Tailoring and evaluating visual communication

Although the field of visually communicating climate change is growing, there has to date been comparatively little empirical research on the communicative effectiveness of such visualisations (Lieske, 2012, Moser, 2010, Stephens et al., 2012). However, whilst climate visualisations might be a fairly recent phenomenon, the assessment of visuals and graphical representations more broadly has been

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widely researched in the fields of health and cognitive sciences (Ancker et al., 2006, Elting et al., 1999, Parrott et al., 2005), risk (Ibrekk and Morgan, 1987), design (Quispel and Maes, 2014) and computing (Kelleher and Wagener, 2011, Sanyal et al., 2009). More closely related to adaptation research, lessons can also be learned from research on environmental hazards and geosciences (Bostrom et al., 2008, Broad et al., 2007, Gahegan, 1999), and hydrology (Gimesi, 2009, Pappenberger et al., 2013). Insights from these past studies can help grow our understanding as to how to comprehensively assess and evaluate climate visualisations as well.

It is argued that graphical communication involves a process of encoding and decoding of information and that ‘[a] graphical method is successful only if the decoding is effective’ (Cleveland and McGill, 1985: 828). Whilst there has been some discussion as to which variables should be utilised in the assessment of this effectiveness, it is recognised that a combined understanding of user comprehension, preferences and the ability to support user needs is required (Ancker et al., 2006, Bostrom et al., 2008, Hawley et al., 2008, Lipkus and Hollands, 1999, Spiegelhalter et al., 2011).

Numerous studies, particularly in the health sciences, have focused on the analysis of the perception and comprehension of different types of commonly used graphs such as pie charts, risk ladders, icon arrays, bar graphs, line graphs and less commonly used formats such as so-called ‘spark plugs’ or ‘magnifying glasses’ (Garcia-Retamero and Galesic, 2010, Garcia-Retamero and Dhami, 2011, Shah and Freedman, 2011, Spence and Lewandowsky, 1991, Stengel et al., 2008, Wong et al., 2012). These studies have found that a number of factors such as respondents’ level of numeracy (Garcia-Retamero and Galesic, 2010) and graphical literacy (Garcia-Retamero and Galesic, 2010, Shah and Freedman, 2011), familiarity with the graph format used (Lipkus and Hollands, 1999, Roth, 2002, Shah and Freedman, 2011) and with the topic communicated (Shah and Freedman, 2011) all affect respondents’ level of comprehension. User comprehension of visualisations has also been studied in relation to communicating natural hazards. Broad et al. (2007) in their study of the hurricane cone as communicated by the National Hurricane Center in the US, find that the public do not understand the uncertainty communicated through the cone and highlight common misconceptions, such as the presumed safety from the hazard for locations outside the outer boundaries of the cone or the overemphasis of focusing on the black line marking the assumed hurricane path. In another study examining the use of a coastal flooding risk tool in the US, Wong-Parodi et al. (2014) explored an evaluation procedure for the

usability of climate change impact decision aids in terms of how they affect users’ understanding of their situation defined in terms of their knowledge, consistency of preferences and active mastery of the material. They suggest that if designers of decision-aids were to apply these three measures consistently to the development of their visualisation tools, communications of intended aims would be more focused and explicit (Wong-Parodi et al., 2014).

The previously highlighted research has predominantly focused on understanding the comprehension and preferences of the public and only few studies have specifically focused on expert or practitioner audiences. A couple of studies with experts in both hazard mapping (Kunz et al., 2011) and flood forecasting (Pappenberger et al., 2013) highlight that there is no real consensus amongst specific target audiences as to visualisation preferences. Furthermore, specifically in relation to climate forecasts, a study by Davis et al. (2015) evaluated and compared a variety of different probabilistic forecasts from a range of forecasting centres and, through expert interviews, found that users perceived the forecast visualisations as difficult to interpret. The study however, does not specifically state how expert comprehension was assessed or evaluated, making it difficult to extract useful guidance for repeatable assessments for other visualisation tools. Daron et al. (2015), in their study with the African vulnerability and adaptation practitioner community, have taken a more quantitative approach to directly comparing user comprehension, likelihood assessment and preference for different visualisations of climate projections and found that users extract different messages from the same visualisation and that expressed preferences for visualisations are associated to user confidence and their comprehension of those visualisations.

Whilst it can be seen that previous research in this field is very varied, a number of overarching messages emerge from the different studies. Firstly, we cannot assume that one single image, graphical format or visualisation will be unanimously and uniformly interpreted and consequently it is not achievable to pick a single one that will be effective across target audiences or even within a specific target audience (Bostrom et al., 2008, Lipkus and Hollands, 1999, Nicholson-Cole, 2005). To increase effectiveness, visual communications should therefore be evaluated and tailored to specific individual or audience settings, perceptions, and needs (Broad et al., 2007, Hawley et al., 2008, Hess et al., 2011, McInerny et al., 2014, Stephens et al., 2012). Consequently, such tailoring refers to ‘any number of methods for creating communications individualized for their receivers’ (Hawkins et al., 2008: 1). Tailoring, however, cannot be conceptualised in a vacuum and requires

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systematic empirical testing and evaluation (Broad et al., 2007, Lipkus and Hollands, 1999, Wong-Parodi et al., 2014). The recognition by a number of key scholars in this field (Bostrom et al., 2008, Fischhoff, 2011, Lipkus, 2007, Pidgeon and Fischhoff, 2011, Spiegelhalter et al., 2011) that such testing and evaluation has only rarely been done to date provides the motivation for further empirical testing as suggested in this thesis.

1.2.3.4 Justification for this thesis

The review of the literature in this section has highlighted a number of key demands for further research. Firstly, it is evident that the emphasis in much of the research predominantly rests on examining the understanding and views of the public as the principal ‘audience’ of climate change communication. As outlined in Section 1.2.1, much of the implementation of adaptation planning will happen at the local scale and in a review on communicating adaptation specifically, local practitioners have been identified as a key target audience for communication (Moser, 2014). Moser (2014) suggests that a better translation and tailoring of climate information to such local practitioners would support an easier application of this information to the implementation of adaptation actions. However, detailed research on providing tailored visual communication to this target group is not available to date. This links to the second key finding from the review of the research, namely that there is a clear demand for more empirical research in this field to identify more systematic and structured processes for the assessment and evaluation of the effectiveness of climate visualisations. This thesis therefore aims to contribute to this field by gaining a better understanding of local adaptation practitioners as a target audience and by responding to the call for more empirical research in the field of tailoring of climate visualisations.