Kirk (2019) tells us that Data adjustments affect what data is displayed and Presentation adjustments affect how the data is displayed. Each of these adjustments involve specific features. Data adjustments include: Filtering, Highlighting and Participating. Presentation adjustments include:  Annotating, Animating and Navigating. This week you also reviewed: “What is Interactive Visualization”, a website at:  https://www.sisense.com/glossary/interactive-visualization/

Reply Post

Select one Data adjustment feature and one Presentation adjustment feature and expand on each.

When replying to a classmate’s post, offer your opinion on what they posted – give your thoughts about their chosen interactivity feature and/or other information you learned about how interactivy works in visualization.

Discussion 1:

Data filtering is one of the data adjustments features, and it is the process of removing unwanted components from a given data. Also, it is the process of selecting a part of data and using its subset to do analysis or view data. It needs one to give a specific rule or identify the logic of the cases included in the data analysis. It is also referred to as sub-setting data or drilling down of data. One reason for filtering is to clean data and remove the information that has errors and is not applicable for analysis. For instance, a decision can be made to remove respondents who have not completed a given survey and the ones who raced through the survey and selected the answers with attention to the answer. Filtering also can be used to view results and evaluate the performance of the statistical models and algorithms. the primary purpose is to divide the sample into several groups and apply the analysis independently to every group and giving

Annotating is one of the data presentation adjustments, and it is the process of placing labels on data that appear in various formats like videos and text. For the case data visualization, chart annotation gives out an extra detail that highlights the points or uses for disambiguation. Therefore, when an annotation is filled with graphics, it will distract the data visual silence, and thus it is vital to find the most appropriate balance. The annotations can be well applied in giving specific details on the individual points, line segments, and cluster points. Also, they can be applied in the explanation of empty spaces (Brock, 2015). The most use of annotation is printing bars values in the bar chart. It is thus helpful but cannot be a compulsory requirement. Annotations also use a simple typeface, just like axis labels. There is no need to use vibrant colors and fancy fonts because they act as distractions and hardens the reading.

References

Brock, T. (2015). How to Improve Your Data Visualizations with Annotations. Data visualization: Annotations, https://www.infragistics.com/community/blogs/b/tim_brock/posts/annotate-wisely#:~:text=Merriam%20Webster%20defines%20an%20annotation,be%20used%20for%20disambiguation%20purposes.

Facer, C. (2020). data adjustments: data filtering. Data adjustments, https://www.isa.org/intech-home/2018/july-august/features/data-filtering-in-process-automation-systems.

Discussion 2:

Interactive Data Visualization focuses on the graphical representation of the data that the user interacts with the data and the information. It is also known as the visual display used by the analytics to represent the data using the various analytical tools. Visualization makes the data representation easier for the user to explore, manipulate and interact with the data. The data analytics uses various dynamic charts, changing colors, and shapes based on the queries ot interactions. One of the best things about the interactive visualization is that it offers better access to real-time data. (sisense, n.d.)

One data adjustment feature that I would like to mention is the focus. Focusing is the feature that controls the visually emphasized data and how the data are highlighted. This focus feature helps the users choose the value that has the top most priority and the values that bring the most attention to the user. So in the focus feature, we can say that the values that have the most importance are obtained in the first place. The users think or choose to get attention to the filters, maybe through the modification and making it more attractive through various effects such as depth through colors such as the foreground, mid-ground, and background. The change can be done in other ways, too, like sorting arrangement; when we use this technique, no data are eliminated from the display but simply relegating in its contrast or position. (Kirk, 2016)

Example: Geography on the avg national income, In the model, we can bring the average national income and get to the left, and the left will emphasize different cohorts of the income distribution of people across the United States.

References

Kirk, A. (2016). Data Visualization: A handbook for Data-Driven design. Thousand Oak, CA: Sage Publications Ltd.

sisense. (n.d.). Interactive Data Visualization. Retrieved from sisense: com/glossary/interactive-visua…”>https://www.sisense.com/glossary/interactive-visua…