Modeling joint evacuation decisions in social networks: The case of Hurricane Sandy
Section snippets
Introduction and motivation
Hurricane Sandy (October 2012) caused about 254 deaths in the US, Caribbean, and Bahamas with an estimated economic loss of $65 billion (USD) (Ehrhart et al., 2012). Seventy-two fatalities, including 41 drownings from storm surge, resulted from Sandy’s wind and flood impact (Gladwin et al., 2013). Timely evacuation could have averted such storm surge deaths that were higher than all but two prior hurricanes (Katrina and Camille) in the past 60 years (National Hurricane Center, 2015). Most of
Background and related work
Evacuation is a typical form of travel during extreme events and it is the usual recourse to prevent loss of life if high storm surge occurs. However, evacuees often show synchronized behavior in terms of when they depart and what route they take. (Wolshon, 2002). Several studies show important factors affecting evacuation decisions such as hurricane trajectory and warning system, characteristics of evacuees and their households, etc. to explain the overall evacuation process (Baker, 1991,
Data collection
In this study, data was collected by surveying residents from high storm surge risk areas of New York and New Jersey all of whom participated in the survey voluntarily (see Fig. 1). The survey was funded by the National Science Foundation. The sample frame for the survey was based on US census block group areas in coastal New Jersey and New York likely to be at risk of life-threatening storm surge in a major hurricane; the block groups within five km of the coastline or inland tidal water body
Social networks, social influence, and evacuation decisions
Social networks include individuals as nodes and their relationships as links or edges. Such relationships help to construct a network of individuals (Jackson, 2010) and guide how the process of social influence takes place between peers. Studies have suggested that sources of influence may include personal influence between the direct contacts, influence between social groups or social circles, and influence from information sources such as media (Kadushin, 2012). As such, there exists a
Modeling framework
In this study, a Hierarchical Generalized Linear Modeling (HGLM) approach for a multinomial model is used to assess the joint evacuation decision outcome during a major hurricane at the dyadic (ego-alter) level. The multinomial multilevel modeling approach is appropriate for clustering data with a multinomial outcome considering the following two specific reasons: (1) multinomial analysis is more efficient than a series of binary logit analyses since the former approach involves a large number
Model estimation results
Following the modeling framework discussed above, a multi-level multinomial logit model is estimated in this study. As presented in Table 2, all of the variables included in the mixed logit model are statistically significant with plausible signs at the usual 5% or 10% levels of significance. Prior to estimating the full, two-level multinomial HGLM, to examine how much variation in evacuation decision exists between different egos, the empty model, which has no explanatory variable, was
Conclusions and key findings
In this study, the role of social ties on evacuation behavior is explored by using ego-centric social network data obtained from Hurricane Sandy and by considering the hierarchical structure of the data i.e. friends or close contacts (alters) being nested within an individual (ego). With the help of Hierarchical Generalized Linear Modeling (HGLM) technique, this study develops a multinomial multilevel model of joint evacuation-decision outcome based at the dyadic (ego-alter tie) level.
Acknowledgements
The authors are grateful to National Science Foundation for the Grant CMMI-1131503 to support the research presented in this paper. Several questions used for the survey questionnaire were derived from earlier research on the Hurricane Sandy evacuation done by Hugh Gladwin and Betty Morrow supported by National Science Foundation grant CMMI-1322088. The survey was conducted by Hugh Gladwin of Florida International University. The writers also acknowledge Prof. Sharon Christ of Purdue University
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