SMHS SciVisualization

Scientific Methods for Health Sciences - Scientific Visualization

Questions

• How and why should we “look” at data?
• What data characteristics are important for exploratory data analytics (EDAs)?

Scientific Data-driven or Simulation-driven visualization methods may be classified in many alternative ways. Visualization techniques can be classified according to many criteria:

• Data Type: structured/unstructured, small/large, complete/incomplete, time/space, ascii/binary, Euclidean/non-Euclidean, etc.

• Task type: Task type is one of the aspects considered in classification of visualization techniques, which provides means of interaction between the researcher, the data and the display software/platform

• Scalability: Visualization techniques are subject to some limitations, such as the amount of data that a particular technique can exhibit

• Dimensionality: Visualization techniques can also be classified according to the number of attributes

• Positioning and Attributes: the distribution of attributes on the chart may affect the interpretation of the display representation, e.g., correlation analysis, where the relative distance among the plotted attributes is relevant for observation

• Investigative Need: the specific scientific question or exploratory interest may also determine the type of visualization:
  • Examining the composition of the data
  • Exploring the distribution of the data
  • Contrasting or comparing several data elements, relations, association
  • Unsupervised exploratory data mining

• test (indented bullets)

http://www.socr.umich.edu/CSCD/html/Cores/Macore2/SciViz.html

Complex Network Visualization

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• SOCR Home page: http://www.socr.umich.edu

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