This report will consider a fictitious scenario and suggest suitable approaches to tackle several questions that have been posed in relation to the scenario. By employing current theory around data analysis, data visualisations, and visualisation design processes (Roberts, Headland, & Ritsos, 2016), several approaches will be considered to show how best to solve the problem.
It is January 2014, and GASTech, a large energy company on the island of Kronos, is hosting a party to celebrate the success of the company. Although a popular company with the local governmen and financial institutes, GASTech has been unpopular due to its apparent lack of regard for environmental responsibilities.
During the party, several the GASTech employees have gone missing. It is suspected that they have been kidnapped by an organisation known as the Protectors of Kronos (POK).
The task is to assist law enforcement in trying to understand the circumstances in which the
GASTech employees have gone missing, using the following available information;
A map of Kronos
A chart describing the local GASTech organization
A spreadsheet of GASTech employee records
Email headers from two weeks of internal GASTech company email
Resumes and short biographies of many, but not all, of the GASTech employees
Historical reports and descriptions of the countries involved
Relevant current and historical news reports from multiple domestic and translated foreign sources, in text file format.
The key questions that require answering include developing an understanding of the POK organisation, how it has developed over time and who its main members are. It is neessary to develop any information that links GASTech or its employees to the POK; and what, if any, motivation POK has for the kidnap of the missing employees. Finally, characterising the events prior to, and during, the GASTech party will assist the authorities in tracing the missing persons.
It is important that in any situation where data requires analysis there is an understanding of the task, and the terms of reference for that task (Project Management Tips, 2013). In the case of this exercise, we are being asked to answer two specific problems;
Present a summary of POK members over time
Making use of a visual analytics as a problem solving too will assist in discovering relationships in the available data, determine hypothesis based on the data and validate these hypotheses. As the proliferation of data is ever increasing, the use of computer-based analysis and visualisation systems are required to assist in providing visual representations of the data in an efficient and effective way (Hansen & Johnson, 2005). These tools not only assist the analyst or developer, but also those who ultimately need to examine the results of the analysis.
The ‘visual’ element of visual analytics is essential. The human brain can assimilate vast amounts of information via our sense of smell, sight, touch, hearing and taste. However, information which is taken in through our eyes is the quickest and allows for much more information to be taken in (Taylor, 2014). Our sight is the main channel to our brains.
Good data visualisation needs to have visually stimulating and cohesive design that is relevant to the person viewing it. It must have the right context for the circumstances for which it has been designed (Balliet, 2011). Furthermore, a good visualisation should be designed so that all viewers can reach the same conclusions based on what is shown.
Having asked what is to be shown (our data and the information extracted from it), and why our user needs to look at it (the task at hand and the motivation behind it), it is now relevant to ask ‘How’ the data is to be shown (Munzner, 2014).
Considering the principles of visual analytics and data visualisation, determining the best way to present the data we have, by choosing the correct design space idiom, is fundamental. In this context, an idiom refers to the distinct approach that will be taken to create and manipulate the visualisation (Cham, 2014). The number of possibilities when considering the design is huge, and made even bigger when considering the option for user interaction.
Depending on the data available, a design can be static, for example a simple bar chart or scatter plot. However, by highlighting a single plot, or clicking on a set of attributes can potentially link to different type of visualisation, providing a new point of view of the data.
A major challenge is ensuring that the design is suitable for the task, and is also effective. A design for one set of circumstance may work extremely well, but replicating its principles for another task may render the design unusable (Munzner, 2014).
In our scenario, when considering the data available, it is important to ask ‘what’ can be visualised. For example, to answer the first problem, we must quantify certain attributes (names of POK members mention in news articles) based on the number of times they appear in the data, over a time range determined by the date of the article. The most appropriate idiom would be that of the streamgraph, which can suitably visualise one category attribute, and one quantitate attribute over time. It is also ideal for handling hundreds of time and category attributes.
It would also be possible to achieve the same results using a stacked bar chart. However, it has been shown that the general flow and fluidity of the stream graph is visually pleasing. And, even though stream graphs may be more difficult to interpret overall, comparison of individual layers is potentially easier to comprehend (Byron & Wattenberg, 2008).
In the first instance, initial brainstorming around this scenario and the potential ways to approach the problem was completed, documented and presented (Appendix A). This represented the first stage of the design process.
Now, having considered the various design idioms, it is decided that the first scenario question will be best addressed by making use of the data from the various news articles and historical reports.
By analysing the content of these various documents, it is possible to identify frequent terms and words using basic word clouds. By removing words that are less relevant to our objective, it is possible to identify key words and names that are linked to POK. Below are examples of potential word cloud development.
This form of topic modelling (Brett, 2012) used in conjunction with a stream graph to show the use of particular words in the articles at specific points in time, would make it possible to identify which POK members were actively part of the organisation at a given time.
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