Managing a region's natural hazard risk while also meeting demands for new development can be challenging. New development supports local governments' tax revenues and other economic activities but may also lead to longer-term natural hazard losses if new construction is left unrestricted. Government entities commonly use land use policies to influence the location of new development. We developed a new land use policy analysis method that aims to assist planners and risk managers in disaster risk management by evaluating long-term housing development outcomes under different land use policy scenarios to reduce long-term natural hazard impacts. The computational method analyzes the spatial distribution of new housing, expected natural hazard losses, and property tax revenue impacts for a multidecadal projection period and multi-county region under alternative regional land use policy scenarios. While different risk management land use scenarios can be generated using this analysis method, we illustrate the method's usefulness by comparing baseline housing development patterns with three disaster risk management land use policy scenarios in eastern North Carolina. These 30-year projections can inform planners and risk managers about how land use decisions can alter their region and their state's vulnerabilities to natural hazard losses as well as the expected changes in property tax revenues.

When planning the construction of a new house, the decision of where and how to build are critical for long-term disaster resilience. In the United States, local government agencies often use land use policies to dictate where new construction takes place and building codes to specify how houses are constructed. This study compares disaster risk reduction land use policies, building code policies, and combinations thereof, in terms of the expected economic losses caused by hurricane damages among single-family housing units, as well as the expected impact on property tax revenues. This multi-county level analysis is conducted for new single-family housing construction over a 30-year projection period in eastern North Carolina. Findings indicate that a land use policy that limits new housing construction in high-risk areas and a building code policy that aligns with the FORTIFIED GoldTM construction standard lead to approximately the same loss reductions after a 30-year period. However, the building code policy leads to minimal changes in property tax revenue. Additionally, a combination of these land use and building code strategies can double the expected loss reduction in the region. However, the political challenges of enacting such policies would need to be addressed.

This paper presents the Stakeholder-Based Tool for the Analysis of Regional Risk (STARR), a dynamic, stochastic computational framework designed to inform the creation and analysis of government policies for regional disaster risk management. STARR consists of seven interacting modules. Three describe the decision-making of, respectively, government agencies, insurers, and households; four (hazard, damage and loss, buildings, economy) describe the natural, built, and economic environments in which those decisions are made. The tool is intended to (1) support policymaking by facilitating development, evaluation, and comparison of possible disaster risk management policies; (2) facilitate understanding of the dynamic system of regional disaster risk management, including interactions among stakeholder actions and the effects of changes in the context or assumptions; and (3) guide future research in a way that tightly integrates social science, physical science, and engineering contributions, demonstrating the interrelation among research advances, building on previous research, and identifying lingering gaps in knowledge. Just as regional loss estimation or catastrophe (cat) models have guided a generation of disaster risk analysis research, by extending such loss models to be dynamic and to include stakeholder decision-making modules, the STARR framework can facilitate future disaster research and practice in a way that acknowledges the decision-making required to make real change and thus overcomes some of the barriers preventing implementation of risk reduction strategies in the real world. Although it is possible to extend the framework to consider other hazards and stakeholders, STARR currently focuses on hurricanes and on households and housing.

Hurricanes significantly harm homeowners through physical damage and long-term financial strain due to rising insurance costs, property value loss, and repair expenses. This paper focuses on the interrelated decisions of the government mitigation funding of residential acquisitions and retrofit subsidies and of price restrictions on the insurance market in eastern North Carolina to determine the financial effects on stakeholders. The introduction of these policy interventions have impacts that propagate through the system due to risk adjustments, homeowner take-up behaviour, and insurer profit-maximising behaviour. This study uses an integrated game theoretic model to demonstrate that there are cost-effective government spending levels that reduce residential loss from hurricane damage. When insurance prices are capped at preintervention levels, the number of households and their distribution of losses, which has been altered through mitigation, leads to increased insurer insolvency. When insurance prices are allowed to adjust after mitigation, some homeowners find insurance is no longer affordable. This highlights the tradeoff between ensuring insurer stability and expanding homeowner insurance accessibility.

Sources of disaster resilience represent important (but understudied) dimensions of the interplay between immigrants and disasters, as do immigrants' disaster response activities. Using key informant interviews, we examine immigrant faith-based organizations' (FBO) responses to two contemporary pandemics. Additionally, we assess for the presence of disaster-relevant social capital in immigrant FBOs. FBOs were found to possess key components of social capital and to actively engage in pandemic response activities, including provision of health risk communication, education, leadership, infection control measures, cash and in-kind contributions, advocacy, and psychosocial support. For immigrant communities, FBO-based social capital contributes to effective disaster and pandemic responses.

