Improving Hurricane Evacuation Prediction Models

Authors: Hannah Heinke-Green*, Florida State University, Jay Baker, Florida State University , Tingting Zhao, Florida State University , Minna Jia, Florida State University
Topics: Environmental Perception, Geographic Information Science and Systems, Coupled Human and Natural Systems
Keywords: Hurricanes, Risk Factors, Prediction, Models, GIS, Risk Perception
Session Type: Guided Poster
Day: 4/6/2019
Start / End Time: 9:55 AM / 11:35 AM
Room: Roosevelt 3.5, Marriott, Exhibition Level
Presentation File: No File Uploaded


Accurately predicting evacuation rates before a hurricane can aid emergency management officials. Having an estimate of the number of residents who will evacuate, helps inform a multitude of decisions officials must make regarding public safety as a hurricane approaches. Current models assess risk perception factors in order to predict the evacuation decisions. Combining survey data collected from residents of Charlotte County, Florida who were impacted by Hurricane Irma (2017) with spatial data analysis techniques will allow new variables to be incorporated into evacuation prediction models. These new variables, along with the known factors that impact hurricane evacuation decisions, will create a versatile model that most accurately predicts evacuation decisions prior to an impending hurricane.

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