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Discussions responses

Discussion 1:

Data Visualization: Line Chart A Line chart is commonly used in many fields. The information is displayed by plotting the data on the chart using connecting points. The line can either be straight or continuous.  This chart is particularly effective as it helps in visualizing data trends over a time interval. Once can display the trends in data over a period of time. It also helps in predicting course of the data set during a specific period of time or otherwise. A line chart is advantageous over other types of visualizations such as pie, bar chart. This is because a pie chart forces comparison between the parts of the data which is difficult to handle especially when handling large data sets. A line chart is easier to use than a pie chart as one just has to compare the lengths of the bars. However, a line chart is most recommended as it helps us to visualize the change in data trends over time. Compound Visualization: Learning Map A learning map is a two-dimensional illustration of the data. It includes details about the topic and subtopics, relationship between the different components of the map, a line that helps to understand the relationship between the topic and the subtopics. A learning map is similar to knowledge map. Unlike the knowledge maps that focus on the geographical setting; learning map focuses on the visualization. Learning maps have a a strong advantage over other compound visualizations as they illustrate a big picture (it is easier to visualize when the entire unit is laid out infant of you). They allow repeated reviews as various discussions reflect on how well visualization process is goin on.  References: Khan, M., & Khan, S. S. (2011, November). Data and Information Visualization Methods, and Interactive Mechanisms: A Survey. International Journal of Computer Applications. Retrieved from https://www.researchgate.net/profile/Muzammil_Khan5/publication/264623537_Data_and_Information_Visualization_Methods_and_Interactive_Mechanisms_A_Survey/links/5530b5580cf20ea0a06f8495/Data-and-Information-Visualization-Methods-and-Interactive-Mechanisms-A-Survey.pdf

Discussion 2:

According to Kraker, Kittel, & Enkhbayar (2016), the human eye tends to get attracted to colour. From this perspective, it can be said that data visualization aims at presenting data in a pictorial form. As per Williamson (2016), data visualization is a process of representing data in visual context in which cases of trends, patters, and correlations that might be un-noticed in a text-based format can be recognized easily. With the advancement in technology, data visualization has advanced and goes beyond traditional charts and graphs to embrace sophisticated ways of representing data in heat maps, bar, infographics, fever charts and sparklines (Ward, Grinstein, & Keim, 2015).

From the periodic table, one method of data visualization is the use of pie charts. For me, it is easy to work with pie charts, in which the slices in the pie chart depict numerical proportion of a given variable. For example, from the periodic table, the pie chart in this case shows the operations review of each of the continents.  From the pie chart above, it is easy to read the numerical value of each continent. At the same time, they can be interpreted easily will less explanation needed as the audience can easily understand what is being communicated (Williamson, 2016). However, one of the main issues of pie charts is that when there are more slices, it is hard to judge the size of the small sizes.

Under compound visualization, I would like to pick knowledge maps. According to Ward, Grinstein, & Keim (2015), a knowledge map is a visual aid that shows where knowledge can be found within a group or organization, and how to find those with the most expertise. Often referred to as an “inventory of knowledge”, these maps are organized using various interconnected nodes to make it easy to find out where to look for information. As per Williamson (2016), knowledge maps are essential when dealing with large amounts of complex data as they offer a simple too for finding someone who can help based on their expertise. In this case, knowledge maps refer to sources such as documents, databases, and recordings. To Kraker, Kittel, & Enkhbayar (2016), Knowledge maps are very powerful tools to inventory a company’s critical knowledge. They can also pinpoint areas within the organization that may be at risk. Sometimes, the simple act of creating a knowledge map shows up the weak links. It may also reveal bottlenecks in the flow of data and knowledge.

Reference

Kraker, P., Kittel, C., & Enkhbayar, A. (2016). Open knowledge maps: Creating a visual interface to the world’s scientific knowledge based on natural language processing. 027.7 Zeitschrift für Bibliothekskultur/Journal for Library Culture, 4(2), 98-103.

Ward, M. O., Grinstein, G., & Keim, D. (2015). Interactive data visualization: foundations, techniques, and applications. AK Peters/CRC Press.

Williamson, B. (2016). Digital education governance: data visualization, predictive analytics, and ‘real-time’policy instruments. Journal of Education Policy, 31(2), 123-141.