Information visualization is tangibly valuable. However it is very difficult to quantify this value, which is exactly where InfoVis comes as a valuable asset. The paper discusses a big number of topics that are relevant not only for visualization purposes, but also for data mining and unsupervised machine learning.
One of the biggest challenges of data mining is to explain patterns and associations in higher dimensions. While visualization can support and supervise the data mining algorithms in few dimensions, visualization is limited to explain only a few dimensions at a time.
There is also a detailed discussion on some classical examples for visualization like Napoleon’s march to Moscow and Snow’s cholera pandemic source illustration, two graphs we saw in class. The paper also does a very good job explaining basic principles and dimensions for InfoVis (proximity, similarity, continuity, symmetry, closure, relative size) and why they are all important. It includes relevant examples and illustrations that are to be used for further reference. However it lacks consistency when trying to quantify the value of InfoVis.
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