Development of an Asset Value Map for Disaster Risk Assessment in China by Spatial Disaggregation Using Ancillary Remote Sensing Data

The extent of economic losses due to a natural hazard and disaster depends largely on the spatial distribution of asset values in relation to the hazard intensity distribution within the affected area. Given that statistical data on asset value are collected by administrative units in China, generating spatially explicit asset exposure maps remains a key challenge for rapid postdisaster economic loss assessment. The goal of this study is to introduce a top‐down (or downscaling) approach to disaggregate administrative‐unit level asset value to grid‐cell level. To do so, finding the highly correlated “surrogate” indicators is the key. A combination of three data sets—nighttime light grid, LandScan population grid, and road density grid, is used as ancillary asset density distribution information for spatializing the asset value. As a result, a high spatial resolution asset value map of China for 2015 is generated. The spatial data set contains aggregated economic value at risk at 30 arc‐second spatial resolution. Accuracy of the spatial disaggregation reflects redistribution errors introduced by the disaggregation process as well as errors from the original ancillary data sets. The overall accuracy of the results proves to be promising. The example of using the developed disaggregated asset value map in exposure assessment of watersheds demonstrates that the data set offers immense analytical flexibility for overlay analysis according to the hazard extent. This product will help current efforts to analyze spatial characteristics of exposure and to uncover the contributions of both physical and social drivers of natural hazard and disaster across space and time.     

Developing a multi-level organization-public dialogic communication framework to assess social media-mediated disaster communication and engagement outcomes

Dialogic communication has long been viewed as vital for effective organization-public relations. Yet, it is under-theorized whether and how organizations’ disaster communication messages may embody dialogic communication principles, and how various dialogic features are associated with different public engagement outcomes on social media. Extending the Organization-Public Dialogic Communication (OPDC) framework to the context of social media-mediated disaster communication, we propose a multi-level framework to assess the dialogic capacity of Facebook messages sent by disaster management organizations during a natural disaster. Three levels of dialogic communication characteristics (i.e., message structure-level, topic-level, and linguistic level) are examined using content analysis and Linguistic Inquiry and Word Count (LIWC). Results identified media richness, correcting, and confirming topics as three consistent predictors of public engagement of all types. Meanwhile, there exhibit greater variations regarding how other topical features and linguistic characteristics are related to public’s cognitive, emotional, and behavioral engagement during a disaster.     

Disaster Relief as Social Action: A Weberian Look at Postdisaster Donation Behavior

Disaster relief involves a broad range of participants, which often results in a disjunction between actual disaster needs and the perception of appropriate strategies in relief provision. This research examines data collected through interviews with donation drive operators responding to Superstorm Sandy in 2012 and the May 2013 Oklahoma tornadoes. Using Weber's ideal types of social action to understand motivations for engaging in disaster relief, this article argues that motivations for participating in and organizing such drives influence donation strategies, and these motivations can be used to explain persistent donation of nonuseful materiel items. Findings indicate several different motivations for involvement and that donation type appeared linked to motivation. We suggest that motivation for engaging in social action is a valuable approach to better understanding donation behavior and, consequently, addressing the social problem of materiel convergence in the postdisaster environment.     

Aesthetics of disaster as decolonial aesthetics: making sense of the effects of Hurricane María through Puerto Rican contemporary art

ABSTRACT

On 20 September 2017, Hurricane María made landfall on Puerto Rico causing unprecedented disaster. From that day onwards, the Puerto Rican multi-layered colonial, social and political context was further complicated by the traumatic acceleration of a human disaster via this natural disaster. This crystalized the urgency of using art as vehicle for (social) catharsis, a practice that continues to be used by individual artists, collectives, community organizations, art projects, and other art institutions on the island and abroad, through mural art, community paintings, art exhibitions, literature, music, and many other aesthetic expressions. This article examines, from a decolonial and critical cultural studies perspective, post-Hurricane María artistic expressions in contemporary art as decolonial aesthetics through the cathartic use of the frame of an aesthetics of disaster. It is argued that, an aesthetics of disaster aims to re-assert an artistic form that is able to accelerate the discursive nullification of a deeply rooted colonial, social and cultural problem by way of art as catharsis inspired by the way that Hurricane María unveiled these problems. The piece briefly contextualizes Puerto Rico, and it examines the idea of Puerto Rican contemporary art as catharsis. Then, it describes how Puerto Rican contemporary art exhibitions and associated aesthetic production are processing urgent post-hurricane issues through three illustrative pieces in exhibitions in PR and abroad in the United States (US). Lastly, decolonial aesthetics is reexamined and re-understood, informed by Édouard Glissant's view expressed in Poetics of Relation which aids to the conclusion that Puerto Rican contemporary art using the frame of an aesthetics of disaster functions as a powerful form of decolonial aesthetics.     

