Is It Scientific? Viewer Perceptions of Storm Surge Visualizations

16 Scientists and coastal and risk managers are using semi-realistic visualizations of storm17 surge connected to hydrodynamic models. These visualizations may make depictions of 18 forecast impacts more engaging and accessible. However, they do not fit within established 19 frameworks for visualizing risk because they add representational detail that exceeds current 20 guidance. This study explores how audiences regard these visualizations in relationship to 21 perceived representational norms. Survey respondents were asked about characteristics that 22 make a representation “scientific.” Results suggest that audiences may perceive semi-realistic 23 visualizations of real places as scientific providing some conventions are met. It demonstrates 24 that the persons and institutions behind the visualization may influence perceptions of 25 legitimacy more than the style of the visualization. Although this opens new representational 26 possibilities, it also may increase the potential of visualizations to be misleading and may foster 27 perceptions that scientists are engaged in advocacy. 28


Is It Scientific? Viewer Perceptions of Storm Surge Visualizations Is It Scientific? Viewer Perceptions of Storm Surge Visualizations
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This article is available at DigitalCommons@URI: https://digitalcommons.uri.edu/maf_facpubs/56 make a representation "scientific." Results suggest that audiences may perceive semi-realistic and Data Visualization. Landscape Visualization "represents actual places and on-the-ground 42 conditions in 3D perspective views, often with fairly high realism" (Sheppard and Salter 2004). 43 Data visualization is concerned with "the representation and presentation of data to facilitate 44 understanding." (Kirk 2016). Although this definition of data visualization does not directly 45 exclude the notion of representing landscapes, norms of data visualization exhibit a preference 46 for emphasizing essential aspects of the data (e.g., Harold et al. 2016; Tufte and Weise Moeller 47 1997). The level of detail and scenography included in many landscape visualizations 48 contradicts this norm. We thus define these visualizations as a hybrid for purposes of 49 exploration and discussion. 50 Landscape Data Visualizations (LDVs) are here defined as: realistic and semi-realistic 3D 51 visualizations of real places distinguished by their integration of numerical models (e.g., 52 ADvanced CIRCulation model) and 3D visualization platforms (e.g., game engines, custom 53 software) that make visualization outcomes a direct product of underlying modelling, such as 54 the implementation of fragility curves and damage functions (e.g., Spaulding et al. 2016 the concern that there may be a 'style penalty' for using advanced semi-realistic visualizations. 144 Exploring how these visualizations are understood, and the characteristics that make a 145 visualization appear to be "scientific" is thus highly relevant to any use of semi-realistic 3D included repeated phrases such as, "citation of data and sources". Initial groupings were based 213 on obvious similarities, taking care to discern when the intent of a phrase was altered by other 214 aspects of the text. From these groupings a set of four major themes was identified. To validate 215 the coding, codes were applied to a random subset of the data (n = 100) by an independent 216 coder. That coded sample was then compared and found to be 84% in agreement with the 217 coded data.

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Audiences seem willing to accept LDVs as being "scientific" (or representations of 397 science) providing that certain conventions are adhered to such as transparency of data 398 and sources, and that those sources are reputable. Scientists who employ LDVs as tools 399 to communicate their work should thus not presume that the visualization will be 400 regarded differently than other maps or representations they stand behind. providing certain conventions for disclosure and transparency are met. These shifting boundaries are 469 indicative of the larger set of value judgements that scientists and consumers of scientific graphics make 470 every day (Walsh 2017). LDVs surface these judgements because they include elements such as light and 471 shadow and perspective that are clearly extraneous to the underlying data but may nonetheless 472 contribute to the ergonomics of the presentation and the impression it creates. This forefronts a range 473 of decisions, color choice, emphasis, that can make even the simplest presentation of data into a 474 dramatic and iconic image (Schneider 2016). It is therefore understandable that the use of semi-realistic 475 visualizations would be roundly discouraged in the context of existing frameworks for risk 476 communication. 477 As it pertains to the presentation of data that is spatially relevant to specific audiences, 478 however, there is an at least reasonable case for the consideration of perspectival presentations and 479 inclusion of recognizable landmarks that make outcomes less abstract (Lewis and Sheppard 2006 Highly diagrammatic 3D visualizations, for instance, may have more in common with 2D data 499 visualizations than is currently acknowledged by the ways in which visualizations are 500 categorized. More testing with real audiences is thus warranted. 501 The development of semi-realistic and realistic visualizations in real-time connection with storm 502 models follows a larger pattern of technology driving representational decisions (Lovett et al. 2015;503 Sheppard and Cizek 2009). The desire to use the best and most advanced tools, even before the 504 evidence exists to support their use, is understandable given the desire of many scientists to connect to 505 audiences. Doing this blindly, however, risks distracting or misleading the public. The extent to which 506 reputation is a factor in assessments should give scientists pause, lest they undermine their own 507 credibility (O'Neill and Nicholson-Cole 2009). This study is a modest step to inverting the technology first 508 paradigm and better directing the development of these visualizations. 509 Tables   656   Table 1, themes identified in response to: What characteristics make a graphic or visualization