Visual and Cognitive Processing, Data Exploration and View Manipulation

generation of a visualisation that effectively shifts the analyst’s cognitive load onto the visual systems of the brain as they are more suited to perform pattern recognition tasks. In this final step of the visualisation process the Information Analyst must use the visualisation produced to attempt to answer the research question. When the visualisation is initially presented it is subject to visual and cognitive processing where the analyst attempts to identify interesting patterns within the data and attach meaning to them. A well designed visualisation will have shifted the majority of the pattern recognition tasks to the analyst’s visual systems (which are optimised for this task). Determining potential causes for the identified patterns will be the responsibility of the analyst’s cognitive system.

The process by which the analyst uses a visualisation to discover / reveal information about the research question was summed up by Ben Shneiderman when he developed the information seeking mantra “Overview first, zoom and filter, then details on demand” (Shneiderman, 1996). This mantra describes both how data should be presented and how the information analyst will interact with the visualisation. Assuming that it is an interactive visualisation where interaction is possible. In total Shneiderman identifies seven “Task- domain information actions” that visualisation users may perform. In summary these tasks are:

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i. Overview Gain an overview of the entire data collection ii. Zoom Zoom in on items of interest

iii. Filter Filter out uninteresting items

iv. Details-on-Demand Select an item or group and get details when needed v. Relate: View the relationships between items

vi. History Keep a history of actions to support undo, replay and progressive refinement.

vii. Extract Allow extraction of sub-collections and of query parameters. The mantra also serves as a description of the final part of Ware’s visualisation process (Figure 2-12) in which the analyst uses the visual and cognitive system to explore the data presented, identify patterns and features and then modify the presented visualisation to extract greater detail about interesting features.

2.3.4.1 Examining the Information Seeking Mantra

Each component of the Information Seeking Mantra serves to facilitate the analysts quest to answer their research question and is worthy of independent examination. An insightful analysis of the mantra is provided by Craft & Cairns who identified what can be learnt from the mantra (Craft & Cairns, 2005). Their findings are summarised by mantra section below:

2.3.4.2 Overview

The presentation of a complete overview of the dataset is of primary importance as it provides context for all the stages that follow. Many patterns and themes within a dataset can be see only in the context of the entire dataset and therefore this forms the first step of the analysts ‘visual thinking’ in examining the patterns within a dataset. The primary goal is to identify features that are considered ‘interesting’ within the context of the research question being asked. The recognition of the ‘interesting’ patterns (mostly by the analysts visual cortex) and the selection of those relevant to the research question forms the basis for the next step of the visual information seeking process by selecting candidates to be retained or eliminated in the zoom & filter step.

2.3.4.3 Zoom and Filter

Both zooming and filtering serve the same purpose specifically “reducing the complexity of the data representation by removing extraneous information from view and allowing for further data organization” (Craft & Cairns, 2005). The difference between the two is subtle and is further complicated by the fact that zooming is itself usually divided into two possible actions – Zooming-in and Zooming-out. At the highest level the distinction between zooming and filtering can be stated as:

i. Zooming – adjustment, by the user, of the size and position of data elements. Zooming may be regarded as “filtering by navigation and change of representational vantage point” (Craft & Cairns, 2005).

a. Zooming-in: removes extraneous information from the visual field. This in turn allows the cognitive centres to further organise the information into patterns to inform further interpretation and decision making.

b. Zooming-out: reveals hidden information – usually contextual information that is already know but cannot be recalled. Essentially the user is rediscovering

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his location within the information space and integrating the detailed information revealed by the previous ‘zoom-in’ with their overall mental model. ii. Filtering – Reducing the complexity of the display by removing extraneous

information without changing the data representation or the user’s view of it.

In either case both Shneiderman and Craft note that the visualisations responsiveness to the user’s interaction must be swift or its usefulness as an aid to cognition will be impaired.

2.3.4.4 Details on Demand

A typical information visualisation will contain many data points with the count ranging from tens to millions of points. Almost immediately it becomes impossible, given limited screen space, to display supplemental data on all these points. The mantra advocates a “details on demand” approach where supplemental data is provided on a data point by data point basis at the user’s request. The ‘request’ should be a simple action such as a mouse-over or selection of a data point that does not change the representational context in which the user is viewing the data.

2.3.4.5 Relate

An interactive visualisation should support its user in identifying and viewing the relationships that may exist between data points. Usually this is implemented as a change in viewpoint when the user makes a selection of a particular data item; the new viewpoint should present related items to the selected data point by degree of similarity. Of course what constitutes an appropriate measure of similarity will depend on the data being visualised and on any measure that might be calculated or made available to the visualisation engine during the data transformation step of the visualisation process.

2.3.4.6 History

Maintaining a record of the user’s actions as they explore the dataset and providing a facility to rapidly undo or redo actions is a key part of the user’s interaction with the visualisation. This accepts that the user is performing an ‘exploration’ of the dataset and that due to the exploratory nature it is possible for the user to need to restore the visualisation to a previous state. It may be that some user action that might have yielded a useful result does not, after all, achieve its goal. In this case a user will immediately wish to return to the previous state to continue the exploration of the dataset in a different direction. Equally the user may gain useful information from the action but still desire to return to the previous state as the contrast between the two states can itself provide further information. Finally of course the user may simply make an error and they should be able to rapidly recover from this.

2.3.4.7 Extract

An interactive visualisation frequently results in users performing a lengthy set of interactions to reveal the information needed to answer their research question. The information so revealed is often useful in many different tasks and it would be very labour intensive to re-create the interaction sequence every time the information was required for a task. Accordingly an interactive visualisation should provide the user with a means to extract and preserve the information exposed by their exploration of the dataset for use in other computer systems and projects.

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