What are designers actually saying about issues in design? 

Spring 2020
6 weeks

Data Visualization
Python (NLTK)
Interaction Design
Print Design

Alice Fang
Rachel Lee
Jaclyn Saik

Examining the roles that gender, power, education, etc. play within the design industry.

The AIGA Design Census is a comprehensive overview of the design industry, surveying quantitative data about salary, job satisfaction, future outlook, etc. in its census. Hidden in the raw dataset were the responses to the question “What critical issues face design?,” where respondents could write-in their own answers.

The 2700+ written responses were super interesting and expressive, and felt personal in a way that the discrete data did not. We wanted to use this corpus to tell a story, and to humanize the data set—to see reponses as people and not data points.

My roles on the project included parsing the text with python, analyzing responses, prototyping interactions, and laying out the booklet. We worked collaboratively in designing the final web and print interactions.

This project exists in two forms: an exploratory website, and an interactive book

01. The Site


Creating a site to make users engage with all of the content of the dataset

The site was designed to be visually busy, with multiple layers of interaction with the data. We wanted users to ‘physically’ engage with the content, by being able to receive visual feedback from the words themselves.

A digital interface allows for quick transitions between content, with hyperlinks and visual feedback. This movement through layers of meta information and cyclical clicking  inspired our visual design and interactions.

I prototyped different iterations of content layout and web interactions, and organized the information architecture of the site, so that the team would remain on the same page as Rachel moved forward with web development. Jaclyn and I finalized the look and visual system of the site.
02. Site Architecture

03. Visual System


Using different web interactions to highlight—sometimes literally highlight—different kinds of information

04. Theme Highlights

Each quote has highlights that relate back to the broader category. Hovering over the outline of a highlight connects the quote to a different category, representing intersections in response topics. This prompts users to either follow a theme through one category, or jump to a different topic entirely. 

05. Quote Preview
The column on the left side provides previews of every quote within a category, and gives the user a way to mark their location and jump around, browsing based on interest.

06. Spot Words
Keywords that, regardless of category, appear throughout the entire corpus. Upon hover, they show stats about the frequency of use/mention.

07. External Articles
Curated articles from AIGA Eye on Design, linking concerns and critiques to real-life commentary from the community

08. The Book

✨ Translating the digital experience into print

With the book, we were inspired by the different narrative paths a reader can take in choose-your-own adventure books. We turned to sticky notes as a method of layering an additional level of information that purposefully obstructs the content, and acts like a physical pop/up or hover-state.

I was in charge of laying out the book, and analyzed responses to pull out the individual keywords and associated quote, from which Jaclyn could find the content to place on each sticky note.

Featuring me as a hand model
09. The (Guest) Book

We wanted to collect our readers’ reactions and thoughts on the question as well!


Reading, parsing, and analyzing many, many quotes

I used python and NLTK to parse the corpus, collecting the most frequent 150 words (excluding function words like “I,” “the,” “only,” etc.) and phrases. Looking at the generated lists, we picked out words and phrases that described themes or ideas, which we grouped into six broader categories: power, representation, education, quality, change, and community. These groups became the dictionaries that I used to parse through the quotes again, to sort which responses fell into which category.

10. The Code (and the Dictionaries)

This site doesn’t use every single response—it’s a prototype of a bigger collection. We selected ~20 quotes per category to analyze further, and picked phrases that described the main theme of each response. If quotes intersected with other categories, we made sure to highlight those phrases as well. (And we had a few super sexy color-coded spreadsheets.) Jaclyn used NLTK to find statistics for the different spot words on the site.

11. Sorting Categories

Using NLTK was a great way to initially sort and parse the corpus, but when dealing with this kind of data, there will still be a lot of manual labor (and love) going into the project. The three of us read lots of responses, annotated, highlighted, and cross-reference ~90 of them for the site, and curated a collection of key words, themes, and external sources.  

Reading the responses that working designers in the field wrote gave us a different perspective on the realities of the industry that, as students, we haven’t been as exposed to yet. It was disheartening at times to see the same consistent themes show up about diversity, or social media, but we’re hopeful that things are moving in a more inclusive, critical direction.