Interpreting climate data visualisations to inform adaptation decisions

Daron, Joseph; Lorenz, Susanne; Wolski, Piotr; Blamey, Ross C.; Jack, Christopher

doi:10.1016/j.crm.2015.06.007

Cited by 44 publications

(37 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In higher-level decision tasks, visualization design also influences data interpretation and decision making [9,4]. People interpret climate data differently depending on whether the visualization presented percentile information versus showing the range [8]. In bar graphs depicting average values, people judge data values that fall within the bar as being more likely to be part of the data set than points outside the bar, despite being equidistant from the mean [30,6].…”

Section: Related Workmentioning

confidence: 99%

Illusion of Causality in Visualized Data

Xiong¹,

Shapiro

Hullman³

et al. 2020

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

When students eat breakfast very often (more than 4 times a week), their GPA is around 3.5; while when students eat breakfast not very often (less than 4 times a week), their GPA is around 3.0.Number of breakfasts eaten per week GPA Number of breakfasts eaten per week GPA Number of breakfasts eaten per week GPA When students eat breakfast very often (more than 4 times a week), their GPA is around 3.5; while when students eat breakfast not very often (less than 4 times a week), their GPA is around 3.0. Fig. 1. The same data showing the relation between eating breakfast and GPA presented via text, bar graph, line graph or scatter plot. Which depiction makes eating breakfast causing higher GPA seem more plausible to you?Abstract-Students who eat breakfast more frequently tend to have a higher grade point average. From this data, many people might confidently state that a before-school breakfast program would lead to higher grades. This is a reasoning error, because correlation does not necessarily indicate causation -X and Y can be correlated without one directly causing the other. While this error is pervasive, its prevalence might be amplified or mitigated by the way that the data is presented to a viewer. Across three crowdsourced experiments, we examined whether how simple data relations are presented would mitigate this reasoning error. The first experiment tested examples similar to the breakfast-GPA relation, varying in the plausibility of the causal link. We asked participants to rate their level of agreement that the relation was correlated, which they rated appropriately as high. However, participants also expressed high agreement with a causal interpretation of the data. Levels of support for the causal interpretation were not equally strong across visualization types: causality ratings were highest for text descriptions and bar graphs, but weaker for scatter plots. But is this effect driven by bar graphs aggregating data into two groups or by the visual encoding type? We isolated data aggregation versus visual encoding type and examined their individual effect on perceived causality. Overall, different visualization designs afford different cognitive reasoning affordances across the same data. High levels of data aggregation by graphs tend to be associated with higher perceived causality in data. Participants perceived line and dot visual encodings as more causal than bar encodings. Our results demonstrate how some visualization designs trigger stronger causal links while choosing others can help mitigate unwarranted perceptions of causality.

show abstract

Section: Related Workmentioning

confidence: 99%

Illusion of Causality in Visualized Data

Xiong¹,

Shapiro

Hullman³

et al. 2020

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

show abstract

“…With an increasing volume of climate data, online data portals are becoming an important mode of communication (Daron, Lorenz, Wolski, Blamey, & Jack, 2015). Institutes across the world host online portals to help users access relevant data and information; examples include the Climate System Analysis Group climate information platform, the Royal Netherlands Meteorological Institute (KNMI) climate explorer, the World Bank climate data portal, and the Potsdam Institute climate impacts platform.…”

Section: Introductionmentioning

confidence: 99%

The utility of weather and climate information for adaptation decision-making: current uses and future prospects in Africa and India

Singh

Daron

Bazaz

et al. 2017

Climate and Development

165

108

View full text Add to dashboard Cite

(2018) The utility of weather and climate information for adaptation decision-making: current uses and future prospects in Africa and India, Climate and Development, 10:5, 389-405, DOI: 10.1080/17565529.2017 Developing countries share many common challenges in addressing current and future climate risks. A key barrier to managing these risks is the limited availability of accessible, reliable and relevant weather and climate information. Despite continued investments in Earth System Modelling, and the growing provision of climate services across Africa and India, there often remains a mismatch between available information and what is needed to support on-the-ground decision-making. In this paper, we outline the range of currently available information and present examples from Africa and India to demonstrate the challenges in meeting information needs in different contexts. A review of literature supplemented by interviews with experts suggests that externally provided weather and climate information has an important role in building on local knowledge to shape understanding of climate risks and guide decision-making across scales. Moreover, case studies demonstrate that successful decision-making can be achieved with currently available information. However, these successful examples predominantly use daily, weekly and seasonal climate information for decision-making over short time horizons. Despite an increasing volume of global and regional climate model simulations, there are very few clear examples of long-term climate information being used to inform decisions at subnational scales. We argue that this is largely because the information produced and disseminated is often ill-suited to inform decision-making at the local scale, particularly for farmers, pastoralists and sub-national governments. Even decision-makers involved in long-term planning, such as national government officials, find it difficult to plan using decadal and multi-decadal climate projections because of issues around uncertainty, risk averseness and constraints in justifying funding allocations on prospective risks. Drawing on lessons learnt from recent successes and failures, a framework is proposed to help increase the utility and uptake of both current and future climate information across Africa and India.

