate all the space usage. Correcting errors becomes an art, especially when the physicalization is at a very advanced stage. Plan “the construction,” build the small components first if possible, and finally put them all together. That will leave more room for surprises and corrections. 

• During the construction of the physicalization, enjoy the process of discussing the hows and whys, and spend the necessary time going back and forth to the data sources, to better understand the raw data. We found these discussions the most exciting and rewarding part of being on the construction team! By the time you finish the physicalization, you will be true experts in the dataset.

• If you can, experiment with different audiences – even informally! One day, while working on the construction of the physicalization, Aida had a very interesting conversation about the visualization with her 5-year-old daughter, who only needed a couple of minutes to understand the content and start asking questions. This made us think that children could not only participate in building the physicalizations, but also by getting involved in the analysis, asking questions, and participating in the discussion. 

Above all, we encourage you to embrace creative uncertainty – thinkering – in bringing data to life. Engage in discussions with the other experts and with the public, and together you too can explore new insights about history through the physicalization of data.

The post From The Historical Archive To The Citizens: Visualizing Census Data From Brill Street in 1922 appeared first on Nightingale.

That day, I spent ten hours running a piece of cloth through my typewriter. I selected a few lines from the Book of Common Prayer to overtype 549 times, in all caps: “FROM PESTILENCE FAMINE BATTLE MURDER AND FROM SUDDEN DEATH GOOD LORD FROM LIGHTNING AND TEMPEST, FROM PLAGUE DELIVER US.” I’d recently learned that the Book of Common Prayer was compiled in the 16th century during the Black Death. That context may explain why the book’s many pleas for deliverance from plague still radiate urgency. 

Eventually, I began to document daily case counts in my region of Iowa on a larger textile. I recorded cases every day, each with a stamped prayer, “FROM PLAGUE DELIVER US.” Prayers tend to be said on behalf of those closest to us, and a county was the smallest geographic area for which case counts were available. Working with data from Johnson County, IA, I recorded just the new cases each day, excluding recovery data or deaths. My intention for the final product was to situate both the act of prayer and the practice of art making as nuanced methods for approaching uncertain situations. 

What happens when we can both see and touch data? I worked on DEMAND/PRAYER for about 20 hours a week over 18 weeks. I anticipated the project would be a sad one; I didn’t expect it to make me shake with anger while I sewed. It’s one thing to model cases digitally, and another to perform a small physical act of labor that acknowledges each person in a dataset. My anger was fueled by numbers from a relatively small area, compared to the pandemic’s global reach. I wondered if (and how) public policy outcomes might change if legislators, and the public, had a more embodied relationship to case data. As the writer Jenn Shapland claims, “I feel that there is a price we pay for disembodiment.” What might happen if those shaping public policy could touch and see the lives being impacted by this pandemic? 

There are important elements of the pandemic in Johnson County that this textile does not capture, such as which demographic groups bore disproportionately high caseloads. Additionally, the piece represents all new cases in the county over the course of a year with a reference to a Christian text, the Book of Common Prayer. But, perhaps only a third of Johnson County’s residents are religious, and the local religious groups include Jews, Muslims, Buddhists, Hindus, and Christians. Given these demographics, my work is an example of how data art differs from data visualization. As an artist, my work recycles canonical Christian texts to assert that mystical and spiritual experiences happen within, and sometimes despite, established social structures.

Cloth is a practical medium for my data physicalization projects: even durable paper can become worn out from repetitive stamping or typing. A second reason I use cloth is “computing’s historical dependency on textile design, its production methods, and its labor models.” Binary code was invented during the industrial revolution to automate weaving patterned cloth. Core memory, the first viable and widely adopted re-writable computer memory, was a conductive fabric which coded ones and zeros in magnetic beads as positive or negative charges. Core memory was woven by hand, and its fabrication resembled a combination of bead looming and pin loom weaving. A touchscreen technology often found in consumer electronics like tablets and smartphones relies on conductive grids that operate similarly to core memory planes. The anthropologist Stephen Monteiro draws parallels between needlework, touchscreen gaming, and image-based social media interfaces, arguing that “personal touchscreen device use resembles the actions, strategies, and conditions of craft production.”

Some data physicalizations are self-conscious of these connections, demonstrating integration between medium and message. Visualizing blockchain technology with yarn, knitting a blanket of sleep pattern data, and encoding the pace of global warming into winter wear—in these projects, yarn is a considered material choice. However, some textile data physicalizations leave me wondering why textiles were chosen as a medium for that data. To engage a certain demographic as makers or viewers of the data object? Because knitting, crochet, and embroidery are financially accessible techniques for making data physical? There’s nothing wrong with either motivation. However, many makers working at the intersection of textiles and data are still unaware of the degree to which information society depends on textile technology. When we choose textiles as a medium, we have an opportunity to highlight that connection.

Artistic data physicalizations are laborious, and they’re not immediate enough for many of the ways we use data. Still, I am drawn to the ways textiles can make associative leaps and humanize data: as if by giving data a body, we glimpse its soul. 

Learn more about gendered labor, textiles, and the 1969 moon landing:

1. Skilled seamstresses sewed Neil Armstrong’s space suit by hand, using techniques adapted from lingerie construction.
2. A workforce of unnamed women, some of whom were retired or laid-off textile workers, hand wove the space shuttle’s navigation program.
3. A workforce comprised mostly of Diné (Navajo) women fabricated the microchips for Apollo 11.

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