Enhancing demographic coverage of hurricane evacuation behavior modeling using social media

摘要

Hurricane evacuation is a complex dynamic process and a better understanding of the factors which influence the evacuation behavior of the coastal residents could be helpful in planning a better evacuation policy. Traditionally, the various aspects of the household evacuation decisions have been determined by post-evacuation questionnaire surveys, however, these surveys have seen a deterioration in the quality of the data due to a gradual decrease in response rates in recent years, which may lead to non-response bias. Increased activity of users on social media, especially during emergencies, along with the geo-tagging of the posts, provides an opportunity to gain insights into user's decision-making process, as well as to gauge public opinion and activities using the social media data as a supplement to the traditional survey data. This paper leverages the geo-tagged Tweets posted in the New York City (NYC) and Jacksonville, FL in wake of Hurricane Sandy and Matthew respectively to understand the evacuation behavior of the Twitter users and compare them with that of the survey respondents. We design the Twitter user classification problem as a novel HMM modeling framework to classify them into one of the three categories: outside evacuation zone, evacuees, and non-evacuees. We compare the demographic composition (age, gender, and race/ethnicity) and spatial coverage of Twitter users with that of the survey respondents to highlight the complementary nature of the two data sources, which when combined give a representative sample of the population. We analyze the GPS coordinates of the tweets by evacuees to understand evacuation and return time and evacuation location patterns and compared them with survey respondents. The techniques presented in this paper provide an alternative (fast and voluntary) source of information for modeling evacuation behavior during emergencies, which is complementary in terms of demographics and spatial distribution as compared to the traditional surveys and could be useful for authorities to plan a better evacuation campaign to minimize the risk to the life of the residents of the emergency hit areas. (C) 2020 Elsevier B.V. All rights reserved.