Phased evacuation is an under-studied strategy, and relatively little is known about compliance with the phased process. This study modelled households’ responses to a phased evacuation order based on a household behavioral intention survey. About 66% of the evacuees reported that they would comply with a phased evacuation order. A latent class logit model sorted evacuees into two classes (“evacuation reluctant” and “evacuation keen”) by their stakeholder perceptions (i.e., whether government agencies have responsibility for the safety of individuals) and evacuation perceptions (i.e., whether evacuation is an effective protective action), while risk perception becomes non-significant in interpreting their compliance behavior to a phased evacuation order. Those that evacuate to the home of friends/relatives and/or bring more vehicles during evacuation are less likely to follow phased evacuation orders. “Evacuation reluctant” individuals with a longer housing tenure are more likely to follow phased evacuation orders. “Evacuation keen” individuals with a longer travel delay expectation are more likely to comply with phased evacuation orders. This study not only unveiled the impacts of incorporating three psychological perceptions (i.e., risk, stakeholder, and evacuation perceptions) in modeling compliance behavior (e.g., parameter sign/significance shift) but also provides insights of evacuees’ compliance behavior to phased evacuation orders.

Today’s regional natural hazards loss models rarely incorporate changes in a region’s built environment over time, and thus likely misestimate a region’s natural hazard risk. Of the existing natural hazard loss models that incorporate changes in the built environment, none are developed at an adequately granular spatiotemporal scale that is appropriate for regional (multi-county) natural hazards loss modeling. This work presents the new Housing Inventory Projection (HIP) method for estimating changes in a region’s housing inventory for natural hazards loss modeling purposes. The method includes two modules: (1) the Regional Annual County-Level Housing module, which estimates the annual number of housing units per county over a multi-county region and multi-decadal projection period, and (2) the Single-family Location Estimation module, which estimates the likely location of future single-family housing units across a subcounty grid space. While the HIP method can be applied over a range of spatiotemporal scales, we present a case study that estimates the number of single-family houses per 1 km 2 grid cell in the state of North Carolina for each year from 2020 to 2049. We then used these projections to estimate how a future housing stock would experience a Hurricane Florence-type event. Future housing projections suggest that between 2020 and 2049, nearly 2900 new houses will be built, each year, in areas that experienced at least two feet of flooding following Hurricane Florence.

Emergency managers have the important responsibility of planning and implementing mitigation policies and programs to reduce losses to life and property. To accomplish these goals, they must use limited time and resources to ensure the communities they serve have adequately mitigated against potential disasters. As a result, it is common to collaborate and coordinate with a wide variety of partner agencies and community organizations. While it is well established that strengthening relationships and increasing familiarity improve coordination, this article advances that narrative by providing direct insights on the ways a select group of local, state, and federal emergency managers view relationships with other mitigation stakeholders. Using insights from a 1-day workshop hosted at the University of Delaware to gather information from mitigation stakeholders, this article provides a discussion of commonalities and challenges workshop participants identified with other stakeholder groups. These insights can inform other emergency managers about potential collaborators and coordination opportunities with similar stakeholders in their own communities.

During evacuations, households make a number of important, related choices including accommodation type, destination, and departure time. They may make trade-offs among these choices where one decision affects the others. The analysis models the linkages among these three aforementioned choices using data from a household behavioral intention survey conducted in 2017 in the Hampton Roads, VA area. Statistical tests and a theoretical basis show that the approach that best fits the dataset was to estimate the three choices in a sequence, where the first decision serves as an independent variable in the next choice process. To model the sequence, we began by modeling accommodation choice using a multinomial logit (MNL) model. Next, the accommodation choice decisions were used with other control variables to estimate destination choice in a second MNL model. Last, evacuation distance (related to destination decisions) was used in a Cox proportional-hazards model to estimate departure time choices. The models that provide the best estimates included the following control variables that help explain the sequence of decisions residents in the Hampton Roads area expect to make: (1) a variable expressing residential stability helps explain accommodation choice; (2) prior evacuation experience, the geographic location of a household, and the duration of living in the area help predict the destination choice; and (3) distance to the chosen destination helps predict departure time. Findings from this study provide evidence that the decisions associated with these three choices influence each other and help emergency managers identify additional actions that potentially can improve the evacuation experiences of local residents.

