Week 10: Critical Data Viz

This week we are continuing our exploration of data visualization by discussing various strategies to effectively visualize data. We will also touch upon the ethical responsibilities that come with working with data.

WEB Dubois, Charting Black Lives

(W.E.B. Dubois, Charting Black Lives)

Quick Recap

We can use loadJSON() to fetch data from external sources like APIs or Google Sheets.
for loops and arrays to efficiently handle and visualize data.
Use map() to translate data values (input) into visual properties (output).
Use array.map(item => item.property) to find specific properties of each item in the array, such as min and max values.
Use array.push() to dynamically add new objects to an array.
Use Object-Oriented Programming (OOP) such as class and constructor to create reusable code structures.

Assignment

Due: Oct 21, 2025

Submit Assignment

This week, you will continue working on your data sculpture project. Think about how your data can be turned into both a digital and physical object. Consider using Object-Oriented Programming to structure your code and make it more modular.

What kind of physical form will best represent your data? How can you use materials and techniques to bring your data sculpture to life?
What is your data about? How did you clean, filter, or otherwise edit your dataset? How did each step affect the patterns or narratives that emerge?
How can they translate into an object using attributes like color, size, shape, position, scale, material?
What’s your way of conceptualizing the system? If using this method of inputs and output. What are the inputs and outputs of your system?
What are things you are including on your legends? How are you designing your legends? Other than printing as a separate paper?

This is the continuing process of our project 2: data sculpture. We will continue to refine and develop this project over the next few weeks. The full description is here.

Readings

Class Notebook

Bella Tsai

This week, I've had a deeper reflection on data visualization. The collection of data itself isn't objective; data can always be chaotic and complex. In my data, the 20th animal was "unknown birds," a line I could easily delete. Many of the animal names after that were also inconsistent. This highlights not only potential problems with the data collection system itself but also the challenges of data visualization methods and scope, and even how our understanding of the data itself is affected. Because data visualization is a process of simplification and transformation, the selection and omission of data during this transformation are human choices. The most challenging aspect is how to transform lines of numbers into interpretable, meaningful, objective information while simultaneously presenting a subjective filter through the data.

Flora Fang

During the office time coding together with the professor, I knew exactly what was going on with my code and how it works, which makes it easier for me to figure out when my code has a problem. But it was quite hard to create the Manhattan-shaped base. We spent a lot of time on this part, especially filling all the gaps after finding the outline cubes, but it finally worked. The logic was to find the first and last cubes in each column and then fill everything in between to make the whole shape complete. It was a new kind of coding logic for me, but I learned a lot from it.

Kate Chon

I learned how to organize my code in a more efficient and fun way.

Lin Kim

In the future, I plan to create actual 3D sculptures externally and integrate them into the web project. By producing physical versions of the antennas and crop circle-inspired forms, users could experience UFO data both digitally and tangibly, bridging the gap between virtual visualization and real-world interaction. This approach would enhance the immersive quality of the project, allowing audiences to engage with the data as art in a more tactile and spatial way.

Nancy Scanlon

I learned that even though things may not go by your original plan,, you can still create something that replicates what you want to do. As well as have fun

Sarena Yadav

Working with the Indian Languages dataset taught me how important focus and clarity are in data-driven design. By organizing 36 major languages and their dialects, I learned how patterns emerge when data is cleaned and structured. Using p5.js helped me visualize this complex linguistic information in a clear, interactive way, making it easier to translate into a physical mandala. Seeing the digital circles transform into laser-cut wooden shapes showed me how digital and physical systems can work together. Overall, I learned how visualization, repetition, and material choice can turn abstract data—like India’s many languages into a meaningful, tangible form.