To many of us scientists, visuals are key to many of our research activities. We use them to structure our thoughts during initial brainstorming sessions, to share and discuss ideas during moments of fruitful exchange with colleagues, or to identify relevant trends and patterns in our preliminary results. And probably most common, we use them to share and communicate our results with our target audience.

Visuals are particularly valuable and powerful to portray knowledge of complex, intangible, and abstract research subjects. They have a huge potential for sustainability scientists, which often deal with complex multi-scalar and multi-actor interactions that link socio-ecological systems. Indeed, whenever I am to present my research on global sustainability challenges to a fellow scientist or a non-scientific audience, I readily tend to rely on visuals to share my ideas and findings in a more accessible and memorable way. Yet, while I am developing those visualizations, I often find myself facing a number of challenging decisions: How can I best portray complex real-world phenomena while avoiding both an oversimplification of the subject matter and an overloading of the visualization? Which elements of the complex socio-ecological interactions studied do I want to give visual attention, which ones less? How can I best visualize connectivity across people, places, or scales?

In a recent study, some colleagues and I further explored these challenges. To this end, we systematically reviewed existing visualizations of telecoupling phenomena. The concept of ‘telecoupling’ links to an emerging field of research that deals with distant linkages between social-ecological systems in an interconnected world. It covers a wide range of topics (e.g. commodity trade, species migration, or conservation), bridging scientific efforts from various disciplines. Visuals are thereby commonly used. We assessed telecoupling visualizations from scientific literature in terms of their content and the adopted visualization approaches. Drawing on insights from data visualization and social network analysis, we then identified seven main visualization types and provided recommendations for improving current visualization practices.

Reflecting back at the initial challenges that led us to set out on this journey, below some of the key lessons learned:

  • Visualizations are powerful communication tools that can help to bridge disciplines and to engage with non-scientific audiences. It is thus well worth it if we spend a fair share of our efforts and time on them.
  • Visualizations are simplifications of a complex reality. We need to carefully select the contents that they represent (or leave out), and be aware of potential biases that our design choices may introduce.
  • Visualizations are representations of our mental models of the phenomena that we are investigating. The visualization design process can help us to become aware and critically reflect on these mental models.
  • The integration of multiple perspectives is a key challenge for telecoupling visualizations. We need to carefully choose which perspective(s) our visualization should represent.
  • Visual encoding, the process of assigning visual properties to data values (using marks and attributes), can help us to reflect more explicitly about the desired representation of the different visualization contents.
  • A huge diversity of visualization techniques exists – let’s get inspired, and then carefully select those techniques and designs that are most effective in bringing our message across. Insights from the field of data visualization and existing visualization catalogues can be of help here (see e.g., Data Visualization Catalogue, From Data to Viz, Data Viz Project)

For more insights, results, and tips on how we can better visualize our findings, read the full article:

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