Near‐Misses and Future Disaster Preparedness

Disasters garner attention when they occur, and organizations commonly extract valuable lessons from visible failures, adopting new behaviors in response. For example, the United States saw numerous security policy changes following the September 11 terrorist attacks and emergency management and shelter policy changes following Hurricane Katrina. But what about those events that occur that fall short of disaster? Research that examines prior hazard experience shows that this experience can be a mixed blessing. Prior experience can stimulate protective measures, but sometimes prior experience can deceive people into feeling an unwarranted sense of safety. This research focuses on how people interpret near‐miss experiences. We demonstrate that when near‐misses are interpreted as disasters that did not occur and thus provide the perception that the system is resilient to the hazard, people illegitimately underestimate the danger of subsequent hazardous situations and make riskier decisions. On the other hand, if near‐misses can be recognized and interpreted as disasters that almost happened and thus provide the perception that the system is vulnerable to the hazard, this will counter the basic “near‐miss” effect and encourage mitigation. In this article, we use these distinctions between resilient and vulnerable near‐misses to examine how people come to define an event as either a resilient or vulnerable near‐miss, as well as how this interpretation influences their perceptions of risk and their future preparedness behavior. Our contribution is in highlighting the critical role that people's interpretation of the prior experience has on their subsequent behavior and in measuring what shapes this interpretation.     

Florida Households’ Expected Responses to Hurricane Hazard Mitigation Incentives

This study tested a series of models predicting household expectations of participating in hurricane hazard mitigation incentive programs. Data from 599 households in Florida revealed that mitigation incentive adoption expectations were most strongly and consistently related to hazard intrusiveness and risk perception and, to a lesser extent, worry. Demographic and hazard exposure had indirect effects on mitigation incentive adoption expectations that were mediated by the psychological variables. The results also revealed differences in the factors affecting mitigation incentive adoption expectations for each of five specific incentive programs. Overall, the results suggest that hazard managers are more likely to increase participation in mitigation incentive programs if they provide messages that repeatedly (thus increasing hazard intrusiveness) remind people of the likelihood of severe negative consequences of hurricane impact (thus increasing risk perception).     

Sizing Up a Superstorm: Exploring the Role of Recalled Experience and Attribution of Responsibility in Judgments of Future Hurricane Risk

Research suggests that hurricane‐related risk perception is a critical predictor of behavioral response, such as evacuation. Less is known, however, about the precursors of these subjective risk judgments, especially when time has elapsed from a focal event. Drawing broadly from the risk communication, social psychology, and natural hazards literature, and specifically from concepts adapted from the risk information seeking and processing model and the protective action decision model, we examine how individuals’ distant recollections, including attribution of responsibility for the effects of a storm, attitude toward relevant information, and past hurricane experience, relate to risk judgment for a future, similar event. The present study reports on a survey involving U.S. residents in Connecticut, New Jersey, and New York (n = 619) impacted by Hurricane Sandy. While some results confirm past findings, such as that hurricane experience increases risk judgment, others suggest additional complexity, such as how various types of experience (e.g., having evacuated vs. having experienced losses) may heighten or attenuate individual‐level judgments of responsibility. We suggest avenues for future research, as well as implications for federal agencies involved in severe weather/natural hazard forecasting and communication with public audiences.     

The Effects of Revealed Information on Catastrophe Loss Projection Models’ Characterization of Risk: Damage Vulnerability Evidence from Florida

We examine whether the risk characterization estimated by catastrophic loss projection models is sensitive to the revelation of new information regarding risk type. We use commercial loss projection models from two widely employed modeling firms to estimate the expected hurricane losses of Florida Atlantic University's building stock, both including and excluding secondary information regarding hurricane mitigation features that influence damage vulnerability. We then compare the results of the models without and with this revealed information and find that the revelation of additional, secondary information influences modeled losses for the windstorm‐exposed university building stock, primarily evidenced by meaningful percent differences in the loss exceedance output indicated after secondary modifiers are incorporated in the analysis. Secondary risk characteristics for the data set studied appear to have substantially greater impact on probable maximum loss estimates than on average annual loss estimates. While it may be intuitively expected for catastrophe models to indicate that secondary risk characteristics hold value for reducing modeled losses, the finding that the primary value of secondary risk characteristics is in reduction of losses in the “tail” (low probability, high severity) events is less intuitive, and therefore especially interesting. Further, we address the benefit‐cost tradeoffs that commercial entities must consider when deciding whether to undergo the data collection necessary to include secondary information in modeling. Although we assert the long‐term benefit‐cost tradeoff is positive for virtually every entity, we acknowledge short‐term disincentives to such an effort.     