Regional hurricane risk is often assessed assuming a static housing inventory, yet a region's housing inventory changes continually. Failing to include changes in the built environment in hurricane risk modeling can substantially underestimate expected losses. This study uses publicly available data and a long short-term memory (LSTM) neural network model to forecast the annual number of housing units for each of 1000 individual counties in the southeastern United States over the next 20 years. When evaluated using testing data, the estimated number of housing units was almost always (97.3 % of the time), no more than 1 percentage point different than the observed number, predictive errors that are acceptable for most practical purposes. Comparisons suggest the LSTM outperforms the autoregressive integrated moving average (ARIMA) and simpler linear trend models. The housing unit projections can help facilitate a quantification of changes in future expected losses and other impacts caused by hurricanes. For example, this study finds that if a hurricane with characteristics similar to Hurricane Harvey were to impact southeastern Texas in 20 years, the residential property and flood losses would be nearly USD 4 billion (38 %) greater due to the expected increase of 1.3 million new housing units (41 %) in the region.

We develop a computational framework for the stochastic and dynamic modeling of regional natural catastrophe losses with an insurance industry to support government decision-making for hurricane risk management. The analysis captures the temporal changes in the building inventory due to the acquisition (buyouts) of high-risk properties and the vulnerability of the building stock due to retrofit mitigation decisions. The system is comprised of a set of interacting models to (1) simulate hazard events; (2) estimate regional hurricane-induced losses from each hazard event based on an evolving building inventory; (3) capture acquisition offer acceptance, retrofit implementation, and insurance purchase behaviors of homeowners; and (4) represent an insurance market sensitive to demand with strategically interrelated primary insurers. This framework is linked to a simulation-optimization model to optimize decision-making by a government entity whose objective is to minimize region-wide hurricane losses. We examine the effect of different policies on homeowner mitigation, insurance take-up rate, insurer profit, and solvency in a case study using data for eastern North Carolina. Our findings indicate that an approach that coordinates insurance, retrofits, and acquisition of high-risk properties effectively reduces total (uninsured and insured) losses.

Effective evacuation management plans can help reduce the negative impacts of disasters. Understanding evacuee travel behavior is critical for the design of evacuation plans. In this paper, we explore which factors contribute to evacuees selecting freeway vs. non-freeway evacuation routes. Freeways are of particular interest due to their ability to evacuate large volumes of traffic. This study used survey data collected for the Hampton Roads region of Virginia. Respondents were asked to provide their preferred route types in the event of a hypothetical Category 4 hurricane evacuation. A mixed (random parameters) logit model was proposed to determine factors that influence evacuees selecting between freeway and non-freeway route. The study found that several factors contribute to evacuees choosing a freeway over other routes. In the descending order of importance (i.e., marginal effects), these factors are: willingness to use the official recommended route, living in a single-family or duplex housing, expected travel time to reach the destination, being employed, and possessing prior evacuation experience. Conversely, a few factors had a negative effect on choosing a freeway. These factors are: willingness to evacuate two days prior to landfall and evacuating to a public shelter or a second home. The findings of this study can help emergency management and transportation agencies design effective traffic control plans to safely evacuate populations during a hurricane.

The science of resilience presents the opportunity to explain how natural, social, and physical systems interact to impact community functioning and well-being postdisaster. This paper describes the development and theoretical foundation of a comprehensive conceptual model, presenting a shift from the usual thinking about resilience to construe resilience more precisely as the trajectory of postdisaster recovery, with community functioning and well-being as the outcome of interest. Unique contributions of the results include the identification of the natural, social, and physical systems that are implicated in disasters, and the dynamic nature and directionality of how these elements relate in the context of hazards. The model represents the integrated and interdependent nature of the natural, social, and technical systems that influence community functioning, and resistance to and recovery from disasters. We argue that an integrated and interdependent model of community resilience can benefit scholars building theories of disaster and policymakers who need a guide for navigating the complex disaster environment. The paper concludes with a discussion of how the model is used in practice.

This study provides insights from individuals working to reestablish permanent housing in Sea Bright, NJ, following Hurricane Sandy. To collect these perspectives, we gathered data in two ways: a self-administered questionnaire and semi-structured interviews. We mailed questionnaires to every household in Sea Bright that included a number of fixed response items and open-ended questions that focused on the interface of government and citizens to discuss their housing recovery process and problems or pitfalls they have encountered while recovering. To complement our questionnaire data, we conducted interviews with full-time residents, part-time residents, homeowners, renters, and representatives of the local, state, and federal government. We utilized conventional content analysis methods to discover sociological themes, focusing on the underlying behaviors, actions, and emotions the text portrayed. Several powerful themes emerged from our analysis of the open-ended questions and interviews. Most notably, we found a fundamental disconnect between how policymakers and homeowners viewed the housing recovery process. In particular, survivors highlighted the amount of and complexity-laden paperwork associated with the aid process, unfavorable interactions with government employees and a system that seemed to have goals that were inherently different from their own, and the sense that the rules associated with aid were constantly changing. We conclude with a discussion of additional research needs and some preliminary policy recommendations based on these insights.

