1. Purposes of data visualisations Term 2 – Week 10 VCE IT – UNIT 2

2. Purposes of data visualisations Main purpose is to reduce the effort required to analyse the information and increase the effectiveness of the information. Needs to be engaging. Often we make decisions based on the information we are given and therefore visual representations must be accurate and relevant.

3. Design tools for representing data visualisations A variety of tools can be used to represent the design of a data visualisation: – Layout diagram: Needs to show what type of chart might be used and where the source data will occur (along the x or y axis). Should show colours, headings, axis, labels and legends. – Storyboard: Can be used to show how data visualisation animation might work. If using Flash, a storyboard can be used to show how it will transition. – Flowchart: Can be used to show the procedure that users need to complete to create a data visualisation.

4. See page 204 of your textbooks for an example of a layout diagram and story board.

5. Characteristics of users Need to consider gender, special needs, culture, age, education level, status and location of audience (this was covered in the previous area of study). When creating a data visualisation, you need to keep the following in mind: – Age: Must be age appropriate. Pictographs are often engaging for younger children. – Special needs: Axis labels need to be clear, as does the difference in colours/images so those with a visual impairment aren’t excluded. Can also include audio and if using animation, ensure it moves slowly. – Culture: May use images and icons instead of just writing so the information is universal.

6. Evaluation criteria and techniques Evaluation is an important part of the PSM. Allows us to check whether our solution has met the needs of the users. We can evaluate our data visualisations based on two key areas: efficiency & effectiveness.

7. Evaluating the efficiency of a data visualisation Efficiency deals with quantitative data that can be easily measured. – Time: Does the data visualisation save us time is processing the information? – Cost: Has it decreased the cost associated with processing the data (can be labour or equipment costs)? – Effort: Can further data sets be added at a later time with minimal effort?

8. Evaluating the effectiveness of data visualisations Effectiveness deals with qualitative data. – Quality: Has the data visualisation provided users with the means to understand the data quickly? – Relevancy: Is the relevant information easy to find, or does the user have to search for it? – Timeliness: Is it communicated in a timely manner? Is the data current or old? – Accuracy: Is it accurate? Can we compare a data set with the visualisation to ensure it’s accurate? – Clarity: Does it provide ambiguous information which could potentially lead to misinterpretation?

9. Using problem statements to evaluate data visualisations When the problem statement is clearly defined, this can guide how we evaluate the solution. – Does the data visualisation improve the quality by providing information that is easier to understand? – Is the visualisation more relevant to the task? – Is the visualisation timely?

10. Questions – Create a flowchart that shows the procedure for creating a bar chart. – Identify a demographic that may benefit from a data visualisation rather than a spreadsheet. Propose a situation and what type of data visualisation would be presented. – How might a design assist you in clarifying the purpose of a data visualisation? – Explain how age might impact the design of a data visualisation. – Provide an example of how culture might change the form of data visualisation. – Identify a special need that might change the format of a data visualisation.

11. Provide an example of how you might evaluate a data visualisation to demonstrate efficiency. Create three questions that would test a data visualisation’s clarity. How can we test for accuracy when creating a data visualisation?