Users’ Cognitive Load: A Key Aspect to Successfully Communicate Visual Climate Information

Abstract

The visual communication of climate information is one of the cornerstones of climate services. It often requires the translation of multidimensional data to visual channels by combining colors, distances, angles, and glyph sizes. However, visualizations including too many layers of complexity can hinder decision-making processes by limiting the cognitive capacity of users, therefore affecting their attention, recognition, and working memory. Methodologies grounded on the fields of user-centered design, user interaction and cognitive psychology, which are based on the needs of the users, have a lot to contribute to the climate data visualization field. Here, we apply these methodologies to the redesign of an existing climate service tool tailored to the wind energy sector. We quantify the effect of the redesign on the users’ experience performing typical daily tasks, using both quantitative and qualitative indicators that include response time, success ratios, eye-tracking measures, user perceived effort and comments among others. Changes in the visual encoding of uncertainty and the use of interactive elements in the redesigned tool reduced the users’ response time by half, significantly improved success ratios, and eased decision making by filtering non-relevant information. Our results show that the application of user-centered design, interaction, and cognitive aspects to the design of climate information visualizations reduces the cognitive load of users during tasks performance, thus improving user experience. These aspects are key to successfully communicating climate information in a clearer and more accessible way, making it more understandable for both technical and non-technical audiences.

How decadal predictions entered the climate services arena: an example from the agriculture sector

“…Users demand simple and intuitive visualisations, especially if the objective is that anyone (i.e., different user profiles in the agriculture sector, from farmers to field technicians, and from large food companies to food security policy makers) can understand and use the prediction ( Calvo et al, 2022 , Terrado et al, 2021 ). Also, different users from the agriculture sector require varied levels of information to be presented based on their needs.…”

Towards more effective visualisations in climate services: good practices and recommendations

“…In climate science, psychology methods have been applied to make information more accessible to expert and non-expert audiences (Harold et al 2016) and to improve the efficiency of users during the performance of particular tasks (Hegarty et al 2010;Calvo et al 2021). The memorability aspect has been commonly used to communicate climate information to the general public (e.g.…”

“…Once the best option has been selected, it is important to co-evaluate the visualisation through quantitative and qualitative methods that go beyond merely asking users about their views on the service (Sharp et al 2007;Calvo et al 2021). Examples of quantitative metrics for user testing include the time used and the number of success, failure and abandonment rates when completing a particular task.…”

“…When the visualisation of climate data is aimed at various audiences, basic visualisation options with interaction mechanisms may not suffice. Allowing visualisations to be tailored to reflect individual preferences, such as variables' preferred display thresholds, modification of visual properties like the dark/light mode or selection of a particular location, provide a high level of flexibility to the visualisation (Meijie et al 2020;Calvo et al 2021). Figure 1 shows some examples of customisation options, including the user selection of thresholds (e.g.…”

Image muting of mixed precipitation to improve identification of regions of heavy snow in radar data

“…This visualization does not remove areas of melting but rather "mutes" them, making the regions stand out less than the snow-only or rain-only portions of the storm. Work by Calvo et al (2021) demonstrates how making small changes in climate visualizations can reduce the cognitive load and support analysis and potential decision making.…”