Emergency Preparedness and Response

How Location Intelligence Can Help Protect Lives During Disasters

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?

How location intelligence can improve emergency management

Location intelligence provides insight into global movement patterns in the physical world. By analyzing pseudonymous location signals from mobile devices, we can understand how people, products, and materials move throughout the world. Beyond providing actionable insights to businesses, location intelligence can help to solve some of society’s biggest challenges.

With location intelligence, regional and local leaders can help vulnerable communities reduce risk and enhance safety during disasters and emergencies. With the insights derived from location data, local emergency managers can better prioritize outreach, deploy life-saving services, and direct the efforts of first responders based on how residents in a certain community react during times of crisis.

How the University of Florida Transportation Institute used location analytics to understand evacuation behaviors

To enhance the understanding of evacuation behaviors during disasters, researchers from the University of Florida Transportation Institute (UFTI) wanted to study the movements of local residents during the 2019 Kincade Fire in Sonoma County, California, by leveraging a different data collection method than those used in past studies. They hypothesized that location analytics would provide new insights into human decision-making and movement during this catastrophic event.

UFTI researchers worked with Gravy Analytics to secure trusted human mobility data for their study. Gravy provided the researchers with a privacy-friendly location data set for mobile devices seen in the evacuation zone and surrounding area of the wildfire.

From there, UFTI researchers were able to determine and apply their own modeling parameters to the location data, which fueled their analysis into the movement of local residents before, during, and after the fire. Through this new methodology, UFTI researchers categorized residents as either non-evacuees or specific types of evacuees based on where they lived and when they chose to evacuate during the wildfire.

The researchers were then able to determine the specific areas that had high concentrations of evacuees and on what day and time they chose to evacuate. With this knowledge, emergency managers could enhance various response measures in the future, such as executing traffic management strategies, planning evacuation zones, issuing evacuation orders, providing support for travelers in need, undertaking rescues, and more.

Improved natural disaster emergency response planning and management

Now more than ever, improving our understanding of disaster evacuation behaviors is a pressing need. The results of UFTI’s study not only helped the research team to better understand evacuation behaviors during the 2019 Kincade Fire, but the same methodology can be applied to disasters everywhere.

By analyzing location data that illustrates how local residents evacuate during a natural hazard, emergency managers across the country can develop and improve their emergency response measures. They can even customize emergency management processes according to the time of year, day of week, or even time of day based on understanding the regular behaviors of local populations at those times.

Perhaps most importantly, location intelligence can be used by emergency managers and planners in the development of targeted public outreach campaigns, training protocols, and emergency communication strategies to prepare high-risk households for future disastrous events. With enhanced strategies, at-risk communities may be able to reduce the impact of a natural hazard. As these disasters become more frequent, this has never been more critical.

Jeff White is the founder and CEO of Gravy Analytics. Xilei Zhao, PhD, is an assistant professor in the Department of Civil and Coastal Engineering at the University of Florida.

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