La exclusión social.

NEUVis can also communicate in a way that acknowledges different interpretations of common syntax. The use of artistic metaphor may be an example of how NEUVis can, in this way, act as a se- mantic boundary object. Recognising interpretive differences allows NEUVis to engage with sources of knowledge, and how they differ across the boundary of knowledge. As a semantic boundary ob- ject, NEUVis can be used tacit knowledge explicit. Finally, as a pragmatic boundary object, NEUVis can receive information from the audience. This can create a two-way flow of knowledge; though, this is is contrast to the goal of the field of science communication, as noted in 2.4 on page 52, which seeks to create a one-way system of communication. Using pragmatic boundary objects, NEUVis in- cludes a process for transforming the knowledge from the audience, back across the boundary to the primary researcher/creative practitioner collaboration. This implies some form of interactive method used, but is not exclusive to digital representation.

4.3 Reflection 2: A process model for visualisation design

Design is a flexible and adaptive practice; a distinct field from the fine arts, or the natural or social sciences, it is a “liberal art of technological culture” [21]. There are many ideas and methods that can be called ‘design’ and a single definition cannot adequately cover them all. By starting with a common design process, and comparing it to the process of creating an interactive visualisation, this research reveals a design process for NEUVis.

Tim Brown, CEO of renowned design firm IDEO, states that the design process goes through three stages: Inspiration, the context that motivates the designer to search for solutions to a problem, or opportunity; Ideation, generating, testing and evaluating ideas that may lead to a solution; Implementation, transitioning to an artefact ready for market[167]. This three-step design process is the foundation for the reflection on a NEUVis design process.

The first stage in the process revolves around user needs, the actual desires or goals of a potential user that can be satisfied by the design. Human-centred investigation informs the technological design. There are many tools for discovering user needs, such as interviews and user observation in context. The second stage involves the designer using the uncovered needs as a platform for developing the artefact. One interpretation of this process is:

1. Create: a problem is defined, based on the user needs. The designer is solving a problem as

they understand it, which satisfies the needs of the user. A tentative solution is proposed in response to this problem, which is developed into a testable prototype.

2. Critique: user testing with the potential audience or market for the design should be undertaken.

As well as this, heuristic evaluation against specific criteria (Nielsen’s usability heuristics [145], for example, see 2.2.1 on page 32) can provide important feedback. If the design satisfactorily solves the problem, and serves the user needs, it can be completed. If not the designer must reflect on how the user needs are not satisfied.

3. Constraints: this reflection allows the user to redefine the problem they are solving, in order to

Figure 4.2: A Design Process Model for NEUVis.

to begin a new iteration of the design, to leverage what has been learned from previous tentative solutions.

The final, implementation stage involves publishing or producing a satisfactory design. It also acts as a loop back into the beginning of the process, as the needs of the users change over time.

NEUVis fits into a similar framework; ideation still requires iteration over a cycle of creation, critique, and constraints. In response to the way that the message and implication of the data needs to be merged with the needs and context of the user Six Visualisation Questions that designers can use to merge the data and the user needs are proposed. This was formulated during an early invest- igation into the way that a designer can create an understanding of the data, and compose a unified message for their audience (see figure 4.2). The creation of this understanding is the defining dif- ference between NEUVis and a standard design process. These questions help the designer clarify the relationship between the message and implications of datasets with the needs and context of the audience. It should be noted that these questions are not speculative: they should be supported by user research.

4.3.1 Six Visualisation Questions

1. How does this new knowledge benefit the user? Addressing the needs and context of the user. This question prompts the designer to consider what practical outcome the new knowledge will give to the user. It is intended to help the designer empathise with their users.

4.3. REFLECTION 2: A PROCESS MODEL FOR VISUALISATION DESIGN 79 2. What about this data is relevant or important? Addressing the message of the data and the context of the user. The designer should identify the elements and implications of data that are necessary for visualisation. It is also important to note that large portions of datasets may not be interesting at all, and may be irrelevant to the user, such as on the map of the London Underground, by Harry Beck (see figure 2.5b and the discussion in 2.3.2 on page 45). Superfluous data should not be visualised, and a pitfall for poor design is to cram in unnecessary information, as much as it is to embellish visual construction so that the data seems more interesting. As Tufte suggests, the right numbers are never boring.

3. What is otherwise inaccessible to the user? Addressing the message of the data and the con- text of the user. This is how designers can leverage novelty to engage users, stimulating curiosity. However, new information is not essential for visualisation, sometimes new representations are just as interesting. Either way, it is important that the designer understand how their data is positioned in the understanding of their audience.

4. What can the user access for themselves? Addressing the message of the data and the context of the user. Allow the users to continue to interact with data on their own terms. If most of the data is accessible to users, it is worth considering how users can engage on different levels, and the influence this will have on the construction of a visualisation. If most of the data is difficult to access for the general public (such as scientific literature behind a paywall), what implications can be presented that call the user to action, or engage with the content beyond the visualisation. This relates closely to one of the value-driven goals presented in [186]: encourage insight and insightful questions about the data. These questions can be prompted by the visualisation, and the user can be given the opportunity to engage further and find out the information for themselves.

5. What myths and misconceptions are relevant to the data? As discussed in section §2.4, tradi- tional mass media shows such as Mythbusters and YouTube channels like Veritasium have built successful shows around addressing myths and misconceptions. But research has also suggested that this may have the opposite result, known as the backfire effect, and is discussed in [156]. The authors of this paper suggest that facts and myths presented together can become intertwined in the audience’s memory, leading to incorrect reinforcement of myths that are being addressed, so the communication should deal in facts, rather than in myths. If myths are unavoidable, they suggest prompting the user to form their own attitudes of the information. Ask questions such as “What is your opinion?” or “Here are the facts, make up your own mind!” [156].

6. What is the potential for impact, and what are the risks of this visualisation? Express the potential for impact of a user empowered with data, as well as the converse risks. This will allow the designer to reinforce information that promotes the benefits of impacts. It can also highlight information that should be clarified in order to negate risks, in particular, the risks associated with the misunderstanding of data. This final question was influenced by the ongoing research described in 6.4.2 on page 131.