How to predict human mobility in response to a disaster

New research by a Northeastern engineering professor used recent storms and the COVID-19 pandemic to predict human movement during disasters in anticipation of more effective emergency response.

The research team, led by Qi Ryan Wang, an associate professor of civil and environmental engineering at Northeastern, and Jianxi Gao, an assistant professor of computer science at Rensselaer Polytechnic Institute, also found a disparity in movement between different economic groups that exposed those with few resources. at higher risk.

Wang and his team used anonymous data from 90 million Americans during six major events to create a mathematical model to predict human mobility during disasters. The results were published in early August in the prestigious journal Proceedings of the National Academy of Sciences (PNAS).

Predictable movement patterns arose from Hurricane Dorian, Tropical Storm Imelda, the Saddleridge Wildfire, the Kincade Wildfire, all in 2019, the Texas winter freeze of 2021, and the COVID-19 pandemic, Wang says.

Headshot of Qi 'Ryan' Wang.
Qi “Ryan” Wang, Assistant Professor of Civil and Environmental Engineering, poses for a portrait. Photo by Matthew Modoono/Northeastern University

“The idea started with the pandemic,” says Wang.

“We started looking at people’s behavior, but particularly their mobility behavior,” he says. “How much time do they spend away from home, especially when social distancing was so important?”

Wang and other team members used anonymous information provided by an outside company to analyze the pings from the electronic devices of 90 million people in the US.

There were some universal behaviors, such as the tendency for people to leave their homes more frequently as time went on, a phenomenon known in scientific terms as temporary deterioration.

When the researchers added variables such as information provided by census tracts on income and ethnic diversity, they found large differences between human mobility in less and more affluent neighborhoods.

They found that people from poorer neighborhoods left home earlier and more often than people who lived in wealthier areas.

The behavior is not based on a lack of commitment to safe practices, says Wang.

“People in the slums took much longer to practice social distancing” during the COVID-19 pandemic, says Wang. “They are essential workers. They still need to go to work to support their families.”

The research team observed similar patterns during weather-related catastrophes, says Wang.

“The model can describe them all,” he says.

Wang says the research can help emergency services and other agencies target responses during disasters and also identify people most at risk of exposure to danger from large-scale events.

“Some probably want to social distance more, but just can’t,” he says.

“Based on the results, we can speculate as to why,” says Wang.

People with lower incomes don’t just need to be physically present at work; they are also less likely to be able to stock up on food, water and ice and have emergency generators at their disposal.

Wang says that mobility patterns can also help explain the different rates of COVID-19 in different communities.

“We hail these essential workers as heroes, but in reality we are sacrificing their health so that they can provide these services,” says Wang.

Governments and emergency services can use the information provided by the human mobility model to better understand how to allocate their resources during a public crisis, say Wang and the other authors in the PNAS paper.

“Our model represents a powerful tool for understanding and forecasting post-emergency mobility patterns and thus helping to produce more effective responses.”

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