Search

A major challenge in enhancing the resilience of communities stems from current approaches used to identify needs and strategies that build the capacity of jurisdictions to mitigate loss and improve recovery. A new generation of resilience-based planning processes has emerged in the last several years that integrate goals of community well-being and identity into recovery-based performance measurement frameworks. Specific tools and refined guidance are needed to facilitate evidence-based development of recovery estimates. This article presents the participatory modeling process, a planning system designed to develop recovery-based resilience measurement frameworks for community resilience planning initiatives. Stakeholder engagement is infused throughout the participatory modeling process by integrating disaster recovery simulation modeling into community resilience planning. Within the process, participants get a unique opportunity to work together to deliberate on community concerns through facilitated participatory modeling. The participatory modeling platform combines the DESaster recovery simulation model and visual analytics interfaces. DESaster is an open source Python Library for creating discrete event simulations of disaster recovery. The simulation model was developed using a human-centered design approach whose goal is to be open, modular, and extensible. The process presented in this article is the first participatory modeling approach for analyzing recovery to aid creation of community resilience measurement frameworks.

The U.S. National Institute of Standards and Technology (NIST)’s Community Resilience Planning Guide uses recovery times of infrastructure functions as key metrics for disaster resilience. The existing literature also widely uses the recovery curve and the area under it to measure infrastructure resilience. Therefore, infrastructure recovery curve estimation is critical to understanding and improving disaster resilience. Unfortunately, this process is challenging in the pre-event planning context due to lack of historical data. To bridge this gap, we consider a situation where infrastructure experts are asked to estimate the time for different infrastructure systems to recover to certain functionality levels after a scenario hazard event. We propose a methodological framework to use expert-elicited data to estimate the expected recovery time curve of a particular infrastructure system. This framework uses the Gaussian process regression (GPR) to capture the experts’ estimation-uncertainty and satisfy known physical constraints of recovery processes. The framework is designed to find a balance between the data collection cost of expert elicitation and the prediction accuracy of GPR. We evaluate the framework on simulated expert-elicited data concerning two case study events, the 1995 Great Hanshin-Awaji Earthquake and the 2011 Great East Japan Earthquake. It is shown that the framework is robust against different configurations such as the number of experts, how the quantities of interest are elicited, and uncertainty in the experts’ estimates.

The goal of this paper is to facilitate a community of practice for disaster recovery modeling. This community should include hazard and disaster researchers without modeling experience and...

A growing body of literature suggests that restrictive public health measures implemented to control COVID-19 have had negative impacts on physical activity. We examined how Stay Home orders in Houston, New York City, and Seattle impacted outdoor physical activity patterns, measured by daily bicycle and pedestrian count data. We assessed changes in activity levels between the period before and during Stay Home orders. Across all three cities, we found significant changes in bicycle and pedestrian counts from the period before to the period during Stay Home orders. The direction of change varied by location, likely due to differing local contexts and outbreak progression. These results can inform policy around the use of outdoor public infrastructure as the COVID-19 pandemic continues.

The goal of this study was to first develop a systematic data collection strategy to create and maintain a database of policies in the United States that promote seismic mitigation. With this database, indices were developed to facilitate understanding of the relationship between policy activity, policy coverage, and seismic risk, as well as to perform cross-state comparison. The most basic index is a count of the number of policies each state has adopted. An index was developed that directly accounts for the seismic risk of each state. A Guttman scale was developed to characterize states’ policy coverage, as well as the level of intensity represented by states’ policy activity. The relationship between policy coverage and seismic risk was also explored. For the most part, this study found a moderate relationship between seismic risk and policy activity, as well as a moderately strong relationship between risk and policy coverage.

We are developing a new natural language processing (NLP) method to facilitate analysis of text corpora that describe long-term recovery. The aim of the method is to allow users to measure the degree that user-specified propositions about potential issues are embodied within the corpora, serving as a proxy for the disaster recovery process. The presented method employs a statistical syntax-based semantic matching model and was trained on a standard, publicly available training dataset. We applied the NLP method to a news story corpus that describes the recovery of Christchurch, New Zealand after the 2010–2011 Canterbury earthquake sequence. We used the model to compute semantic measurements of multiple potential recovery issues as expressed in the Christchurch news corpus that span 2011 to 2016. We evaluated method outputs through a user study involving twenty professional emergency managers. User study results show that the model can be effective when applied to a disaster-related news corpus. 85% of study participants were interested in a way to measure recovery issue propositions in news or other corpora. We are encouraged by the potential for future applications of our NLP method for after-action learning, recovery decision making, and disaster research.

