Environmental science is an applied discipline, which therefore requires interacting with actors outside of the scientific community. Visualisations are increasingly seen as powerful tools to engage users with unfamiliar and complex subject matter. Despite recent research advances, scientists are yet to fully harness the potential of visualisation when interacting with non-scientists. To address this issue, we review the main principles of visualisation, discuss specific graphical challenges for environmental science and highlight some best practice from non-professional contexts. We provide a design framework to enhance the communication and application of scientific information within professional contexts. These guidelines can help scientists incorporate effective visualisations within improved dissemination and knowledge exchange platforms. We conclude that the uptake of science within environmental decision-making requires a highly iterative and collaborative design approach towards the development of tailored visualisations. This enables users to not only generate actionable understanding but also explore information on their own terms. Effective visualisations can engage non-scientists with unfamiliar and complex subject matter.We review the main principles of visualisation and specific graphical challenges for environmental science.We provide a design framework to help scientists develop effective visualisations within nonscientific, professional contexts.The uptake of science within environmental decision-making requires a highly iterative and collaborative design approach.

Environmental data visualisation for non-scientific contexts

Throughout history, storytelling has been an effective way of conveying information and knowledge. In the field of visualization, storytelling is rapidly gaining momentum and evolving cutting-edge techniques that enhance understanding. Many communities have commented on the importance of storytelling in data visualization. Storytellers tend to be integrating complex visualizations into their narratives in growing numbers. In this paper, we present a survey of storytelling literature in visualization and present an overview of the common and important elements in storytelling visualization. We also describe the challenges in this field as well as a novel classification of the literature on storytelling in visualization. Our classification scheme highlights the open and unsolved problems in this field as well as the more mature storytelling sub-fields. The benefits offer a concise overview and a starting point into this rapidly evolving research trend and provide a deeper understanding of this topic.

Storytelling and Visualization: An Extended Survey

The primary goal of visual data exploration tools is to enable the discovery of new insights. To justify and reproduce insights, the discovery process needs to be documented and communicated. A common approach to documenting and presenting findings is to capture visualizations as images or videos. Images, however, are insufficient for telling the story of a visual discovery, as they lack full provenance information and context. Videos are difficult to produce and edit, particularly due to the non-linear nature of the exploratory process. Most importantly, however, neither approach provides the opportunity to return to any point in the exploration in order to review the state of the visualization in detail or to conduct additional analyses. In this paper we present CLUE (Capture, Label, Understand, Explain), a model that tightly integrates data exploration and presentation of discoveries. Based on provenance data captured during the exploration process, users can extract key steps, add annotations, and author "Vistories", visual stories based on the history of the exploration. These Vistories can be shared for others to view, but also to retrace and extend the original analysis. We discuss how the CLUE approach can be integrated into visualization tools and provide a prototype implementation. Finally, we demonstrate the general applicability of the model in two usage scenarios: a Gapminder-inspired visualization to explore public health data and an example from molecular biology that illustrates how Vistories could be used in scientific journals.

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From Visual Exploration to Storytelling and Back Again

Interaction is fundamental to data visualization, but what “interaction” means in the context of visualization is ambiguous and confusing. We argue that this confusion is due to a lack of consensual definition. To tackle this problem, we start by synthesizing an inclusive view of interaction in the visualization community – including insights from information visualization, visual analytics and scientific visualization, as well as the input of both senior and junior visualization researchers. Once this view takes shape, we look at how interaction is defined in the field of human-computer interaction (HCI). By extracting commonalities and differences between the views of interaction in visualization and in HCI, we synthesize a definition of interaction for visualization . Our definition is meant to be a thinking tool and inspire novel and bolder interaction design practices. We hope that by better understanding what interaction in visualization is and what it can be, we will enrich the quality of interaction in visualization systems and empower those who use them.