Public Response to a Near‐Miss Nuclear Accident Scenario Varying in Causal Attributions and Outcome Uncertainty

Many studies have investigated public reactions to nuclear accidents. However, few studies focused on more common events when a serious accident could have happened but did not. This study evaluated public response (emotional, cognitive, and behavioral) over three phases of a near‐miss nuclear accident. Simulating a loss‐of‐coolant accident (LOCA) scenario, we manipulated (1) attribution for the initial cause of the incident (software failure vs. cyber terrorist attack vs. earthquake), (2) attribution for halting the incident (fail‐safe system design vs. an intervention by an individual expert vs. a chance coincidence), and (3) level of uncertainty (certain vs. uncertain) about risk of a future radiation leak after the LOCA is halted. A total of 773 respondents were sampled using a 3 × 3 × 2 between‐subjects design. Results from both MANCOVA and structural equation modeling (SEM) indicate that respondents experienced more negative affect, perceived more risk, and expressed more avoidance behavioral intention when the near‐miss event was initiated by an external attributed source (e.g., earthquake) compared to an internally attributed source (e.g., software failure). Similarly, respondents also indicated greater negative affect, perceived risk, and avoidance behavioral intentions when the future impact of the near‐miss incident on people and the environment remained uncertain. Results from SEM analyses also suggested that negative affect predicted risk perception, and both predicted avoidance behavior. Affect, risk perception, and avoidance behavior demonstrated high stability (i.e., reliability) from one phase to the next.     

Airline Safety Improvement Through Experience with Near‐Misses: A Cautionary Tale

In recent years, the U.S. commercial airline industry has achieved unprecedented levels of safety, with the statistical risk associated with U.S. commercial aviation falling to 0.003 fatalities per 100 million passengers. But decades of research on organizational learning show that success often breeds complacency and failure inspires improvement. With accidents as rare events, can the airline industry continue safety advancements? This question is complicated by the complex system in which the industry operates where chance combinations of multiple factors contribute to what are largely probabilistic (rather than deterministic) outcomes. Thus, some apparent successes are realized because of good fortune rather than good processes, and this research intends to bring attention to these events, the near‐misses. The processes that create these near‐misses could pose a threat if multiple contributing factors combine in adverse ways without the intervention of good fortune. Yet, near‐misses (if recognized as such) can, theoretically, offer a mechanism for continuing safety improvements, above and beyond learning gleaned from observable failure. We test whether or not this learning is apparent in the airline industry. Using data from 1990 to 2007, fixed effects Poisson regressions show that airlines learn from accidents (their own and others), and from one category of near‐misses—those where the possible dangers are salient. Unfortunately, airlines do not improve following near‐miss incidents when the focal event has no clear warnings of significant danger. Therefore, while airlines need to and can learn from certain near‐misses, we conclude with recommendations for improving airline learning from all near‐misses.     

An Agent‐Based Model of Evolving Community Flood Risk

Although individual behavior plays a major role in community flood risk, traditional flood risk models generally do not capture information on how community policies and individual decisions impact the evolution of flood risk over time. The purpose of this study is to improve the understanding of the temporal aspects of flood risk through a combined analysis of the behavioral, engineering, and physical hazard aspects of flood risk. Additionally, the study aims to develop a new modeling approach for integrating behavior, policy, flood hazards, and engineering interventions. An agent‐based model (ABM) is used to analyze the influence of flood protection measures, individual behavior, and the occurrence of floods and near‐miss flood events on community flood risk. The ABM focuses on the following decisions and behaviors: dissemination of flood management information, installation of community flood protection, elevation of household mechanical equipment, and elevation of homes. The approach is place based, with a case study area in Fargo, North Dakota, but is focused on generalizable insights. Generally, community mitigation results in reduced future damage, and individual action, including mitigation and movement into and out of high‐risk areas, can have a significant influence on community flood risk. The results of this study provide useful insights into the interplay between individual and community actions and how it affects the evolution of flood risk. This study lends insight into priorities for future work, including the development of more in‐depth behavioral and decision rules at the individual and community level.     

