While facing big data, we developed a visualization strategy based on head/tail breaks in order to see a clear pattern from a hairball. The strategy is to recursively drop out the tail parts until the head parts are clear or visible enough. The head parts are self-similar to the whole, thus the parts enable us to see the whole: http://en.wikipedia.org/wiki/Head/tail_Breaks
Waldo Tobler had the similar strategy by dropping out those below the mean; see http://www.csiss.org/clearinghouse/FlowMapper/
I am curious, what are cognitive implications for the strategy?
Interestingly, density and size are features that have been identified as pre-attentive, so this technique that allows to highlight large from small items that are more numerous combining both size and density in a visualization can help users to quickly perceive patterns. Check this paper "Perception in Visualization"
http://www.csc.ncsu.edu/faculty/healey/PP/
http://www.csc.ncsu.edu/faculty/healey/PP/
Thanks Denis for pointing to the interesting source!
I have one point to add on figure-ground perception: http://en.wikipedia.org/wiki/Figure%E2%80%93ground_%28perception%29
With the right panel of the previous figure, the big dots are considered as figure (the head being a minority), while the small dots as ground (the tail being a majority). Importantly, the head is always distinguished from the tail. In this regard, there is little ambiguity between figure (a minority) and ground (a majority), unlike the Profiles/vase (see below left, the figure and ground is 50 to 50 rather than a minority to majority - the right).
refer this IEEE paper :
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6268174&tag=1
I find some good perspective related to your question in it....it might help you...
- Badges
- Science topic
- Similar topics
- Data Mining
- Classification