Bridging Theory with Practice: An Exploratory Study of Visualization Use and Design for Climate Model Comparison

Abstract

Evaluation methodologies in visualization have mostly focused on how well the tools and techniques cater to the analytical needs of the user. While this is important in determining the effectiveness of the tools and advancing the state-of-the-art in visualization research, a key area that has mostly been overlooked is how well established visualization theories and principles are instantiated in practice. This is especially relevant when domain experts, and not visualization researchers, design visualizations for analysis of their data or for broader dissemination of scientific knowledge. There is very little research on exploring the synergistic capabilities of cross-domain collaboration between domain experts and visualization researchers. To fill this gap, in this paper we describe the results of an exploratory study of climate data visualizations conducted in tight collaboration with a pool of climate scientists. The study analyzes a large set of static climate data visualizations for identifying their shortcomings in terms of visualization design. The outcome of the study is a classification scheme that categorizes the design problems in the form of a descriptive taxonomy. The taxonomy is a first attempt for systematically categorizing the types, causes, and consequences of design problems in visualizations created by domain experts. We demonstrate the use of the taxonomy for a number of purposes, such as, improving the existing climate data visualizations, reflecting on the impact of the problems for enabling domain experts in designing better visualizations, and also learning about the gaps and opportunities for future visualization research. We demonstrate the applicability of our taxonomy through a number of examples and discuss the lessons learnt and implications of our findings.

Examining Implicit Discretization in Spectral Schemes

“…Despite the visualization community's promotion of more perceptually appropriate alternatives [BGP∗11, BRT95, Mor09, Tru81, LH92, Gre08, KRC02], rainbow color maps remain commonplace in a variety of scientific domains, including medicine [BGP∗11], atmospheric and climate sciences [QM16, DPW∗15], bioengineering [BTGM16], aerospace [KES13], and astronomy [ZDM∗15]. Although domain convention is often used to justify the inclusion of rainbow color map variants in visualization systems [WP13, QM16, PWB∗09], we still do not understand why experts continue to gravitate to spectral schemes.…”

“…Although domain convention is often used to justify the inclusion of rainbow color map variants in visualization systems [WP13, QM16, PWB∗09], we still do not understand why experts continue to gravitate to spectral schemes. Cited reasons include familiarity [BGP∗11, QM16], aesthetic preference [BGP∗11, Bre97, Mor16], and ease of use [BT07, Mor16], but evidence also suggest that rainbow color maps may be a satisficing design choice for specific types of tasks, such as locating and quantifying extreme values [DPW∗15, WTS∗17, WTB∗18, RNA18, War88, WTB∗18]. Improving our understanding of both how rainbow color maps are used and the ways in which they are ineffective could lead to improved guidance regarding effective color usage more broadly.…”

Data Visualization in Support of Executive Decision Making

“…A popular solution is data visualization. Data visualization is a methodically developed graphic which represents data in a manner that allows one to obtain insights, develop understanding, identify patterns, trends, or anomalies faster, and promote engaging discussions (Dasgupta et al, 2015). Data visualization has been widely used as a tool for aiding understanding of complex phenomena by using technology to integrate graphic creation with image understanding and enabling more efficient communication (Wang, 2015).…”

“…As visualization is a tool promoting understanding, it enhances the link between visualization and sensemaking (de Regt, 2014). In relation to big data, which adds another layer of complexity, data visualization is significant in presenting and communicating complex data intuitively by assembling and summarizing various forms and amounts of data for effective human information interpretation (Campbell, Chang, & Hosseinian-Far, 2015;Dasgupta et al, 2015;Gatto, 2015). Data visualization assists with sense-making by extrapolating meaning from complex datasets and uses the human visual system in order to create insight regarding conceptual information (R. E. Patterson et al, 2014;Reilly, 2013).…”

Reducing the Analytical Bottleneck for Domain Scientists: Lessons from a Climate Data Visualization Case Study

“…[1][2][3][4] There is inherent uncertainty and disagreement in the parameterization of these models and in understanding their effects on outputs. However, gauging consensus among model outputs is critical for achieving high accuracy about prediction of environmental events, climate change patterns, and so on.…”