Artificial Circumstances Select A Sample Visualization Or In
Artificial Circumstancesselect A Sample Visualisation Or Infographic P
Artificial Circumstancesselect A Sample Visualisation Or Infographic Project and identify all the composition choices on display. Pretend you are now the designer working up some new composition choices in the face of having to accommodate new contextual factors, how might you colour this project if… You had to demonstrate the worst possible data visualisation composition practices (in the same space) You had to force yourself to use as small a space as reasonably possible? You have to transpose the work from landscape > portrait or vice-versa? Assignment Link:
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The task involves analyzing a given sample visualization or infographic by examining its composition choices in detail. The initial step is to identify all key design elements such as layout, color palette, typographic choices, data representation methods, spacing, and visual hierarchy. This comprehensive assessment helps understand the effectiveness and underlying principles behind the current design.
Following this, the hypothetical scenario requires reimagining the visualization under new contextual constraints. There are three specific challenges to consider: first, to modify the visualization to demonstrate the worst possible data visualization practices within the same space; second, to adapt the design to fit as small a space as reasonably possible; and third, to transpose the visual from landscape to portrait orientation or vice versa.
When illustrating poor practices, the focus should be on exaggerated or misleading color schemes, cluttered layouts, inconsistent typographies, overly complex or trivial data representations, and a lack of clear visual hierarchy. The aim is to highlight what makes a data visualization ineffective or confusing, such as using inappropriate color palettes that distort interpretation or clutter that overwhelms the viewer.
In terms of space constraints, the challenge is to compress the visual without losing essential data or clarity. This involves clever use of space-saving techniques such as abbreviation, simplifying chart types, or stacking information vertically or horizontally, all while maintaining readability and functionality.
Transposing from landscape to portrait or vice versa requires careful adjustment of layout elements to optimize space utilization in the new orientation. This involves repositioning key components, adjusting element sizes, and ensuring that the overall information flow remains logical and engaging.
Overall, this exercise aims to deepen understanding of effective visual composition by exploring its boundaries and limitations. It encourages critical thinking about design principles, contextual adaptability, and the impact of aesthetic choices on data comprehension. Such exercises are valuable for developing more thoughtful, adaptive, and communicative visualizations in professional data visualization practice.
References
Journal of the American Statistical Association, 79(387), 531-554. Now You See It: Simple Visualization Techniques for Quantitative Data. Analytics Press. Data Visualization: A Handbook for Data-Driven Design. Sage Publications. Harvard Business Review. Proceedings of the IEEE Symposium on Visual Languages, 336-343. O'Reilly Media. The Semiology of Graphics. University of Wisconsin Press. Effective Data Visualization: A Guide for the Data-Driven. TDWI Publications. Proceedings of the IEEE Virtual Reality Annual International Symposium, 11-17. The Functional Art: An Introduction to Information Graphics and Visualization. New Riders.