Storytelling with Data - See | Show | Tell | Engage
Visualization Methods Overview Presentation Cambridge University Eppler September 2006
1. The Visualization SpectrumA Systematic Overview of Visualisation Methods for Managers Martin J. Eppler University of Lugano (USI) www.knowledge-communication.org / www.lets-focus.com Martin.Eppler@gmail.com Cambridge, IfM, September 28th 2006
3. Outline The Realm of Visualization Visualization Classifications An Activity-based View How to choose the right Method Conclusion :
4. The ABC of Visualization Size Color/ Texture . . Position Animation Orientation Form Source: adapted from J. Bertin
5. Accuracy Ranking of Quantitative Perceptual Tasks Position More Accurate Length Angle Slope Area Less Accurate Volume Color Density Source: Mackinlay 88 from Cleveland & McGill
6. Emprical Results: Use of visualization in management -> Quantitative charts dominate, what about conceptual visualization? Source: Meier, 1994
14. Identical shapes or colors designate identical types of objects (visualize different things differently)A B Source: adapted and expanded from Rhodes, 1991, p. 135.
15. Outline The Realm of Visualization Visualization Classifications An Activity-based View How to choose the right Method Conclusion :
24. An Empirical Taxonomy: Lohse et al. 1994 structure diagrams: description of physical object cartograms: spatial maps showing quantitative data maps: symbolic representation of physical geography graphic tables process diagrams icons: e.g., logos time charts: e.g., Gantt charts network charts: flow chart, org chart, decision trees, pert tree photo-realistic pictures tables: single to multiple rows graphs: quantitative information using position and magnitude of geometric objects. 1-3D, examples: cartesian or polar coordinate system: scatterplots, line bar, pie chart, Chernoff face graphs)
26. Other Taxonomies Shneiderman (1996) proposes a task by data type taxonomy of information visualization with seven data types: one-, two-, three-dimensional data, temporal and multi-dimensional data, tree and network data and seven tasks (overview, zoom, filter, details-on-demand, relate, history, and extracts). Card, et al., 1998) constructed a data-oriented taxonomy for information visualization techniques, which is based on Card and MacKinlay (1997): This taxonomy divides the field of visualization into several subcategories: Scientific Visualization, GIS, Multi-dimensional Plots, Multi-dimensional Tables, Information Landscapes and Spaces, Node and Link, Trees, Text Transforms
27. Outline The Realm of Visualization Visualization Classifications An Activity-based View How to choose the right Method Conclusion :
30. 1. Examples of Envisioning Verbal Metaphors: ‚Backwardparking‘ Analogies: Benzol ring invention Parables: The Elephantandthe 4 blind man Simulation: Mentallyvisualizing an activity.
39. Outline The Realm of Visualization Visualization Classifications An Activity-based View How to choose the right Method Conclusion :
40. When to use which quantitative chart type? Line graph x-axis requires quantitative variable Variables have continuous values familiar/conventional ordering among ordinals Bar graph comparison of relative point values Scatter plot convey overall impression of relationship between two variables Pie Chart? Emphasizing differences in proportion among a few numbers
42. Selecting the right Visualization Activity = i.e., Iceberg risk metaphor = i.e., scenario sketching = i.e., Gantt chart for project
43. The visualization spectrum contains quantitative and qualitative visualization formats. They can be used to depict structures or processes. In order to choose the right method, think about its main purpose, the content type, the target audience and communication situation. Conceive of visualization as an activity and choose among envisioning, sketching, ex-pressing, diagramming, mapping, materializing or exploring. ! Conclusion
51. Eppler, M., Sukowski, O. (2000) Managing Team Knowledge, in: European Management Journal, June, Oxford.
52. Eppler, M. (2006) A comparison between concept maps, mind maps, conceptual diagrams and visual metaphors. In: Information Visualization, September Issue.