Data Visualization for Meaningful Change with Illah Nourbakhsh, Professor at Carnegie Mellon University & Director of the CREATE Lab

Now these explore bubbles and what they are is layerings of actually about 3000 data layers of information about the earth, everything from forest loss and gain to demography, like people. To urban fragility to sulfur dioxide, air pollution, water pollution, water loss, gain drought, and all these layers together play over time. So the animating and they're zoomable.

So you can zoom out for instance and see the issue of drought across the whole continent of Africa, where you can zoom in and see agricultural development around a Lake that causes the lick to dry up over time in Iran, for example. And yeah. Because there's doable in space and because you can browse them through time, we can tell stories and we can tell stories about solar panels and wind farms being put into place. At the same time.

It can tell stories about Lake Mead shrinking because Las Vegas grows too quickly. And literally thousands of stories are possible. Each one though, because they're visual really burn themselves into your retina and get right into your cortex. So it's like a direct path in that lets you emotionally engage with the whole idea that the earth is changing under our feet rapidly because of the decisions we make.

If you had all the money in the world to spend on research and one group of people there had an idea and the idea was. We need more global empathy about the globe itself about all the changes we're seeing on the earth. So I would create visuals that show that off, because look at us, we're making these amazing panoramas of Mars back then, these are the Mars exploration rovers. Why can't we make amazing panoramas on earth? And I came back from the sabbatical to Carnegie Mellon.

But in fact, that group of researchers came with me. So they left NASA and came to the create lab and they invented ways of creating GigaPan panoramas on earth. We made it, we called it a GigaPan site, still exists, and those robotic devices allowed us to do things like go and show ice flows in Antarctica, flowing backwards, where the glaciers are being lost. They let us show all kinds of impact effects across the earth in images that were very high resolution.

But then as we did that more and more, we got to talk to NASA. We got to talk to JP Morgan and standard and Poor's and the United nations I'm a commissioner for refugees and dozens of other agencies. And they all said, we have data. We can give you more data. So it's not just images, but also information like every refugee on earth that crosses boundaries. So gradually over the course of the last five years, we built up more and more data, but it really started with this idea of doing on earth.

What we already do on Mars, ironically.

Image show of this and you should see their expressions because we can show the trees falling and then the Amazon, and then we can zoom out until you see both the East and West coasts of South American continent. And now you understand the vastness of forest loss and when you show somebody like that, That instead of a chart when you showed it visually like that, they buy into it, they get it and they ask the question, okay, what'd you do about it?

So they move on from doubting the data to wondering about the solution. And I've seen that happen with presidents, with prime ministers time and time again, at the same time, we can sit down with leaders of community organizations in Hazelwood. And in Braddock and we can do the same thing. We can show Mon Valley pollution spreading up toward central Pittsburgh, and we can show the industrial impact on athletics and children and the ways in which they miss school.

Then they don't qualify for college aid because of the way a school participation rate rules work. And so at the very micro local level, we can use the same tools we use with presidents and prime ministers with a local community organizer. Who in turn becomes empowered to tell a powerful story to the EPA and get fines in place to change the behavior of the industrial power plant and true story that's happened. So in each case we're helping somebody make decisions.

In one case we're empowering the local community organization organizer to have information because usually they don't have that information parity with the big wigs at the EPA or the lawyers of the corporate American firm. So we're giving them a kind of power that gives them the ability to. Basically engage in powerful rhetorical discourse, bolstered by data, much more effectively.

In the case of the president of prime minister, we're convincing them of a problems of reality so that they stop listening to the naysayer and the doubt, the person who creates doubt. And instead they start charging their government with actually solving the problem. But it got both cases. You're basically girding. People's better angels to actually act and to have the ammunition with which to act.

Our whore neural systems are set up to be able to wander through a space like a forest and learn it and have a neural net adapt to that. Shape of that forest. Then you can go back there and where that old Oak tree is by the same token, a map really works well at wiring itself into our brain and helping us understand change over space.

And so to me that the visual information flowing in right through the eyes, not being abstracted into numbers, but being actually pixels of information, like we evolve to see that, especially superimpose on a map that gives us a sense of place. Those two are like this winning combination is a one, two punch that makes us understand the data emotionally. It makes us have a response to the data in our heart and not just in our mind. And so I think it's a special way in, and it's what artists use.

Of course it's what filmmakers use, but we're using it to help people understand the reality, the veracity with which the earth is changing.

So the corporations and governments aren't on top and instead people are on top and people have the information with which to act well, but fundamentally what the create lab does with air quality monitors, with the maps we make, actually with pretty much everything we do is we make invisible things visible. Almost every technology we invent is a technology that takes something that's abstract or invisible or hard to comprehend or hard to measure.

