How to predict human mobility in response to a disaster

New research by a Northeast engineering professor has 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, associate professor of civil and environmental engineering at Northeastern, and Jianxi Gao, assistant professor of computer science at Rensselaer Polytechnic Institute, also found a disparity in movement between the different groups. economic which exposes those who have little means. at greater risk.

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

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

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 especially their mobility behavior,” he says. “How often do they spend time 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 pings from the electronic devices of 90 million people across the United States.

There were universal behaviors, like the tendency for people to leave their homes more frequently over time, a phenomenon known in scientific terms as temporal decay.

When the researchers added variables such as census tract information on income and ethnic diversity, they found large differences between human mobility in poorer and richer neighborhoods.

They found that residents of poor neighborhoods left their homes earlier and more frequently than residents of wealthier neighborhoods.

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

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

The research team observed similar patterns during weather-related disasters, Wang said.

“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 those most at risk of being exposed to danger from large-scale events.

“Some people probably want to distance themselves further from society, but they just can’t,” he says.

“Based on the results, we can speculate on the reason,” says Wang.

Low-income people not only need to be physically present at their work; they are also less likely to be able to stock up on food, water and ice and to have emergency generators at their disposal.

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

“We hailed these essential workers as heroes, but we are really sacrificing their health so they can provide these services,” Wang said.

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

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

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