Modeling joint evacuation decisions in social networks: The case of Hurricane Sandy. (December 2017)

• Record Type: Journal Article
• Authors: Sadri, Arif Mohaimin; Ukkusuri, Satish V.; Gladwin, Hugh
• Is Part Of: Journal of choice modelling. Volume 25(2017)
• Page Start: 50
• Page End: 60
• Publication Date: 2017-12
• Subjects: Hurricane Sandy -- Evacuation decision -- Social network -- Ego-centric -- Dyadic level; Decision making -- Periodicals; Social choice -- Periodicals; Decision making; Social choice; Periodicals; 302.13
• DOI: 10.1016/j.jocm.2017.02.002
• Languages: English
• ISSNs: 1755-5345

Abstract

Abstract: Coastal areas of the United States are vulnerable to substantial loss of lives and property damage from repeatedly occurring hurricanes and evacuation is the usual recourse to prevent loss of life when high storm surge threatens. The fundamental question in evacuation modeling is to explore the complex evacuation decision-making process leading to an individual's decision to evacuate or not during a hurricane threat. Recent studies suggest that the social network characteristics of individuals could potentially determine overall evacuation patterns. This study explores the joint evacuation decisions of individuals in personal networks by using ego-centric social network data obtained from Hurricane Sandy and by considering the nested structure of the ego-centric network data, i.e. close contacts (alters) as nested within an individual (ego). In this regard, the study develops a multinomial multilevel model of joint evacuation decisions at the dyadic (ego-alter tie) level utilizing a Hierarchical Generalized Linear Modeling (HGLM) approach. Model estimation results suggest factors that define a social tie (contact frequency, discussion topic and geographic proximity) significantly influence the evacuation decisions between individuals and their social partners. In addition, individuals' (both ego and alter) own socio-demographics such as age, marital status, previous evacuation experience, evacuation order, household's type, size, location and proximity to a water body also affect the decision to evacuate. These findings are useful to help emergency managers implement efficient evacuation strategies and to facilitate planning by policymakers by determining fractions of people evacuating or not for a major hurricane within the context of their social networks.