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Information Design Trends Unit Three: Information Visualization Lecture 1: Escaping Flatland.

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Presentation on theme: "Information Design Trends Unit Three: Information Visualization Lecture 1: Escaping Flatland."— Presentation transcript:

1 Information Design Trends Unit Three: Information Visualization Lecture 1: Escaping Flatland

2 What is Flatland When information from the real world is transferred to a two-dimensional media such as paper or a computer screen, we enter Flatland. “All the interesting worlds that we seek to understand are inevitably and happily multivariate in nature.” -Edward Tufte

3 Tufte’s Design Goals Visualizations should strive towards the following goals: content focus comparison rather than mere description integrity high resolution utilization of classic designs and concepts proven by time

4 Tufte’s Basic Principles 1. Enforce visual comparisons. 2. Show causality, the mechanism, and process. 3. Display multivariate information. 4. Integrate text, numbers, images into a complete picture. 5. Show information adjacent in space rather than stacked in time. 6. Use small multiples. 7. Presentations stand or fall based on the quality, relevance, and integrity of your content.

5 Chartjunk Definition: miscellaneous graphic gunk attached to a chart (visualization) that has nothing to do with the data and everything to do with poor taste. The principle of 1+1=3 The space between 2 objects can create new objects

6 Data-ink ratio Maximize the data-ink ratio, within reason Erase non-data-ink, within reason Erase redundant data-ink, within reason

7 micro-macro information display The concept of looking for hierarchies in large data fields. To clarify, add detail. Visualization is condensed, slowed, and personalized. Artificial boundaries can be good for dividing up information. Stem and leaf plots can save characters and give better visual comparisons. Clutter and confusion are failures of design, not attributes of information.

8 John Snow and the Cholera Epidemic of 1854 Possible cause-effect relationship rather than simple time-series. Making quantitative comparisons. Considering alternative explanations and contrary cases. Assessment of possible errors in the numbers reported in graphics.


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