Understanding Household Preferences for Hurricane Risk Mitigation Information: Evidence from Survey Responses

Risk information is critical to adopting mitigation measures, and seeking risk information is influenced by a variety of factors. An essential component of the recently adopted My Safe Florida Home (MSFH) program by the State of Florida is to provide homeowners with pertinent risk information to facilitate hurricane risk mitigation activities. We develop an analytical framework to understand household preferences for hurricane risk mitigation information through allowing an intensive home inspection. An empirical analysis is used to identify major drivers of household preferences to receive personalized information regarding recommended hurricane risk mitigation measures. A variety of empirical specifications show that households with home insurance, prior experience with damages, and with a higher sense of vulnerability to be affected by hurricanes are more likely to allow inspection to seek information. However, households with more members living in the home and households who live in manufactured/mobile homes are less likely to allow inspection. While findings imply MSFH program's ability to link incentives offered by private and public agencies in promoting mitigation, households that face a disproportionately higher level of risk can get priority to make the program more effective.     

Individual learning as a driver of changes in community vulnerability under repeated hurricanes and changing climate

The risks from singular natural hazards such as a hurricane have been extensively investigated in the literature. However, little is understood about how individual and collective responses to repeated hazards change communities and impact their preparation for future events. Individual mitigation actions may drive how a community's resilience evolves under repeated hazards. In this paper, we investigate the effect that learning by homeowners can have on household mitigation decisions and on how this influences a region's vulnerability to natural hazards over time, using hurricanes along the east coast of the United States as our case study. To do this, we build an agent-based model (ABM) to simulate homeowners’ adaptation to repeated hurricanes and how this affects the vulnerability of the regional housing stock. Through a case study, we explore how different initial beliefs about the hurricane hazard and how the memory of recent hurricanes could change a community's vulnerability both under current and potential future hurricane scenarios under climate change. In some future hurricane environments, different initial beliefs can result in large differences in the region's long-term vulnerability to hurricanes. We find that when some homeowners mitigate soon after a hurricane—when their memory of the event is the strongest—it can help to substantially decrease the vulnerability of a community.     

An Integrated Scenario Ensemble-Based Framework for Hurricane Evacuation Modeling: Part 1—Decision Support System

This article introduces a new integrated scenario-based evacuation (ISE) framework to support hurricane evacuation decision making. It explicitly captures the dynamics, uncertainty, and human–natural system interactions that are fundamental to the challenge of hurricane evacuation, but have not been fully captured in previous formal evacuation models. The hazard is represented with an ensemble of probabilistic scenarios, population behavior with a dynamic decision model, and traffic with a dynamic user equilibrium model. The components are integrated in a multistage stochastic programming model that minimizes risk and travel times to provide a tree of evacuation order recommendations and an evaluation of the risk and travel time performance for that solution. The ISE framework recommendations offer an advance in the state of the art because they: (1) are based on an integrated hazard assessment (designed to ultimately include inland flooding), (2) explicitly balance the sometimes competing objectives of minimizing risk and minimizing travel time, (3) offer a well-hedged solution that is robust under the range of ways the hurricane might evolve, and (4) leverage the substantial value of increasing information (or decreasing degree of uncertainty) over the course of a hurricane event. A case study for Hurricane Isabel (2003) in eastern North Carolina is presented to demonstrate how the framework is applied, the type of results it can provide, and how it compares to available methods of a single scenario deterministic analysis and a two-stage stochastic program.     