show abstract

“…These strategies will help organizations such as National Met Services to effectively communicate about a changing climate.Sustainability 2020, 12, 2955 2 of 21 probabilistic climate projections about how the UK climate is expected to change over the course of the current century for the UK Climate Projections 2009 (UKCP09) and 2018 (UKCP18) platform [7,8]. These climate projections are simulations of how, for example, precipitation or temperatures might change in response to increasing concentrations of greenhouse gas emissions in the atmosphere, as well as how likely those changes are.However, climate projections may be challenging for stakeholders from public, private, and third-sector organizations and members of the general public [2,4,[9][10][11]. One potential difficulty in communicating climate projections is their inherent uncertainty [12].…”

mentioning

confidence: 99%

“…However, climate projections may be challenging for stakeholders from public, private, and third-sector organizations and members of the general public [2,4,[9][10][11]. One potential difficulty in communicating climate projections is their inherent uncertainty [12].…”

mentioning

confidence: 99%

Visualizations of Projected Rainfall Change in the United Kingdom: An Interview Study about User Perceptions

Kause

Bruin²,

Fung

et al. 2020

Sustainability

View full text Add to dashboard Cite

Stakeholders from public, private, and third sectors need to adapt to a changing climate. Communications about climate may be challenging, especially for audiences with limited climate expertise. Here, we study how such audience members perceive visualizations about projected future rainfall. In semi-structured interviews, we presented 24 participants from climate-conscious organizations across the UK with three prototypical visualizations about projected future rainfall, adopted from the probabilistic United Kingdom Climate Projections: (1) Maps displaying a central estimate and confidence intervals, (2) a line graph and boxplots displaying change over time and associated confidence intervals, and (3) a probability density function for distributions of rainfall change. We analyzed participants' responses using "Thematic Analysis". In our analysis, we identified features that facilitated understanding-such as colors, simple captions, and comparisons between different emission scenarios-and barriers that hindered understanding, such as unfamiliar acronyms and terminology, confusing usage of probabilistic estimates, and expressions of relative change in percentages. We integrate these findings with the interdisciplinary risk communication literature and suggest content-related and editorial strategies for effectively designing visualizations about uncertain climate projections for audiences with limited climate expertise. These strategies will help organizations such as National Met Services to effectively communicate about a changing climate.Sustainability 2020, 12, 2955 2 of 21 probabilistic climate projections about how the UK climate is expected to change over the course of the current century for the UK Climate Projections 2009 (UKCP09) and 2018 (UKCP18) platform [7,8]. These climate projections are simulations of how, for example, precipitation or temperatures might change in response to increasing concentrations of greenhouse gas emissions in the atmosphere, as well as how likely those changes are.However, climate projections may be challenging for stakeholders from public, private, and third-sector organizations and members of the general public [2,4,[9][10][11]. One potential difficulty in communicating climate projections is their inherent uncertainty [12]. This uncertainty arises from different sources, including natural variability in climate data resulting in imprecision, and uncertainty about how future human activities will impact on the climate, represented through different emission scenarios, or model uncertainty [13][14][15][16][17][18]. Thus, audiences' interpretations of climate projections and associated uncertainties may differ from what climate experts aim to communicate [19,20]. In addition, presenting uncertainty in climate information may delay public action on climate change [21], promote a "wait-and-see" approach [22], or increase public polarization about climate change [23]. This calls for communication formats that allow stakeholders to make adequate adaptation decisions and to integ...

show abstract

Interpreting climate data visualisations to inform adaptation decisions

Cited by 44 publications

References 33 publications

Illusion of Causality in Visualized Data

Illusion of Causality in Visualized Data

The utility of weather and climate information for adaptation decision-making: current uses and future prospects in Africa and India

Visualizations of Projected Rainfall Change in the United Kingdom: An Interview Study about User Perceptions

Contact Info

Product

Resources

About