What data visualization can do for science

This article first appeared on the Habitat X Change blog. Habitat X Change is a collaborative space that connects science, visualization and design for the future of cities. It will debut during the Habitat III summit in October in Quito, Ecuador. You can read more about this event here.

In this dialogue, Garry Peterson, Professor of Environmental Sciences at the Stockholm Resilience Centre, and Sebastian Meier, Research Associate and Lecturer at the Interaction Design Lab at University of Applied Sciences Potsdam, discuss the power of data visualization as a tool for science and science communication — and what scientists need to be aware of when they plan to work with designers on visualizing their own work.

Garry: In my work, data visualization is very important for enabling interdisciplinary communication and trying to explore and explain complex interactions. Many aspects of sustainability involve multiple causation and complex feedbacks. And you can’t easily describe those in a simple, linear narrative. You rather need to try and grasp it all at once. And so that’s why you need images.

Sebastian: I think there are many reasons why data visualization is becoming more important for science. For example, it’s definitely optimizing communication. Communicating to the public, to policymakers, and making complex issues more understandable, more accessible.

Garry: I think data visualizations are a really positive thing for science. As you’re an expert in the field, could you tell me what data visualization has to offer science and where it’s going in the coming years.

Sebastian: One of the most important things is the way data visualization can support science communication. In science we have really complex issues, but these issues need to be understood by policy-makers and society, especially if you’re talking about sustainability. Visualization can really help make these complex issues more understandable, more visible and more graspable.

Garry: So you’ve worked a lot with scientists. And I’m really interested in this. What does it take to create a successful collaboration with scientists, visualizers and designers?

Sebastian: I think we have to see the whole scientific process as something not so linear. Often you have well, the research, and then you have your scientific publication process, and in the end there’s a little bit of PR work maybe when there’s some money left.

But actually it should be an iterative process. The first step is to have a communication or a visualization expert in your research team. And then just over and over thinking about who are we actually trying to reach, who is our audience, and then really creating like a communication and visualizing concept that aims at a specific problem and a specific audience.

I think that could lead to really good teamwork and interdisciplinary work that would not have been possible otherwise.

Garry: And what do you think scientists need to know about design or data visualization to really make that process work well if they are collaborating with a person like that in their team?

Sebastian: I think they should have a basic understanding of what visualization can do and how you can make data visible and create kind of different perspectives on one problem. They don’t need to create visualizations themselves, but rather to understand the possibilities so they can talk to designers and visualization experts, and also to come up with new ideas about what they could do with their research.

Garry: Scientists are often really used to charts and maps, but visualization offers a lot more. How can scientists get into thinking about alternate ways of presenting data, that are maybe more interactive or animated. How do you suggest they do that?

Sebastian: It’s mostly about working directly with designers. At some point, when visualizations get a lot more complex, you need a lot of knowledge about how perception works, about how visual variables work, so there you definitely need interdisciplinary teams. But at least if you’ve worked together with designers, if you have attended workshops and become more aware of the possibilities, then you can anticipate these kind of creative visual processes and also come up with new ideas.

Garry: When do you think these more rich types of visualization are needed or useful? Any particular type of problem?

Sebastian: I think the more complex an issue becomes, the more difficult it is to display it in a linear narrative. And then you need a new perspective and new visualizations that also give the reader of a visualization some controls of their own. In interactive visualizations you can explore the data on your own and find new connections and new relationships inside the data. This allows the user, the reader of a visualization to become more engaged with the actual content and the questions behind it, to come up with new questions and start discussions, for example.

Garry: Yeah. And how about using data visualization techniques to better understand more qualitative data that people traditionally haven’t made charts or graphs of?

Sebastian: My experience so far is that there it gets really important to include designers. Most of the time that you’re dealing with qualitative data, the visualizations move more into the direction of infographics. So it’s not so much about data visualization but about information visualization. And there you really need to come up with complex concepts of how to create those images that are hidden inside the qualitative data.

Garry: One thing I think scientists worry about is that a picture is just too … flashy. But I think there’s really a useful way of thinking about designing scientific visualizations for data exploration, versus designing graphics that are really catchy or engaging to grab people’s interests. Do you try and do both of those? Or do you think you need to do more one or the other? How do you work with scientists, making things that are really conveying precise scientific information versus engaging people with some kind of issue or data.

Sebastian: I mean ideally it’s a combination, you have an interactive visualization that’s based on real data and real facts and tries to be objective, but through the visualization, through the design, through the interaction techniques, you still achieve an engaging interaction with the data. So kind of combining the two.

But of course creating visualizations you need to be aware that the image, the visualization is very powerful. People often have a hard time reading long lists of tables, numbers and reports. And the image, it is really much faster received, so it has a lot more power. So visual literacy becomes more and more important. To teach people to question visualizations. To teach people to find out how a visualization might be true or if it might be used for some other purpose. Whether somebody is just using a visualization to make a point and there is actually no truth in it.

So this also becomes more important. And I think there’s a really nice opportunity to work with scientists. Scientists are used to questioning problems and statements.

Garry: I was just wondering, in your experience, you’ve worked with a bunch of scientists, what are maybe some of the traps or problems that you’ve had, and how scientists can maybe avoid falling into those.

Sebastian: What’s interesting is that even scientists also fall for the visual stuff. Usually, when we present our first ideas and visualizations, they fall for the same power that the visual image has for the rest of society. So they are not immune from this power of the image.

Garry: Seduced by the image.

Sebastian: Yeah.

Garry: Even if it’s wrong.

Sebastian: Yeah, because it really has power, because it is for some reason so much more simple than a big table with numbers. When we look a little bit into the history of the kind of data visualization that has been done by statisticians, for example, people see kind of the “truth“ in this visualization because they think that okay, there’s a bunch of data behind it and this visualization cannot lie because it’s based on data, on truth. But of course you can lie with visualizations.

Garry: So how to avoid this trap? Is it all about improving visual literacy?

Sebastian: One thing that’s really important — and it might sound a bit strange — is having an open mind and not being afraid of technology. Many scientists already have great ideas about how they could visualize their data but they are thinking — okay but we cannot work with those technologies, we cannot code, we don’t know what is possible. Actually so much is possible nowadays, there are all those open source libraries, all those free tools out there that can help you visualize your data and — okay you need to learn a bit, but it’s not that hard to use those tools. And to not to see barriers where no barriers are, you need to be open to it.

This conversation took place in 2014 during the ICSU-ISSC-DFG Young Scientists Networking Conference on Integrated Science at Villa Vigoni, Menaggio, Italy.