Identifying Design Principles
From CS294-10 Visualization Sp11
Lecture on Mar 16, 2011
- Pictorial and verbal tools for conveying routes, Lee & Tversky (pdf)
- Rendering effective routemaps, Agrawala & Stolte (pdf)
- Identification and validation of cognitive design principles for automated generation of assembly instructions, Heiser et al. (html)
- Designing effective step-by-step assembly instructions, Agrawala et al. (html)
Brandon Liu - Mar 16, 2011 05:43:09 pm
An observation I had on the assembly instructions project was that the computer-generated instructions had a consistent, isometric perspective. I would be interested in seeing the hand-drawn instructions, and how the use of a consistent perspective relates to spatial ability. My intuition tells me that people with high spatial ability would minimize the number of times the drawing changed its perspective; only in the cases where a piece goes in an occluded spot does the perspective changes. In most cases, it seems that 2-3 perspectives would be enough to cover all cases. Another interesting facet of this area is how to use zooming in instructions. In some cases, we may need to 'zoom in' on a component to describe more detail. A great dataset for this would be those LEGO instruction booklets.
Michael Porath - Mar 16, 2011 07:37:10 pm
The Mapblast project reminded me of a conversation about mental maps I had lately. Most of the maps I come across are drawn to scale. Mental maps, just as Mapblast, recognize the fact that this is not how we perceive the world. While spatial information is important for many purposes, it doesn't reflect our representation of space.
For my final project I'm looking at visualizing people's driving and mobility patterns. One thing this discussion sparked was whether I could show mobility patterns other than based on spatial distances. A more obvious way to do that would be to distort the distances based on the time it takes to get from one point to the other. The data I'm working with samples a person's location, speed, and velocity based on GPS information. Showing two data points equidistant from each other would do that distortion in a very obvious way. San Francisco and Palo Alto, around 40 minutes away from each other, would be as far away from each other as the two sides of the Bay Bridge during rush hour are when driving during rush hour. Another representation along the same lines could distort the spatial distances based on CO2 emissions or gas consumption. Does anyone know of seminal papers about map distortions?