Human Mobility Perturbation and Resilience in Natural Disasters


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Virginia Tech


Natural disasters exert a profound impact on the world population. In 2012, natural disasters affected 106 million people, forcing over 31.7 million people to leave their homes. Climate change has intensified natural disasters, resulting in more catastrophic events and making extreme weather more difficult to predict. Understanding and predicting human movements plays a critical role in disaster evacuation, response and relief. Researchers have developed different methodologies and applied several models to study human mobility patterns, including random walks, Lévy flight, and Brownian walks. However, the extent to which these models may apply to perturbed human mobility patterns during disasters and the associated implications for improving disaster evacuation, response and relief efforts is lacking. My PhD research aims to address the limitation in human mobility research and gain a ground truth understanding of human mobility patterns under the influence of natural disasters. The research contains three interdependent projects. In the first project, I developed a novel data collecting system. The system can be used to collect large scale data of human mobility from large online social networking platforms. By analyzing both the general characteristics of the collected data and conducting a case study in NYC, I confirmed that the data collecting system is a viable venue to collect empirical data for human mobility research. My second project examined human mobility patterns in NYC under the influence of Hurricane Sandy. Using the data collecting system developed in the first project, I collected 12 days of human mobility data from NYC. The data set contains movements during and several days after the strike of Hurricane Sandy. The results showed that human mobility was strongly perturbed by Hurricane Sandy, but meanwhile inherent resilience was observed in human movements. In the third project, I extended my research to fifteen additional natural disasters from five categories. Using over 3.5 million data entries of human movement, I found that while human mobility still followed the Lévy flight model during these disaster events, extremely powerful natural disasters could break the correlation between human mobility in steady states and perturbation states and thus destroy the inherent resilience in human mobility. The overall findings have significant implications in improving understanding and predicting human mobility under the influence of natural disasters and extreme events.



Human Mobility, Natural Disasters, Geo-Social Networks, Resilience, Big Data