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Abstract Due to its unique location, Bangladesh often faces devastating hydroclimatic shocks such as floods and cyclones. In the recent past, three major cyclones (Sidr in 2007, Aila in 2009, and Komen in 2015) claimed 3800 lives and damaged hundreds of thousands of houses with billions of dollars in property damages. In this paper, we focus on understanding people's evacuation behaviors in the face of approaching cyclones using survey data collected through face-to-face interviews with residents living in the coastal areas of Bangladesh. Through various statistical models, including probit, panel probit, bivariate probit, and multinomial logit models, we have explored the determinants of both past and future evacuation decisions, as well as the choice of evacuation destinations. Our findings reveal consistent patterns across different cyclone events, highlighting the significant roles played by warning time, proximity to the coast, property loss, shelter accessibility, housing structure, literacy, past evacuation experiences, and demographic factors such as age, gender, and employment status. Additionally, the analysis of evacuation destinations uncovers nuanced insights into the preferences and challenges faced by evacuees, including the need for improving shelter accessibility. With rising vulnerabilities in coastal areas in Bangladesh and worldwide, identifying what drives households' evacuation decisions and their destination choices can provide useful inputs for evacuation planning and effective disaster management. 

We examined whether floods and cyclones, the shocks that are transient in nature, affect interregional migration differently compared to riverbank erosion that causes loss of lands and thus generates permanent shocks. We tracked Household Income and Expenditure Survey 2000 participants in nine coastal districts of Bangladesh and collected further information in 2015. Our analyses suggest that both transient and permanent shocks induce households to migrate, but the effect is higher for the latter category. Using a difference-in-differences setting, we find that migrants’ income and expenditure increase relative to their counterparts, indicating that facilitating migration may improve welfare in disaster-prone countries. 

Neighborhood effects have an important role in evacuation decision-making by a family. Owing to peer influence, neighbors evacuating can motivate a family to evacuate. Paradoxically, if a lot of neighbors evacuate, then the likelihood of an individual or family deciding to evacuate decreases, for fear of crime and looting. Such behavior cannot be captured using standard models of contagion spread on networks, e.g., threshold, independent cascade, and linear threshold models. Here, we propose a new threshold-based graph dynamical system model, 2mode-threshold, which captures this dichotomy. We study theoretically the dynamical properties of 2mode-threshold in different networks, and find significant differences from a standard threshold model. We build and characterize small world networks of Virginia Beach, VA, where nodes are geolocated families (households) in the city and edges are interactions between pairs of families. We demonstrate the utility of our behavioral model through agent-based simulations on these small world networks. We use it to understand evacuation rates in this region, and to evaluate the effects of modeling parameters on evacuation decision dynamics. Specifically, we quantify the effects of (1) network generation parameters, (2) stochasticity in the social network generation process, (3) model types (2mode-threshold vs. standard threshold models), (4) 2mode-threshold model parameters, (5) and initial conditions, on computed evacuation rates and their variability. An illustrative example result shows that the absence of looting effect can overpredict evacuation rates by as much as 50%. 

We analyzed data from a survey administered to 1,212 respondents living in superstorm Hurricane Sandy-affected areas. We estimated the effect of having experienced hurricane-induced disruptions to utility services, such as electricity, water, gas, phone service, and public transportation, on having an evacuation plan. Around 39% of respondents reported having an evacuation plan in case a hurricane affects their neighborhood this year. Respondents who had experienced disruptions to electricity supply had an approximately 11 percentage-point higher likelihood of having an evacuation plan than those who had experienced no such disruptions. Respondents who had experienced monetary losses from Hurricane Sandy had around a five percentage-point higher likelihood of having an evacuation plan compared with those who had not. Among control variables, prior evacuation, distance to the coastline, residence in a flood zone, concern about the impacts of future natural disaster events, had window protection, and household members being disabled, each had an association with residents’ future evacuation planning and hurricane preparedness. In light of these findings, we discuss the policy implications of our findings for improving disaster management in hurricane-prone areas. 

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 effects of specifying large numbers of families as \early evacuators" in a contagion process, and also dominate effects of peer influence to evacuate. There is a strong network-based evacuation suppression effect from the fear of looting. We also study spatial factors affecting evacuation rates as well as policy interventions to encourage evacuation. 

We study evacuation dynamics in a major urban region (Miami, FL) using a combination of a realistic population and social contact network, and an agent-based model of evacuation behavior that takes into account peer influence and concerns of looting. These factors have been shown to be important in prior work, and have been modeled as a threshold-based network dynamical systems model (2mode-threshold), which involves two threshold parameters|for a family's decision to evacuate and to remain in place for looting and crime concerns|based on the fraction of neighbors who have evacuated. The dynamics of such models are not well understood, and we observe that the threshold parameters have a significant impact on the evacuation dynamics. We also observe counter-intuitive effects of increasing the evacuation threshold on the evacuated fraction in some regimes of the model parameter space, which suggests that the details of realistic networks matter in designing policies. 

Neighborhood eects have an important role in evacuation decision-making by a family. Owing to peer influence, neighbors evacuating can motivate a family to evacuate. Paradoxically, if a lot of neighbors evacuate, then the likelihood of an individual or family deciding to evacuate decreases, for fear of crime and looting. Such behavior cannot be captured using standard models of contagion spread on networks, e.g., threshold, independent cascade, and linear threshold models. Here, we propose a new threshold-based graph dynamical system model, 2mode-threshold, which captures this dichotomy. We study theoretically the dynamical properties of 2mode-threshold in different networks, and fi nd signi ficant differences from a standard threshold model. We build and characterize small world networks of Virginia Beach, VA, where nodes are geolocated families (households) in the city and edges are interactions between pairs of families. We demonstrate the utility of our behavioral model through agent-based simulations on these small world networks. We use it to understand evacuation rates in this region, and to evaluate the effects of modeling parameters on evacuation decision dynamics. Speci fically, we quantify the effects of (i) network generation parameters, (ii) stochasticity in the social network generation process, (iii) model types (2mode-threshold vs. stan- dard threshold models), (iv) 2mode-threshold model parameters, (v) and initial conditions, on computed evacuation rates and their variability. An illustrative example result shows that the absence of looting eect can overpredict evacuation rates by as much as 50%. 

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 eects of specifying large numbers of families as "early evacuators" in a contagion process, and also dominate effects of peer influence to evacuate. There is a strong network-based evacuation suppression effect from the fear of looting. We also study spatial factors aecting evacuation rates as well as policy interventions to encourage evacuation.