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What is Interaction for Data Visualization

The benefits of storytelling's are long-known and its potential to simplify concepts, create emotional connection, and capacity to help retain information has been explored in different areas, such as journalism, education, and others. The necessity to incorporate storytelling in visualizations arises from the need to share complex data in a way that is engaging. Advances in technology have enabled us to go beyond the traditional forms of storytelling and representing data, giving us more attractive and sophisticated means to tell stories.In this paper, we present the results of a focus group study that was conducted with the purpose of collecting information on the narrative elements in a collection of visualizations and the possible inclusion of storytelling elements in those. In this study information about the visualizations in terms of comprehension, navigation, and likability was also collected with the intent of identifying elements that are appealing in the visualizations. Furthermore, we suggest strategies for storytelling in visualizations.

How to Tell Stories Using Visualization

Stories have long been used to convey information, cultural values, and experiences. Narratives not only have been the main way people make sense of the world, but also have been the easiest way humans found out to share complex information. However, today we are confronted with the problem of the amount of information available, which sometimes is hard to cope with. Combining storytelling with visualization has been pointed out as an efficient method to represent and make sense of data, at the same time allowing people to relate with the information. In this paper, we explore the benefits of adding storytelling to visualizations. Drawing on case studies from news media to visualization research websites, we identified possible strategies to introduce storytelling in visualizations such as adding short stories or narrative elements using annotations and using time to introduce the feeling of storytelling or story-flow.

Narrative Visualization: A Case Study of How to Incorporate Narrative Elements in Existing Visualizations

Over the past few years the web has been responsible for the rise in popularity of visualizations and it seems that interactive or playable visualizations have become more popular and end up standing out more. The use of interactivity and animation has been extensively discussed in information visualization research, but there has been some controversy in relation to its benefits. Additionally, there is still little empirical evidence about its efficacy in terms of improving understanding of the data and there is few research that points out guidelines of how to incorporate it successfully and that proves that playable visualizations are indeed more enjoyable and popular among users. In order to guide future research on the actual benefits of interactivity in visualization it is important to understand what types of interactivity are currently being used in the field and to have a framework to help discuss and evaluate interaction techniques. After conducting an extensive review of popular visualizations and their interactive capabilities, we propose eleven categories of interaction techniques: filtering, selecting, abstract/elaborate, overview and explore, connect/relate, history, extraction of features, reconfigure, encode, participation/collaboration, and gamification.

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Towards the Understanding of Interaction in Information Visualization

The need to visualize data has emerged from the research field, it has been a useful tool to the study of scientific problems. However the truth is that data visualization is a great way to present data for any area dealing with information, because visually presented information is not only more appealing due to its use of pictograms and colours, but also more efficient in conveying large amounts of information. Throughout the years there have been efforts to develop a classification for these visualizations, in order to provide a better understanding of this way to present data. There are many different classifications but none of them is fully complete. In this paper it is discussed and developed a typology for online data visualization and info graphics. Such a typology will be relevant for a better understanding of what kinds of visualizations exist and in further research to better identify which elements compose a good visualization that is pleasing to the public.

A Typology for Data Visualization on the Web

Creating a visualization that conveys a narrative requires choosing the dimensions and features that help tell the story. Time and space are two of these storytelling attributes which are commonly present in the story's structure. Thus, these should be considered in the creation process. Narrative Visualization is still a new field in Information Visualization research, and while there are guidelines for designing visualizations, specific ones for this new area are still lacking. Therefore, supported by previous research on broad recommendations for designing visualizations, we propose a specific set of guidelines to structure effective visual narratives divided into four decision categories: Intent, Spatio-temporal, Interaction, and Narrative Elements.

Ana Figueiras

Papers

How to Tell Stories Using Visualization

Narrative Visualization: A Case Study of How to Incorporate Narrative Elements in Existing Visualizations

Towards the Understanding of Interaction in Information Visualization

A Typology for Data Visualization on the Web

Once Upon a Time in a Land Far Away: Guidelines for Spatio-Temporal Narrative Visualization