Agent‐Based Recovery Model for Seismic Resilience Evaluation of Electrified Communities

In this article, an agent‐based framework to quantify the seismic resilience of an electric power supply system (EPSS) and the community it serves is presented. Within the framework, the loss and restoration of the EPSS power generation and delivery capacity and of the power demand from the served community are used to assess the electric power deficit during the damage absorption and recovery processes. Damage to the components of the EPSS and of the community‐built environment is evaluated using the seismic fragility functions. The restoration of the community electric power demand is evaluated using the seismic recovery functions. However, the postearthquake EPSS recovery process is modeled using an agent‐based model with two agents, the EPSS Operator and the Community Administrator. The resilience of the EPSS–community system is quantified using direct, EPSS‐related, societal, and community‐related indicators. Parametric studies are carried out to quantify the influence of different seismic hazard scenarios, agent characteristics, and power dispatch strategies on the EPSS–community seismic resilience. The use of the agent‐based modeling framework enabled a rational formulation of the postearthquake recovery phase and highlighted the interaction between the EPSS and the community in the recovery process not quantified in resilience models developed to date. Furthermore, it shows that the resilience of different community sectors can be enhanced by different power dispatch strategies. The proposed agent‐based EPSS–community system resilience quantification framework can be used to develop better community and infrastructure system risk governance policies.     

Bi-objective multi-resource scheduling problem for emergency relief operations

Abstract

Resource scheduling for emergency relief operations is complex as it has many constraints. However, an effective allocation and sequencing of resources are crucial for the minimization of the completion times in emergency relief operations. Despite the importance of such decisions, only a few mathematical models of emergency relief operations have been studied. This article presents a bi-objective mixed integer programming (MIP) that helps to minimize both the total weighted time of completion of the demand points and the makespan of the total emergency relief operation. A two-phase method is developed to solve the bi-objective MIP problem. Additionally, a case study of hospital network in the Melbourne metropolitan area is used to evaluate the model. The results indicate that the model can successfully support the decisions required in the optimal resource scheduling of emergency relief operations.     

Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

考虑时效与公平性的震后应急物资动态配送优化研究

大规模地震后应急物资的高效保障是应急救援有效开展的必要前提。震后应急物资运输与配送包括从区域储备仓库和外围物资集散地运输到区域应急配送中心、由区域应急配送中心配送到各个受灾点两个阶段。应急物资保障具有持续时间长、需求紧迫性强和物资相对短缺等特点。本文以应急物资保障时效性与分配公平性为目标，建立了考虑多种运输方式、多时段动态的应急物资配送中心选址与运输配送路径优化的多目标规划模型，并基于此模型设计了一种带精英策略的非支配排序的遗传算法，以九寨沟地区地震灾难情景对模型进行实证研究，验证了模型和算法的有效性，以期为震后应急物资保障提供理论指导与决策支持。     

The disaster relief and social work after the Wenchuan earthquake in Mainland China

ABSTRACT

This paper mainly focuses on the development of disaster social work in Mainland China and the intervention of social work in disaster relief. Before the Wenchuan earthquake and in the initial stage of post-earthquake, disaster social work was mainly based on individual psychotherapy; from the earthquake to the year of 2012 in which post-earthquake recovery and reconstruction was completed, the disaster relief began to emphasise community building and integration, while the basic framework for disaster social work was also established. Social workers begin to explore the new mode of developmental and localised disaster social work. By combining with the practical experiences from disaster social work, this paper tries to highlight the dilemmas confronting disaster relief in Mainland China and put forward some corresponding countermeasures and suggestions, which could improve the future disaster relief system in Mainland China.     

Tiered Approach to Resilience Assessment

Regulatory agencies have long adopted a three‐tier framework for risk assessment. We build on this structure to propose a tiered approach for resilience assessment that can be integrated into the existing regulatory processes. Comprehensive approaches to assessing resilience at appropriate and operational scales, reconciling analytical complexity as needed with stakeholder needs and resources available, and ultimately creating actionable recommendations to enhance resilience are still lacking. Our proposed framework consists of tiers by which analysts can select resilience assessment and decision support tools to inform associated management actions relative to the scope and urgency of the risk and the capacity of resource managers to improve system resilience. The resilience management framework proposed is not intended to supplant either risk management or the many existing efforts of resilience quantification method development, but instead provide a guide to selecting tools that are appropriate for the given analytic need. The goal of this tiered approach is to intentionally parallel the tiered approach used in regulatory contexts so that resilience assessment might be more easily and quickly integrated into existing structures and with existing policies.