To create an interactive data visualization of unconventional (and plentiful -- 6000+ images worth of) data: fashion photos on a runway.
Using information visualization best practices and user-centered design principles, I built an interactive visualization to investigate color trends over the seasons.
Data Analyst: Python (for an explanation of our methods, see the site; for a more in-depth and visualization explanation, view the report in the "Deliverables" section below).
As design lead of this project, I quickly created a demonstrative wireframe of the general concept behind the visualization: a series of images, as though the model is stepping through the seasons, and a filter allowing for comparisons within dimensions of interest (i.e. designer country of origin, Fashion Week city, season, etc). This concept was presented to an audience, with no confusion and much intrigue.
Through our testing, our users felt our product was strong in the following areas:
Our users also felt our product was weak in the following areas:
Overall, I am very satisfied with this data visualization. It was a great learning experience in how to creatively incorporate design principles to a data analysis project, work that is often seen as dry. It was well-received, and we received high marks on the assignment and praise on Twitter.
It would be phenomenal to incorporate additional designers, just to add to the sheer comparison power of our data visualization. Additionally, it would be worthwhile to experiment with adding a color filter, like some users proposed. While the addition of such a filter might detract from more low-level hypotheses (similarity across citizenship, geographical Fashion Week location, etc), it was a popular request and should be considered.