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Mapping

Breakout session on. Mapping. Lead: All Participants: Andre Skupin, Peter Hook, Loet Leydesdorff, Katy Borner, Ned Talley, Kevin Boyack. State of the Art. Map vs. classification Partition vs. Hierarchy Visualizations make it possible for human cognition to construct knowledge.

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Mapping

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  1. Breakout session on Mapping Lead: All Participants: Andre Skupin, Peter Hook, Loet Leydesdorff, Katy Borner, Ned Talley, Kevin Boyack

  2. State of the Art Map vs. classification Partition vs. Hierarchy Visualizations make it possible for human cognition to construct knowledge. Maps suggest proximity. Maps are useful as reference frames, e.g., Spotlight circle map. Easy reference frameworks are good.

  3. Existing Problems • Interoperability, e.g., NCBO • Intercomparability – compare different self-contained maps. • Data quality, e.g., define evaluation criteria • Map quality, e.g., legibility, clustering quality, proximity preservation, proper application of semiotic principles. • Map purpose: Alan MacEachren’s Map cube – communication (of something the map maker knew, public) vs. exploration/discovery (nobody knows yet, private). Along this continuum we find different maps. • Take better known entities and replicate real word for general public, e.g., MDS for city Euclidean distances to create a geo map. • Standards of convenience are bad. Mercator is good for sea navigation but bad in Google maps, etc. • How to explain dynamics of knowledge? How to visualize temporal evolution. Use maps to get intuition then go back to data to get quantitative info. • Huge confusion in terms of topologies: Factorial vs. relational. • Cannot separate data analysis and visualization, e.g., too close clusters should be spread out (accuracy decreases) to make them legible. Looks backward to the data and forward to the map usage.

  4. Proposed Solutions • Different purposes demand different projections. • There could be a single reference system. • Need heterarchy, different views of data. • Compare different layouts, sim measures. 1. Measure  size and shape 2. Standardize • reference system 3. Reduce Dimensionality  domain-ignorant 4. Reproject  domain-appropriate 5. Overlay  look for patterns

  5. Associated Challenges and Opportunities • Social network community has standard datasets. Can we have one besides ISI, Scopus (everybody downloads/studies different subset/format). Journal citation dataset, paper dataset, Conference proceedings and attendance/sessions, VIVO? (Need full text) • Precision, recall – what is our gold standard. • One purpose of standards is to empower non-experts to create and read maps. • Tools like Pajek, Sci2 as de facto standard— .net, BC measure, etc. • Accuracy vs. usability • Standardized residuals of the Chi^2 • MDS: specify stress factor • Factor analysis: give variance

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