Low‐Probability Flood Risk Modeling for New York City

The devastating impact by Hurricane Sandy (2012) again showed New York City (NYC) is one of the most vulnerable cities to coastal flooding around the globe. The low‐lying areas in NYC can be flooded by nor'easter storms and North Atlantic hurricanes. The few studies that have estimated potential flood damage for NYC base their damage estimates on only a single, or a few, possible flood events. The objective of this study is to assess the full distribution of hurricane flood risk in NYC. This is done by calculating potential flood damage with a flood damage model that uses many possible storms and surge heights as input. These storms are representative for the low‐probability/high‐impact flood hazard faced by the city. Exceedance probability‐loss curves are constructed under different assumptions about the severity of flood damage. The estimated flood damage to buildings for NYC is between US$59 and 129 millions/year. The damage caused by a 1/100‐year storm surge is within a range of US$2 bn–5 bn, while this is between US$5 bn and 11 bn for a 1/500‐year storm surge. An analysis of flood risk in each of the five boroughs of NYC finds that Brooklyn and Queens are the most vulnerable to flooding. This study examines several uncertainties in the various steps of the risk analysis, which resulted in variations in flood damage estimations. These uncertainties include: the interpolation of flood depths; the use of different flood damage curves; and the influence of the spectra of characteristics of the simulated hurricanes.     

Flood Risk,Flood Mitigation,and Location Choice: Evaluating the National Flood Insurance Program's Community Rating System

Climate change is expected to worsen the negative effects of natural disasters like floods. The negative impacts, however, can be mitigated by individuals’ adjustments through migration and relocation behaviors. Previous literature has identified flood risk as one significant driver in relocation decisions, but no prior study examines the effect of the National Flood Insurance Program's voluntary program—the Community Rating System (CRS)—on residential location choice. This article fills this gap and tests the hypothesis that flood risk and the CRS‐creditable flood control activities affect residential location choices. We employ a two‐stage sorting model to empirically estimate the effects. In the first stage, individuals’ risk perception and preference heterogeneity for the CRS activities are considered, while mean effects of flood risk and the CRS activities are estimated in the second stage. We then estimate heterogeneous marginal willingness to pay (WTP) for the CRS activities by category. Results show that age, ethnicity and race, educational attainment, and prior exposure to risk explain risk perception. We find significant values for the CRS‐creditable mitigation activities, which provides empirical evidence for the benefits associated with the program. The marginal WTP for an additional credit point earned for public information activities, including hazard disclosure, is found to be the highest. Results also suggest that water amenities dominate flood risk. Thus, high amenity values may increase exposure to flood risk, and flood mitigation projects should be strategized in coastal regions accordingly.     

An Assessment of Change in Risk Perception and Optimistic Bias for Hurricanes Among Gulf Coast Residents

This study focuses on levels of concern for hurricanes among individuals living along the Gulf Coast during the quiescent two‐year period following the exceptionally destructive 2005 hurricane season. A small study of risk perception and optimistic bias was conducted immediately following Hurricanes Katrina and Rita. Two years later, a follow‐up was done in which respondents were recontacted. This provided an opportunity to examine changes, and potential causal ordering, in risk perception and optimistic bias. The analysis uses 201 panel respondents who were matched across the two mail surveys. Measures included hurricane risk perception, optimistic bias for hurricane evacuation, past hurricane experience, and a small set of demographic variables (age, sex, income, and education). Paired t‐tests were used to compare scores across time. Hurricane risk perception declined and optimistic bias increased. Cross‐lagged correlations were used to test the potential causal ordering between risk perception and optimistic bias, with a weak effect suggesting the former affects the latter. Additional cross‐lagged analysis using structural equation modeling was used to look more closely at the components of optimistic bias (risk to self vs. risk to others). A significant and stronger potentially causal effect from risk perception to optimistic bias was found. Analysis of the experience and demographic variables’ effects on risk perception and optimistic bias, and their change, provided mixed results. The lessening of risk perception and increase in optimistic bias over the period of quiescence suggest that risk communicators and emergency managers should direct attention toward reversing these trends to increase disaster preparedness.     

