AIGA DEsign census 2017
An interactive data visualization system for anyone willing to participate. The physical logo models for six colleges in CMU which were built in response to our own survey inspired by the AIGA 2017 census.
Data Visualization often provides disconnected experience to the users due to its complexity and heavy content.
As a group, we created a data visualization system that displayed our data in an interactive manner. AIGA census provided to us included over 13,000 responses.
And through manual machine learning, we analyzed trends and found relationships among data set. For this project, we focused on handling data as a subject beyond the purpose of delivering information and ideating a flexible approach to data visualization. The primary reasoning behind creating physical product was to introduce another level of interactive experience for the users by manipulating the orientation of AIGA logo based on collected data.
Spring 2019 - 5 week
Junior Communication Design Studio
Skills: User Research, UX, Visual
Design, Prototyping, User Testing
Group: Liam van Oort & Helen Reynolds
Raw Data Analysis and Insights
Part 1: Finding correlation between data
During the beginning phase of ideation, we worked collaboratively as a group. Our ideas and concepts transformed over time to settle a single idea. Throughout this project, we wanted to focus on creating a final product which provides humanized data visualization through building interactive system.
In the beginning, we decided to use raw data to explore about particular qualities of a designer. Through multi variable analysis of paired questions, we defined relation based on correlational percentages of how satisfied, stable, well-rounded, and educated a designer was corresponding to their responses.
Part 2: Survey Questions & Analysis
After conducting manual analytics, we wanted to take this a bit further and get the perspective of our peers on this project since we wanted this project to be open to students in all majors. We rephrased questions of the original data tailored to students in general at CMU and asked each school what their answer would be. Through their responses, we were able to analyze and determine percentages of how satisfied, stable, well-rounded, and educated; they are as a student.
Physical Visual Design
We divided each letter of AIGA and assigned a specific category to each including Satisfaction, Education, well-roundedness and Stability. But how did we define which questions draw knowledge of these categories? That’s why we conducted manual machine learning by comparing questions and discovering correlation between them. We played with different color options that are present in AIGA system and focused on maintaining current AIGA brand identity.
User Experience Design
Part 1: Physical Model
The key posters are a visual for assisting users to understand and before they interact with the subjects. Below is the example of how to use key posters to determine each element in CFA’s specific logo. Each category within the blocks are manipulated based on the responses of 3-4 questions from that school. The percentage next to the letter indicate the sense of colleges as a whole within the category.
Part 2: Mobile App
Mobile App was created to further assist users to actively engage and enjoy decoding process. We ideated an app that handles our logos as QR codes to identify the logo and shows data that are related to each element. This touch point brings another layer of interaction that adds fun to data visualization and will be more inclined to explore provided information.
Part 3: Website
Website was mainly created for a feedback system. We wanted to make sure survey participants are able to view their contribution on the project. This way, we could add another interaction layer to draw people’s attention to explore the data.
Moving forward, I would like to explore more of potential app features on top of AR data visualization. For this project, I believe that we came out strong by providing various touch points within the data visualization system. Thus if we had more time available, we would have definitely expanded on mobile app feature for its nature of convenient interaction that people experience daily basis. Below are the summary of questions that came up:
What are some other features that can enhance data visualization experience through mobile app?
Is there a way to make this system into a sharable experience for the participants?