HURRICANE EVACUATION ANALYSIS BASED ON SMARTPHONE LOCATION DATA: A CASE STUDY OF HURRICANE FLORENCE

Abstract

Traditionally, hurricane evacuation behavior is investigated based on survey data. Later, various types of data, such as call detail records (CDRs), Global Position System (GPS) data and geo-tagged twitter data are widely used in hurricane related studies. However, few studies have taken advantage of smartphone location data to explore the evacuation behavior during hurricanes. In this study, we analyze the evacuation behavior of residents along the eastern coast of North Carolina based on large-scale, privacy-preserving smartphone location data before and during Hurricane Florence. We first reproduce an existing method and then propose two new models to identify home location for each user based on the processed check-in data. The accuracy of the home locations we identified is assessed by the North Carolina parcel dataset. We find that 89.96% and 90.61% of home locations derived from our two models are in residential areas. Further, a distance-based method is applied to detect evacuees from the trajectory check-ins during the hurricane study period. The evacuation rate estimated is validated by a web-based survey deployed after Hurricane Florence.