The Effect of Proximity to Hurricanes Katrina and Rita on Subsequent Hurricane Outlook and Optimistic Bias

This study evaluated how individuals living on the Gulf Coast perceived hurricane risk after Hurricanes Katrina and Rita. It was hypothesized that hurricane outlook and optimistic bias for hurricane risk would be associated positively with distance from the Katrina‐Rita landfall (more optimism at greater distance), controlling for historically based hurricane risk and county population density, demographics, individual hurricane experience, and dispositional optimism. Data were collected in January 2006 through a mail survey sent to 1,375 households in 41 counties on the coast (n = 824, 60% response). The analysis used hierarchal regression to test hypotheses. Hurricane history and population density had no effect on outlook; individuals who were male, older, and with higher household incomes were associated with lower risk perception; individual hurricane experience and personal impacts from Katrina and Rita predicted greater risk perception; greater dispositional optimism predicted more optimistic outlook; distance had a small effect but predicted less optimistic outlook at greater distance (model R²= 0.21). The model for optimistic bias had fewer effects: age and community tenure were significant; dispositional optimism had a positive effect on optimistic bias; distance variables were not significant (model R²= 0.05). The study shows that an existing measure of hurricane outlook has utility, hurricane outlook appears to be a unique concept from hurricane optimistic bias, and proximity has at most small effects. Future extension of this research will include improved conceptualization and measurement of hurricane risk perception and will bring to focus several concepts involving risk communication.     

Developing a Broadly Applicable Measure of Risk Perception

Decades of research identify risk perception as a largely intuitive and affective construct, in contrast to the more deliberative assessments of probability and consequences that form the foundation of risk assessment. However, a review of the literature reveals that many of the risk perception measures employed in survey research with human subjects are either generic in nature, not capturing any particular affective, probabilistic, or consequential dimension of risk; or focused solely on judgments of probability. The goal of this research was to assess a multidimensional measure of risk perception across multiple hazards to identify a measure that will be broadly useful for assessing perceived risk moving forward. Our results support the idea of risk perception being multidimensional, but largely a function of individual affective reactions to the hazard. We also find that our measure of risk perception holds across multiple types of hazards, ranging from those that are behavioral in nature (e.g., health and safety behaviors), to those that are technological (e.g., pollution), or natural (e.g., extreme weather). We suggest that a general, unidimensional measure of risk may accurately capture one's perception of the severity of the consequences, and the discrete emotions that are felt in response to those potential consequences. However, such a measure is not likely to capture the perceived probability of experiencing the outcomes, nor will it be as useful at understanding one's motivation to take mitigation action.     

Hazard Experience,Geophysical Vulnerability,and Flood Risk Perceptions in a Postdisaster City,the Case of New Orleans

This article investigates the determinants of flood risk perceptions in New Orleans, Louisiana (United States), a deltaic coastal city highly vulnerable to seasonal nuisance flooding and hurricane‐induced deluges and storm surges. Few studies have investigated the influence of hazard experience, geophysical vulnerability (hazard proximity), and risk perceptions in cities undergoing postdisaster recovery and rebuilding. We use ordinal logistic regression techniques to analyze experiential, geophysical, and sociodemographic variables derived from a survey of 384 residents in seven neighborhoods. We find that residents living in neighborhoods that flooded during Hurricane Katrina exhibit higher levels of perceived risk than those residents living in neighborhoods that did not flood. In addition, findings suggest that flood risk perception is positively associated with female gender, lower income, and direct flood experiences. In conclusion, we discuss the implications of these findings for theoretical and empirical research on environmental risk, flood risk communication strategies, and flood hazards planning.     

Explainable deep learning powered building risk assessment model for proactive hurricane response

