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Using big data to predict disasters and democratize climate resilience

Source(s):  Yale University

The day after an Energy and Climate bill passed, Bessie Schwarz (FES ’14) was hustling in the campaign office in Ohio in 2009, working on persuading a few representatives who were key swing votes by pushing the community to take action.

A self-described “organizer at heart,” Schwarz credits her years at the Yale School of Forestry and Environmental Sciences — where she sought out more innovative ways to empower citizens — for enabling her to harness the power of community activism in making tangible change.

 “In the next 12 years, the number of people affected by climate change will double,” she told attendees of the Yale Women Innovators Breakfast Series, a weekly event held at the Yale Entrepreneurial Institute. “Coordinated and strategic ways of empowering communities and delivering it at the right moment can deliver huge change in this country.” The event was co-sponsored by Yale Environmental Women.

Schwartz co-founded Cloud to Street to help people respond more quickly to climate change disasters–particularly flooding, which affects more people globally than any other disaster. The startup is a response to a world where global transformation, population migration, scarcity of resources and climate change are all impacting human lives.

Schwartz said that dozens of millions to hundreds of millions of dollars in personal property is damaged from flooding each year, and billions of dollars need to be deployed in relief funds. In 2016, the U.S. saw more floods than ever in response to rapidly changing systems of weather patterns.

“We are incredibly unequipped to deal with this,” she explained, noting that 80% of developing world is uninsured.

Relief agencies are often unprepared to manage natural disasters. Tens of thousands of houses are left out of flood maps that delineate boundaries for relief agencies to send help, resulting in huge populations of people who have been affected by flooding going without help.

“Predictions show they shouldn’t have been in the affected zone, so federal and international agents do not get to them,” Schwarz explained. “Using technology, this can all be done on one device with internet access.”

The Cloud to Street model combines satellite imagery and cellphone data with social vulnerability modeling to identify the most vulnerable communities along with machine learning hydrology (mapping the floodplain).  The co-founders are working on constructing the largest inventory of past major flooding in the world over the last 20 years in a single record in collaboration with the Dartmouth Flood Observatory.

But, as Schwarz said, physical hazard is only half the story. Working since the beginning with communities that are at higher risk and vulnerability, such as areas of high density, low cultural cohesion and low social capital, has helped inform their work. For example, the biggest statistical trends of those likely to suffer from property damage include people who fall in the following categories: a renter, an African-American, and a woman.

“Identifying things that make someone more vulnerable to experiencing loss helps us prioritize resources for aid, especially given the inequality in the U.S.,” she said. “Inequity did not come with the storm, it came with the city and the country we’re living in.”

The company is currently operating in Nepal and two other countries, and Schwarz aims to continue using moments of climate change to identify and transform how people interact with government agencies.

“Yale is an incredible place to have an entrepreneurial spirit,” she told the audience. “Use the university for inspiration and collaboration.”

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  • Publication date 01 Feb 2017

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