Thinking with Climate Data

In this section, Rahul Bhargava discusses why he selected climate change data as the focus for the course.

The Richness of Climate Change Data

The “data to think with” in the Spring 2017 iteration of Data Storytelling Studio were all derived from climate change science. I chose this focus because I have a background in museum exhibit design, and the hardest exhibit I ever had to create was an interactive simulation of climate change. The exhibit featured the world of 2030 and engaged visitors in making decisions effecting climate. Visitors could increase forestry or reduce power consumption, and the exhibit would show them the societal impacts of those decisions. To design the exhibit I had to read the United Nationals Intergovernmental Panel on Climate Change reports and translate the data into engaging content for museum goers. It was a challenge!

It's hard to communicate about climate change for a few reasons. One is because the data are so politically and emotionally charged. People tie their beliefs about climate change to their self-definition. Also, the science producing the data is dense. If you’re trying to work on bee colony collapse data, for example, you’ll encounter an incredible degree of interconnected complexities. Finally, the definition of climate change is broad. It encompasses everything from colonizing other planets to forestry to migration. 

The diverse interconnectedness of climate change data is precisely what makes them such good data to think with in the course. They challenge student to dig into problems that are not narrowly defined. The broad definition of climate change also means that students are able to find data with which they feel a strong connection. This leads to meaningful project experiences. For many students, especially undergraduates, their identities are tied to their institution, so climate change data allow them to talk about bicycle riding on campus or rising sea levels that impact the Charles River, which could, in turn, flood an iconoclastic MIT building.

No General Public, No Raising Awareness

Another great thing about climate change, as a class topic, is that there’s all this great research in psychology that show that when you show people data that contradict beliefs tied to their self-definition, they will become defensive and the data you show them will actually reinforce their original beliefs. This creates a great opportunity in the course to talk about audience and goals. If you are trying to communicate about adverse weather events with an audience you know is predisposed to think that climate change is a hoax, you need to structure your argument very differently than you would if you were talking to a scientific audience. It’s ripe for challenges and we really dig into them in the course.

One way we do that is by personalizing how we design, deliver, and assess our science communication. In my class, you can’t use the term “general public” and you can’t set a goal of “raising awareness.” That doesn’t cut it. It’s not because I'm against raising awareness in the general public—it’s fine for some things. But we’re trying to create change. If you say “general public,” it doesn’t give you any heuristics. It doesn’t help you select a format for the data you want to present. Conversely, if you specify that that you want to focus on people living in Massachusetts and that you’ll be talking with them on the street, suddenly you have audience criteria to help you deliver and measure your communication. Is this audience predisposed to disagree with you? All right, that helps you plan. Are you speaking with them after lunch? Okay, you have to make sure they don’t fall asleep. Once you’ve defined your audience, you can also begin to think beyond raising their awareness—which is really hard to assess. It’s also a cop out because if your communication isn’t changing people’s behavior, it doesn’t make much of a difference. However, getting your audience to sign a particular petition, changing their buying patterns, getting them to come to a rally, or connecting them with four other people to talk about a particular issue are things you can measure.

On Domain Expertise

You don’t need to be a climate change domain expert to teach this course. I’m certainly not an expert. The place I struggle is in the assessment of how well students have completed data analysis. For instance, I generally have to look carefully at students’ data if I ask them to show me an annualized chart of populations. I recreate the chart to make sure they have normalized the data correctly. Other than that, I don’t need to have particular expertise in the domain. Oftentimes, I identify more with the audience they’re arguing to, and that’s super helpful. 

There’s also no reason someone else teaching a similar course needs to select climate change as their focus. Data Storytelling Studio works well with a variety of data. I’m passionate about food security, so one of the earlier iterations of the course focused on that. It worked well because I was enthusiastic about it. If there’s a domain you’re particularly excited about, do that. We’re living in a world right now where you can get lots of data sets about almost anything. The opportunities are endless.