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Title: Data-Driven Modeling of Evacuation Decision-Making in Extreme Weather Events
Data from surveys administered after Hurricane Sandy provide a wealth of information that can be used to develop models of evacuation decision-making. We use a model based on survey data for predicting whether or not a family will evacuate. The model uses 26 features for each household including its neighborhood characteristics. We augment a 1.7 million node household-level synthetic social network of Miami, Florida with public data for the requisite model features so that our population is consistent with the survey-based model. Results show that household features that drive hurricane evacuations dominate the e ects of specifying large numbers of families as "early evacuators" in a contagion process, and also dominate e ffects of peer influence to evacuate. There is a strong network-based evacuation suppression eff ect from the fear of looting. We also study spatial factors a ecting evacuation rates as well as policy interventions to encourage evacuation.
Authors:
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Award ID(s):
1916805 1745207
Publication Date:
NSF-PAR ID:
10300635
Journal Name:
Complex Networks and their Applications
Sponsoring Org:
National Science Foundation
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