And we create really simple cheap technologies generally, or open-source free technologies that make that invisible thing very palpable, very tangible, very visible. Even with, for math learning, we designed tangible interfaces for learning math, with finger sets. For air quality, we design devices that you put in your home. So you can see when the air quality is bad.

So you can connect the dots and realize when you fry something and you don't have a kitchen hood vent on this is what happens in the child's bedroom. And that's why they're coughing. It's all about making invisible things visible, but it's in service of the idea of empowerment. So once you make that visible, why are you doing that?

We're doing that because we believe a more informed populace is able to make more rational decisions and have common ground with each other so that we don't have as much divisiveness in society.

So you've seen that kind of policy at the national level where people drive policy decisions by looking at the disparities that you get with different, small local policies inside of Pittsburgh, we use the maps for very dramatically different kinds of policy decisions. So for instance, during COVID evictions are a huge problem right now.

But we have maps that show demography of eviction, demography of home ownership, the number of people by race, by color of skin who are denied mortgages by mortgage application. And we can show their salary level. So we can show that make enough money for the mortgage, but they just don't get the mortgage. And so by showing historical red lining and then superimposing that with these kinds of demographies, we've got the entire Pennsylvania housing commission now working on new mortgage vehicles.

So that African-Americans and vulnerable populations can get mortgages because then you have much lower cost of living relatively speaking than rental, especially in a city like Pittsburgh.

And the eviction side, we do huge amounts of machine learning and AI trying to scrape sites where we can get access to the early eviction notices before they're legally binding and then connect them to people's residences and then figure out how to reach those residences and then guide them through how to fill out the CDC paperwork to actually have the moratorium apply to them. So it's AI it's.

Problem finding connected to social interaction with an army of social agents on the ground, calling people to get eviction stayed. And that's a direct policy impact that we have that saves people from losing their home in the middle of COVID the worst possible time to get kicked out of your home. And yes, the landlords aren't making any money because nobody has made to pay them. But that doesn't mean you can have all these tenants suffer just as much. And so we try and stop that.

That is to say, when we take data from a company, we will openly share that. So the public can see the effects of that data. So for the corporations has become a badge of honor now to give us the data and have us boast that they gave us the data so we can openly and freely share it. Cause we charge nobody for this stuff. On the other hand, government data. Boy is that hard. The government data is siloed. It's balkanized in many types of databases.

And for instance, just to get drilling rights data from all the States in the U S you wouldn't believe the number of different parsers. We had to write to scrape a variety of different kinds of databases and interactive sites across the U S just so we could make a coherent. Unified self consistent set of data on drilling and mining across the U S which is really remarkable to watch now.

So unfortunately, a lot of our time is actually spent translating and poking at people to get access to data or writing scrapers that get to very difficult to use websites that were never designed for automation. So we can access data, put it in one place and actually share it in, in, in one form that's really hard. And that takes a lot of labor hours.

What's the data they need. What is it that they're trying to collect to tell their story, to make the evidence irrefutable? So people can move from evidence, building to solution finding. And help them with that. Almost everybody will be using some kind of map-based or GIS based tool to visualize the data. And that visual aspect is critically important.

So if you're a volunteer and you have good visual design skills you're needed, and if you don't have visual design skills, find somebody or learn how to do that because. So much depends on design and how you create through interaction design, the best possible interface to honestly, but compellingly connects to people around data.

And there's so many tools out there, Mapbox and Storybox and RPGs, and of course, earth time product, all these are reasonable tools for taking data and showing them, but the single most compelling thing, I typically find.

Or tools that allow you to show animations, to show data changing over time, because that temporal part where you can actually see how things are getting more and more urgent, it turns out to be critical to really sticking as somebody as crawl and making them feel like there's urgency to the issue. When they see the earth being warm.

It's one thing when they see the rate at which the earth is warming now, compared to 20 years ago, a hundred years ago, they become almost panicky and they become convinced that we have to do something about it now. And that sense of urgency. That's really important in that kind of rhetorical communication that you're trying to do to get somebody to care, not just enough to agree with you, but enough to decide they need to actually set aside some time to help you get the job done.

Do you see this kind of. Is this going to be a way that we frame climate change, environmental discussions in the future?

So I think these visual techniques, these ways of summarizing the data in rest or form individual form. Showing trends over time in a way that human, cognitive systems, the kind of visual cortex can make sense of it. I think it's going to become the only way in to make sense of data at such high bandwidth levels and such high rates. Bandwidth levels and size levels that we can't really handle with sheets and charts and plots. Scatterplots so I think there's a grammar there.

It's being developed here by a number of people in number of groups. And that grammar is going to become essentially the way we communicate environmental change data because by definition is global spatial and very high in data density. So it's exciting. It's, a new way to communicate in a way.