Climate change and rapid urban development have intensified the impact of hurricanes, especially on the Southeastern Coasts of the United States. Localized and timely risk assessments can facilitate coastal communities’ preparedness and response to imminent hurricanes. Existing assessment methods focused on hurricane risks at large spatial scales, which were not specific or could not provide actionable knowledge for residents or property owners. Fragility functions and other widely utilized assessment methods cannot model the complex relationships between building features and hurricane risk levels effectively. Therefore, we develop and test a building-level hurricane risk assessment with deep feedforward neural network (DFNN) models. The input features of DFNN models cover the meta building characteristics, fine-grained meteorological, and hydrological environmental parameters. The assessment outcomes, that is, risk levels, include the probability and intensity of building/property damages induced by wind and surge hazards. We interpret the DFNN models with local interpretable model-agnostic explanations (LIME). We apply the DFNN models to a case building in Cameron County, Louisiana in response to a hypothetical imminent hurricane to illustrate how the building's risk levels can be timely assessed with the updating weather forecast. This research shows the potential of deep-learning models in integrating multi-sourced features and accurately predicting buildings’ risks of weather extremes for property owners and households. The AI-powered risk assessment model can help coastal populations form appropriate and updating perceptions of imminent hurricanes and inform actionable knowledge for proactive risk mitigation and long-term climate adaptation.     

Asteroid Risk Assessment: A Probabilistic Approach

Following the 2013 Chelyabinsk event, the risks posed by asteroids attracted renewed interest, from both the scientific and policy‐making communities. It reminded the world that impacts from near‐Earth objects (NEOs), while rare, have the potential to cause great damage to cities and populations. Point estimates of the risk (such as mean numbers of casualties) have been proposed, but because of the low‐probability, high‐consequence nature of asteroid impacts, these averages provide limited actionable information. While more work is needed to further refine its input distributions (e.g., NEO diameters), the probabilistic model presented in this article allows a more complete evaluation of the risk of NEO impacts because the results are distributions that cover the range of potential casualties. This model is based on a modularized simulation that uses probabilistic inputs to estimate probabilistic risk metrics, including those of rare asteroid impacts. Illustrative results of this analysis are presented for a period of 100 years. As part of this demonstration, we assess the effectiveness of civil defense measures in mitigating the risk of human casualties. We find that they are likely to be beneficial but not a panacea. We also compute the probability—but not the consequences—of an impact with global effects (“cataclysm”). We conclude that there is a continued need for NEO observation, and for analyses of the feasibility and risk‐reduction effectiveness of space missions designed to deflect or destroy asteroids that threaten the Earth.     

Examining the Relationship Between Affect and Implicit Associations: Implications for Risk Perception

It has been suggested that affect may play an important role in risk perception. Slovic et al. argued that people make use of the “affect heuristic” when assessing risks because it is easier and more efficient to rely on spontaneous affective reactions than to analyze all available information. In the present studies, a single category implicit association test (SC‐IAT) to measure associations evoked by different hazards was employed. In the first study, we tested the extent to which the SC‐IAT corresponds to the theoretical construct of affect in a risk framework. Specifically, we found that the SC‐IAT correlates with other explicit measures that claim to measure affect, as well as with a measure of trust, but not with a measure that captures a different construct (subjective knowledge). In the second study, we addressed the question of whether hazards that vary along the dread dimension of the psychometric paradigm also differ in the affect they evoke. The results of the SC‐IAT indicated that a high‐dread hazard (nuclear power) elicits negative associations. Moreover, the high‐dread hazard evoked more negative associations than a medium‐dread hazard (hydroelectric power). In contrast, a nondread hazard (home appliances) led to positive associations. The results of our study highlight the importance of affect in shaping attitudes and opinions toward risks. The results further suggest that implicit measures may provide valuable insight into people's risk perception above and beyond explicit measures.     

People's Risk Recognition Preceding Evacuation and Its Role in Demand Modeling and Planning

Evacuation planning and management involves estimating the travel demand in the event that such action is required. This is usually done as a function of people's decision to evacuate, which we show is strongly linked to their risk awareness. We use an empirical data set, which shows tsunami evacuation behavior, to demonstrate that risk recognition is not synonymous with objective risk, but is instead determined by a combination of factors including risk education, information, and sociodemographics, and that it changes dynamically over time. Based on these findings, we formulate an ordered logit model to describe risk recognition combined with a latent class model to describe evacuation choices. Our proposed evacuation choice model along with a risk recognition class can evaluate quantitatively the influence of disaster mitigation measures, risk education, and risk information. The results obtained from the risk recognition model show that risk information has a greater impact in the sense that people recognize their high risk. The results of the evacuation choice model show that people who are unaware of their risk take a longer time to evacuate.