Pittsburgh: Wealth and Well-Being
Does the wealth and employment rate of citizens affect their physical health and mental well-being?
This project was a data visualization exercise to illuminate connections between the lifestyle, health, mental well-being and the economic make-up of the citizens in different neighborhoods in an effort to help the viewers recognize, engage in, and think critically about the important relationships.
Individual Project, Communication Design
Duration: Four weeks
Tools: Numbers, Illustrator, Sketch, Principle
Exploring data, finding connections
While parsing through all the data sets, I discovered a somewhat clear connection between the lifestyle and health of the citizens with their education and economic status.
Making Data visible
Using Wurman's LATCH principles, I moved forward to organise the data and from Yau's Coordinate systems looked at the possible ways of visualising my data. These methods helped me organise and analyse information to set hierarchy, interaction and narrative of the visualisation.
Exploring the Form
Since this visualisation was essentially about the quality of living of the citizens, I wanted the visualisation to have a more humane feeling as opposed to a very quantitative representation.
The Final Narrative
Visual layering to depict neighborhood characteristics
The Key Stages
How I got there...
Learning: There was too much cognitive overload on screen 1. It's important to differentiate between additive vs integrative approach.
Learning: My variables seemed very abstract here. I was suggested to adopted a more human quality to the visualisation.
The final Concept
Data visualisation has been the toughest project to crack from conceptually. To be able to transform abstract numbers to tell a story where the variables maintain a close cognitive connection to the context was a huge learning. Through the process I also learnt how to build up similar paced sequences in a story while focussing on the micro-interactions and transitions. Moving forward I want to explore how I can add more granularity of information in the income and employment axis.
Detailed process blog here