Data visualization

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Methods Developing a motion chart is a technique can be used to avoid the problems stated above. It can avoid chart junk which contains extraneous visual elements that distract from the message which should not be in the chart. It prevents distracting patterns, overbearing colors and even unnecessary grids and outlines. According to Edward Tufte, chartjunk can turn bores into disasters, but it can never rescue a thin data set. It also helps to improve standardized scale size of each circle. It improves the correctness and effectiveness as the information conveyed by visualization is more readily perceived than information in other. Besides that, data ink ratio can be maximized. The unimportant information can be eliminated for the sake of simplicity without loss of data information. Moreover, sufficient data and information can be provided for the chart. It provides tagging and enough data to show the playfulness of the chart. It can make a chart more informative by improving data density. Furthermore, it can make it more interactive for the chart. It allows users to directly interact with and dynamically change the visualization according to their viewing purposes. Step 1 is generating or gathering data set from the website of Malaysian General Election GE12 and GE13. Step 2 is filtering and processing data by using Microsoft Excel to do the calculation of data set of voting results. Step 3 is to do the visualization by sketching the motion chart on paper. Step 4 is to choose the toolkit or programming language used to develop the chart that is Google Code Developer. Step 5 is the process of generating code/ editing coding which can be implemented by debugging the code, fixing the code error and then testing the code. Last step i... ... middle of paper ... ... the dimension of size or color, a default size and color are applied respectively. Our planning is to use the method of interpolation and regression analysis in order to fill in partially or completely missing data values. The ability of logarithmic, exponential and polynomial models can be added in the chart for transforming data variables dynamically and effectively. Works Cited Jameel Al-Aziz, Nicolas Christou, Ivo D. Dinov. (2010). SOCR Motion Charts: An Efficient, Open-Source, Interactive and Dynamic Applet for Visualizing Longitudinal Multivariate Data. In Journal of Statistics Education, Volume 18, Number 3. Retrieved from SAS Institute Inc. (n.d.). Data Visualization: What Is It and Why Is It Important?. In SAS: The Power to Know. Retrieved from
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