My colleague prof. Jack van Wijk pointed me towards these great guidelines by Deloitte on how to design an effective dashboard.
Some of these rules are more generally applicable to data visualization. Yet, the Deloitte 10 commandments form a good checklist when designing a dashboard.
Here’s my interpretation of the 10 rules:
Know your message or goal
Choose the chart that conveys your message best
Use a grid to bring order to your dashboard
Use color only to highlight and draw attention
Remove unneccessary elements
Avoid information overload
Design for ease of use
Text is as important as charts
Design for multiple devices (desktop, tablet, mobile, …)
Recycle good designs (by others)
In terms of recycling the good work by others operating in the data visualization field, check out:
The People + AI Guidebook was written to help user experience (UX) professionals and product managers follow a human-centered approach to AI.
The Guidebook’s recommendations are based on data and insights from over a hundred individuals across Google product teams, industry experts, and academic research.
These six chapters follow the product development flow, and each one has a related worksheet to help turn guidance into action.
The People & AI guidebook is one of the products of the major PAIR project team (People & AI Research).
Here are the direct links to the six guidebook chapters:
I really like generative art, or so-called algorithmic art. Basically, it means you take a pattern or a complex system of rules, and apply it to create something new following those patterns/rules.
When I finished my PhD, I got a beautiful poster of where the k-nearest neighbors algorithms was used to generate a set of connected points.
As we recently moved into our new house, I decided I wanted to have a brother for the knn-poster. So I did some research in algorithms I wanted to use to generate a painting. I found some very cool ones, of which I unforunately can’t recollect the artists anymore:
Note: these are NOT mine
However, I preferred to make one myself. So we again turned to the work of the author that made the knn-poster: Marcus Volz.
He has written (in R) many other algorithms. And we found that one specifically nicely matched the knn-poster. His metropolis – or generative city:
However, I wanted to make one myself, so I download Marcus code, and tweaked it a bit. Most importantly, I made it start in the center, made it fill up the whole space, and I made it run more efficient so I could generate a couple dozen large cities quickly, and pick the one I liked most. Here’s the end result:
