Note Question And Textbook Attached: Composition Deals With ✓ Solved

Note Question And Text Book Attachedcomposition Deals With The Over

Choose a component of either project composition or chart composition and discuss. The options are:

  • a- Wireframing
  • b- Storyboarding
  • 2- Chart Composition
  • a- Chart size
  • b- Chart scales

Your initial post should be posted by Wednesday 11:59 PM (EST). Follow-up replies to classmates should be made by Sunday 11:59 PM (EST), with a total of three posts for the week. A quality post should include reasons for agreement, examples, or related topics from credible sources.

Sample Paper For Above instruction

In the realm of data visualization, selecting the appropriate component of project or chart composition is crucial for effective communication of data insights. For this discussion, I will focus on chart scales, a vital element of chart composition that directly impacts how data is interpreted by viewers.

Understanding Chart Scales in Data Visualization

Chart scales refer to the range and distribution of data points represented on a chart. They determine how data is scaled and consequently influence the viewer's perception of data relationships, trends, and outliers. Properly chosen scales enhance clarity and accuracy, whereas poorly selected scales can mislead or confuse viewers.

The Importance of Appropriate Chart Scales

Choosing the correct scale is essential to accurately depict data. For example, using a linear scale for data that spans several orders of magnitude might obscure important differences, whereas a logarithmic scale can reveal proportional relationships across wide-ranging data sets (Kirk, 2016). Misuse of scales can result in misinterpretations, affecting decision-making processes in business, science, or policy.

Examples of Chart Scale Application

In financial data visualization, logarithmic scales are often used to depict stock price increases over time, providing a clearer view of growth rates (Few, 2009). In scientific data, the choice between linear and logarithmic scales depends on the nature of the measurement and the audience's familiarity with the data. When presenting survey results with a narrow range, a linear scale may be most appropriate, while broad-ranging data might benefit from a logarithmic scale to prevent compressing large differences.

Implications for Data Visualization Practice

Designers and data analysts must carefully consider their audience and the message they intend to convey. The choice of chart scales should facilitate comprehension without distorting the data's story. User-centered design principles suggest testing various scales and seeking feedback to ensure interpretability and effectiveness (Kirk, 2016; Tufte, 2001).

Conclusion

In sum, chart scales are a fundamental component of chart composition that greatly influence data perception. Thoughtful selection and application of scales can lead to clearer, more truthful visualizations that empower viewers to make informed decisions.

References

  • Few, S. (2009). Now You See It: Simple Visualization Techniques for Quantitative Communication. Analytics Press.
  • Kirk, A. (2016). Data Visualization: A Handbook for Data-Driven Design. SAGE Publications.
  • Tufte, E. R. (2001). The Visual Display of Quantitative Information. Graphics Press.
  • Yau, N. (2013). Visualize This: The FlowingData Guide to Design, Visualization, and Storytelling. Wiley.
  • Heer, J., Bostock, M., & Ogievetsky, V. (2010). A Tour through the Visualization Zoo. Communications of the ACM, 53(6), 59–67.
  • Cairo, A. (2013). The Functional Art: An Introduction to Information Graphics and Visualization. New Riders.
  • Knaflic, C. N. (2015). Storytelling with Data: A Data Visualization Guide for Business Professionals. Wiley.
  • Murray, D. (2013). Creating Effective Data Visualizations. Journal of Data Science, 11(2), 137–154.
  • Roberts, J. (2007). Show Me the Numbers: Designing Tables and Graphs to Enlighten. Value Line.
  • Evergreen, S. D. (2017). Effective Data Visualization: The Right Chart for the Right Data. SAGE Publications.

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