With increased frequency and intensity, disasters like wildfires and hurricanes have become a growing threat to populations across the globe. In fact, according to the Food and Agriculture Organization of the United Nations, natural hazards are occurring three times more often today than in the 1970s and 1980s. This has created severe challenges for emergency services charged with mitigating these crises and helping affected communities recover.

One major way to reduce risk and enhance the safety of communities vulnerable to natural hazards is to improve our understanding of evacuation behaviors. This insight could help emergency managers develop appropriate response measures and make effective decisions during a disaster, while also enhancing emergency planning strategies to prepare high-risk households for potential disasters in the future.

In the past, insight into evacuation behaviors has been gathered through data collection methods, such as surveys, interviews, and focus groups. These methods have various limitations, however, like small sample sizes, narrow timeframes to analyze, and self-reporting bias. When a disaster strikes and residents are evacuating, it’s imperative that emergency managers enact the right strategies based on accurate and reliable data. So, how can they more precisely examine evacuation behaviors?