data visualization and visualization 1
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data visualization and visualization 1
Running head: DATA VISUALIZATION AND VISUALIZATION 1
DATA VISUALIZATION AND VISUALIZATION 2
Data Representation and Visualization
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There are various storytelling methods that can be used in presenting data. The first one is using the bar charts. This is a technique that is used to represent the quantities of different categories. The number of bars represents the total categories under consideration. The length or the height of the bars is the quantities being represented (Kirk, 2016). For example, in relating the difference in per capita income between six countries, a total of six bars are developed with each of them having a length that is equivalent to the value of per capita. The highest bar represents the country with the highest income.
Dot plots can also be used to tell a story on a particular variable. They use dots to show the relative quantity of a particular category. They can be differentiated by colors to show different categories of variables in the same graph(Kirk, 2016). For example, the information regarding the distribution of population in a particular area can be represented using the dots chat. The areas with dense population will have more dots together.
The third storytelling technique is a range chart. It uses bars which are displayed for different categories. The upper and lower positions of the chart represent the minimum and the maximum quantities of a variable. Therefore, the bar lengths are the range between the two limits(Kirk, 2016). For example in determining the temperature for different days, the maximum and the minimum temperatures are plotted for each day. The range can be determined by the length differences.
The importance of these techniques is that they provide a better method for visualization of data. However, they are beneficial because they decrease the complexity of data through simplification of numerous and huge values (Prodromou& Dunne, 2017). They also make the meaning to be easily understandable within a short time by giving clarity to the entire data.
References
Kirk, A. (2016). Data Visualisation: A Handbook for Data Driven Design. Thousand Oaks, CA: Sage Publications, Ltd.
Prodromou, T., & Dunne, T. (2017).Data Visualisation and Statistics Education in the Future. In Data Visualization and Statistical Literacy for Open and Big Data (pp. 1-28). IGI Global.
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