That car data plot is an example of a multidimensional parallel graph. Too bad he didn't go into more depth on this since he showcased this visual representation in his blog post. Some more info you get your feet wet is here:
http://filer.case.edu/~dbh10/eecs466/report.html
Parallel graphs were a starting point on multidimensional al visualization. They are useful only to a certain point of complexity, then they are counterproductive as it takes more time to analyze the graphs than to view a number of simpler graphs side by side or in series. Newer techniques involve radial visualization, such as
It's proper name is "Parallel Coordinates", popularized in the 90s.
Pro:
- Spacing saving. Cartesian space is expansive as the X/Y axis can quickly take up the entire 2D plane. Turning the axis parallel to each other saves a lot of space hence the possibility for multi-dimensionality.
Con:
- Messy. A normal dot in X/Y scatter plot is "stretched" into a line. So a lot of crossings/overlaps.
- Order of the axes matters
Interactivity can help reduce the visual complexity, through axes reordering, or highlighting (also called "brushing").
Also, after little bit training, people can easily develop the ability to recognize patterns in Parallel Coordinates, just think how you learned to read the regression lines in scatter plots : )
Parallel graphs were a starting point on multidimensional al visualization. They are useful only to a certain point of complexity, then they are counterproductive as it takes more time to analyze the graphs than to view a number of simpler graphs side by side or in series. Newer techniques involve radial visualization, such as
http://www.infovis-wiki.net/index.php?title=Radial_Hierarchi...
Something to consider with your animations is the cognitive load of animations of complex visualizations. See the following paper:
http://geoanalytics.net/GeoVisualAnalytics08/a15.pdf