Regional hurricane risk is often assessed assuming a static housing inventory, yet a region's housing inventory changes continually. Failing to include changes in the built environment in hurricane risk modeling can substantially underestimate expected losses. This study uses publicly available data and a long short-term memory (LSTM) neural network model to forecast the annual number of housing units for each of 1000 individual counties in the southeastern United States over the next 20 years. When evaluated using testing data, the estimated number of housing units was almost always (97.3 % of the time), no more than 1 percentage point different than the observed number, predictive errors that are acceptable for most practical purposes. Comparisons suggest the LSTM outperforms the autoregressive integrated moving average (ARIMA) and simpler linear trend models. The housing unit projections can help facilitate a quantification of changes in future expected losses and other impacts caused by hurricanes. For example, this study finds that if a hurricane with characteristics similar to Hurricane Harvey were to impact southeastern Texas in 20 years, the residential property and flood losses would be nearly USD 4 billion (38 %) greater due to the expected increase of 1.3 million new housing units (41 %) in the region.

We hypothesize that for disaster risk mitigation, many households, despite being aware of their risk and possible mitigation actions, never seriously consider doing anything about them. In mitigation-focused decisions, since there is no equivalent to warning messages, the decision process is likely to evolve over an extended time. We explore what activates hurricane mitigation protective action decisions through three research questions: (1) to what extent are homeowners unengaged in protective action decision making? (2) What homeowner characteristics are associated with lack of engagement? And (3) to what extent do different life events trigger engagement in the decision-making process? We use the Precaution Adoption Process Model to conceptualize engagement as distinct from decision making; the broader protective action decision-making literature to explore drivers of engagement; and Life Course Theory to examine potential transitions from unengaged to engaged. We use survey data of homeowners in North Carolina to examine these questions empirically. Findings suggest that one-third of respondents had never engaged in protective action decisions, that life experiences differ in their occurrence frequency and effect on households’ mitigation decisions, and that some events, such as renovating, reroofing, or purchasing a home may offer critical moments that could be leveraged to encourage greater engagement in mitigation decision making.

The Incident Command System (ICS), since its establishment in the 1980s, has been repeatedly discussed with the focuses on its pros and cons. These discussions are related to the benefits and limitations of using a mechanistic system. ICS proponents like its mechanistic design elements to command and control all responders. ICS critics, however, regard ICS mechanistic elements as hurdles to managing disaster response activities, and thus, they propose using more organic elements to design a new response system. Organizational theorists say the two types of systems are not dichotomous. It is possible the ICS has some organic design elements, and thus, cannot be treated as a pure command and control system. This research aims to explore to what degree the ICS is organic versus mechanistic. The researchers will present their analyses of two official ICS documents and three ICS online training courses, which indicates the ICS possesses both organic and mechanistic features. Results of the content analysis also demonstrate some limitations of using the ICS and show why there are so many different viewpoints toward this system.

Understanding how homeowners make protective action decisions is important for designing policies and programs to encourage those actions and community resilience as a whole. This paper focuses on the role of homeowner perceptions of attributes of the protective actions themselves in influencing household protective action decisions. Specifically, using a combination of revealed and stated preference data from a mailed survey of homeowners in North Carolina ( n = 234), we fitted mixed logit models to predict the probability a homeowner has or intends to structurally retrofit (strengthen) her home to mitigate hurricane wind and flood damage. We found evidence supporting the hypotheses that a higher probability of undertaking a retrofit is associated with homeowner beliefs that: (1) The retrofit cost is not too high, (2) the installation does not require too much effort, (3) they understand how it works, (4) it would add to home value, (5) it would protect lives, (6) it would protect property, and (7) it would not make the home less attractive. This work shows that homeowners make retrofit decisions based on a portfolio of perceived attributes that depend on the type of retrofit under consideration. Although cost is important, other factors carry considerable weight in the decision as well. Further, findings suggest that study of one type of protective action (e.g., having an emergency kit) may not be generalizable to other actions (adding hurricane shutters) without considering these attributes.