Developing Frameworks for Data Representation Marcus Lem, MD, MHSc, FRCPC Social Networks Analysis and Visualization for Public Safety Workshop Wachtberg-Werthoven, Germany, Oct. 18, 2005

SCIENCE FINDS, INDUSTRY APPLIES, MAN CONFORMS. Hall of Science, Chicago World’s Fair, 1933

Framework  Who - Public Health  What - Information analysis & transfer  Why - Strategies & interventions  How - Appropriate data representation

Disciplines: Arts and Sciences  Public Health  Statistical graphics  Computer Science  Decision making  Cartography

Public Health: Levels of Decision Making  Political  Health Policy (Strategic)  Public Health (Operational)  Clinical (Tactical)

Public Health Perspective  ↑ Information sharing (i.e.. not superiority)  ↑ Comprehensibility to management  ↑ Utility for decision making  Conform to standards (e.g.. WHO)

Public Health: Information Needs  Establish links / connections  Directionality  Strength of association  Time elements  Identify key players / problem areas  Suggest interventions  Express degree or uncertainty  Alternate explanation → intervention

Principles of Graphical Excellence  Substance, statistics, design  Clarity, precision, efficiency  Greatest number of ideas in the shortest time with the least components in the smallest space (data density)  Multivariate  Truth

Information Display - Poor

Information Display - Good

Computer Science: Ergonomic Quality - 1  Primary criteria Speed Accuracy Pleasurability Influenced by secondary criteria

Ergonomic Criteria - 2  Secondary criteria Learning and recall time Short and long term memory load Fatigue and error susceptibility Naturalness and boundedness Effect of context Effect of user experience and knowledge

Colour Palette

Intuitive Framework 1: Hot to Cold Spectrum

Intuitive Framework 2: Mood / Expression

Intuitive Framework 3: Verbal Expressions  Case fatality (“drop dead”)  Depletion e.g.. CD4 (“burn out”)  Pandemic spread (“tidal wave”)  Frameworks may be culture-specific and need to be tested against identified audience

Decision Making: Optimal Choice Models  A set of alternative courses of action (acts)  A set of possible events associated with each course of action  A value to be associated with each act- event combination  The degree of knowledge with regard to the chance of each of the events occurring

Criteria for Decision under Certainty  Maximization  Minimization  “Satisficing”

Criteria for Decision under Uncertainty  Pessimist (maximin / minimax)  Optimist (maximax / minimin)  Pessimist-Optimist mixture - weighting  Maximization / Minimization  “Satisficing”

Cartographic Principles  U1- reality as seen by cartographer  S1- cartographer  L- language, symbols, rules  M- map  S2- map user  U2- reality as seen by the map user  Ic- cartographic information  Simplicity of design and complexity of data

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Grudin’s law: When those who benefit are not those who do the work, then technology is likely to fail or, at least be subverted.

Get it right or let it alone. The conclusions you jump to may be your own. James Thurber, Further Fables for Our Time (New York 1956)

References  The visual display of quantitative information. Edward R. Tufte. Graphics Press,  Things that make us smart – defending human attributes in the age of machines. Donald A. Norman. Addison-Wesley Publishing Company, 1993  Statistical graphics – Design principles and practices. Calvin F. Schmid. John Wiley and Sons, Inc.,  The human factors of graphic interaction tasks and techniques. James D. Foley, Victor L. Wallace, Peggy Chan. Dept of Computer Science, University of Kansas,  Statistics for decisions – An elementary introduction. Gerald E. Thompson. Little, Brown and Company, Inc., 1972

Acknowledgements  Ann Jolly  Public Health Agency of Canada  Ali M. Binsilim  Communicable Disease Control Division, First Nations and Inuit Health Branch, Health Canada

Questions? ?