Is it easier to glean insights from data using a physical model? Most of the advances in technology (data visualization, charting, interactive graphics) have made it much easier to get insights using on-screen imaging. By and large they have replaced physical construction and drawing of charts with rulers, compasses, and inks. But what if we used physical models to understand data better? I’ve been reading three books recently which make the case for a physical, real world models to help understand:
1) How Round isYour Circle (John Bryant and Chris Sangwin) [website] describes ways of physically representing mathematical equations. In one example, they model harmonic series divergence using stacks of dominoes which can be measured to show the proof. A harmonic series is 1+1/2+1/3+1/4+1/5.…
2) Mathematical Models (Condy and Rollett) was a groundbreaking book in the early 1950s. Cundy in particular was instrumental in developing the “new math” curriculum in Britain in the 1960s. The book is filled with ideas on modeling mathematical concepts, including polyhedra, wire models, and quadratic surfaces. Bryant and Sangwin acknowledge the influence of this book on their work.
3) Architectural Models (Megan Warner) describes materials and methods of creating architectural models. While not strictly data modeling, the methods can be used to create models of 3d graphs and charts.
In a later post, I will create some Venn diagram models using both 2d and physical models as an experiment.