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New in disaster science: using machine learning and maps to see who’s vulnerable

DataKind partners with World Bank to help developing countries reduce their vulnerability to natural hazards and climate change

An OpenStreetMap ariel photo of Sri Lanka. This is an example of the type of open data that could be used in the DataCorps project, in addition to other datasets.
An OpenStreetMap ariel photo of Sri Lanka. This is an example of the type of open data that could be used in the DataCorps project, in addition to other datasets.

Natural disasters are on the rise worldwide, and the consequences are especially devastating for developing countries.

From 1995 to 2014, 89 percent of storm-related fatalities occurred in lower-income countries even though these countries experienced just 26 percent of storms, according to the World Bank, which forecasts that the impact of these disasters will grow “as climate changes increases the frequency and intensity of extreme weather events.”

A new DataCorps project by DataKind and the World Bank’s Global Facility for Disaster Reduction and Recovery (GFDRR) aims to help developing countries build resilience for communities threatened by natural disasters by leveraging data and advanced technology. Using machine learning and satellite imagery, data scientists will identify where people are at risk in a natural disaster as measured by locations and types of buildings.

Current technology can identify where buildings exist. This project takes things a step further by seeking to determine building type. Knowing building location and type would provide GFDRR with a population estimate so they know where to distribute resources in the case of a disaster (for example, if a typhoon strikes during the day, additional resources would be deployed to schools and business centers). The information could also help improve resiliency and reduce risk in these vulnerable areas.

This is one of the first machine learning endeavors for GFDRR, which was seeking a novel approach to identifying where people are at risk in a natural disaster. They can do this work fairly well by hand, but they’re interested in understanding if and how machine learning and satellite imagery could boost their efforts.

The project is funded by the Elsevier Foundation, which gave DataKind $50,000 as part of its Technology for Development program. The foundation also supported a recent DataDive led by DataKind’s UK Chapter, which brought more than 70 volunteers together in London to explore out-of-the-box data solutions for charities.

Ylann Schemm“The work Datakind does is so innovative — bringing predictive analytics to some of the greatest challenges outlined by the UN’s sustainable development goals,” said Elsevier Foundation Director Ylann Schemm.

DataCorps projects apply machine learning and data science to tackle critical humanitarian issues with the help of volunteer data scientists.

Jake Porway“It's inspiring to work with organizations like the World Bank who are continually pushing the envelope and pursuing new and innovative ways to address the complex social problems we face in the world today,” said Jake Porway, founder and executive director of DataKind.

“And when it comes to applying data science and AI to tackle challenges like disaster risk and recovery, to create real impact, you need visionary and knowledgeable partners like these. We’re excited to be collaborating with them on this project and look forward to the learnings and tools that will be gained from the work.”

DataCorps projects bring together teams of pro bono data scientists with social change organizations to collaborate on long-term projects designed to produce data science solutions that lead to action and transform an organization’s work and its sector. DataKind first helps organizations define their needs and discover what’s possible through an extensive scoping and data audit process. They then help translate those needs into data science solutions, including advanced analytics and modeling as well as tool development.

Download the report.The World Bank’s Global Facility for Disaster Reduction and Recovery (GFDRR) provided expert commentary for Elsevier’s recent report A Global Outlook on Disaster Science. Elsevier examines the link between man-made and natural disasters on the one hand and scholarly output on disaster science on the other. The analysis reveals the focus areas of disaster science and zooms in on the most prominent countries involved in disaster science research.

To examine the state of worldwide disaster science research, Elsevier partnered with leading international institutions and drew upon high-quality global data, including Scopus. Read the report.

Elsevier Foundation partner grants

The Elsevier Foundation provides over $1 million a year in grants to knowledge-centered institutions around the world, with a sustainability focus on innovations in health information, diversity in STM, and research in developing countries. Their goal is to harness the power of technology to expand these opportunities to underserved communities through funding and in-kind support. Learn more.

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