Question

Week 12 Discussion Board 100 pts Discuss the importance of format, location, and frequency in determining the optimal data visualization method. Is one more important than the other? Provide at least three reasons to support your argument. Include graphics to illustrate your argument. Provide a minimum of three references.

Answer 1

As we know that Data Visualization is a method where compex data is represented in such attractive form or graphica form which helps readers to attract attention and let them understand the information more easily. In this method, Format, Location and frequency plays important role to make data visualization optimal.

Format of data visualization is important because Format gives the structure to data and make strong base in furture process. Format helps to create attraction and gain attention of readers at first go. for example- A data formated in Table or graph is more attrative than written in paragraph. Format can take many forms like tables, charts,bars etc.

Location is also important in making data visualization because it helps to identify the best place to put data in a report or presentation. if data is not properly located then it can cause confusion to reader or audience. for example- A bar graph showing the sales process is located at different number of heading then people will face confusion to know what the graph is actually about.

Another example- if data related to employment in gender basis involve face diagram of men over female title and Girl diagram over Male title. so it would be problematic.

Frequency means how well data is grouped or used for how many times in data visual. as we know data visualization should be simple and concise so over putting data or same data cause ineffective data visuals and knowing frequency is important.

I think all three are important but if i have to choose then i would choose location as more important then format then frequency because misplacement or poor location of data cause audience to confusion and results are not good like in a presentation if any slide get out of place then presenter as well as audience will get confused.

References:

Yang, H. H., & Moody, J. (2000). Data visualization and feature selection: New algorithms for nongaussian data. In Advances in neural information processing systems (pp. 687-693).

Patterson, J. F., Rohall, S. L., & Wijeyekoon, A. (2003). U.S. Patent No. 6,529,900. Washington, DC: U.S. Patent and Trademark Office.

Risch, J. S., Dowson, S. T., Hart, M. L., & Hatley, W. L. (2008). U.S. Patent No. 7,373,612. Washington, DC: U.S. Patent and Trademark Office.