Critical infrastructure is vulnerable to a broad range of hazards. Timely and effective recovery of critical infrastructure after extreme events is crucial. However, critical infrastructure disaster recovery planning is complicated and involves both domain-and user-centered characteristics and complexities. Recovery planning currently uses few quantitative computer-based tools and instead largely relies on expert judgment. Simulation modeling can simplify domain-centered complexities but not the human factors. Conversely, human-centered design places end-users at the center of design. We discuss the benefits of combining simulation modeling with human-centered design and refer it as human-centered simulation modeling. Human-centered simulation modeling has the capability to make recovery planning simpler and more understandable for critical infrastructure and emergency management experts and other recovery planning decision-makers. We qualitatively analyzed several resilience planning initiatives, post-disaster recovery assessments, and relevant journal articles to understand experts and decision-makers' perspectives. We propose a conceptual design framework for creating human-centered simulation models for critical infrastructure disaster recovery planning. This framework consists of three constructs: 1) user interaction with design features that end-users interact with, including model parameters assignment, decision-making support, task queries, and usability; 2) system representation that refers to system components, system interactions, and system state variables; and 3) computation core that represents computational methods required to perform processes.

The devastating 2017 Puebla quake provides an opportunity to assess how citizens perceive and use the Mexico City earthquake early warning system.

If community disaster resilience is to mature into a robust and lasting area of research, methodologically facilitated dialogue between empirical observations and theory is necessary. However, methodological and empirical research has outpaced community disaster resilience theory. To address this gap, a theoretical framework called WISC is presented. WISC is named after four constructs of the framework: well-being, identity, services, and capitals. WISC relates the two concepts of community and infrastructure, broadly defined, to the four constructs it is named after. The 4 constructs are respectively defined by 29 variables. The broadest interpretation of WISC is that infrastructure supports and facilitates components of community within human settlements. Infrastructure is represented as combinations of capitals and services; community is represented by connections of identity and well-being. Ultimately, well-being of a community is dependent on that community's collective capital. But these two constru...

Prior empirical research has demonstrated that the decisions of affected populations can significantly influence housing recovery outcomes following a natural hazard event. The current study seeks to develop an integrated post-earthquake recovery model that explicitly accounts for household decision-making. An empirical probabilistic utility-based decision model is developed using data from a survey of Los Angeles households. The results from a multinomial logistic regression showed that the time in residence, neighborhood evacuation level, physical damage to residence, duration of utility disruption and loss of access to the building, household income and earthquake insurance coverage had a statistically significant association with homeowners’ decisions. For renter decision-making, only physical damage to the residence and duration of utility disruption are found to be statistically significant. In addition to household decision-making, the integrated model incorporates probabilistic building performance assessment and a discrete-state stochastic process representation of post-earthquake housing recovery. The results from a case study incorporating three Los Angeles neighborhoods (Koreatown, East Hollywood and Lomita) show that the influence of household decision-making on occupancy-based recovery trajectories is amplified as the scale of damage increases.

Models of hazards and disasters are increasingly used to help to develop disaster-related policies and plans. Unfortunately, readily available modelling tools, such as the U.S. Federal Emergency Management Agency's Hazus model provide inadequate representation of housing recovery. Most disaster-related housing models are focused on the immediate impacts of earthquakes in terms of physical damage and economic losses. This paper introduces a new simulation modelling programming library called DESaster (discrete event simulation of disaster recovery) for building disaster recovery simulation models, which currently focuses exclusively on housing recovery. A brief overview of DESaster is provided prior to describing its application to model housing recovery after the 2015 Nepal earthquake. The DESaster library was found to be flexible enough to construct a specific model of housing recovery in Nepal. More specifically, it was found to be particularly useful for better understanding resource needs, such as construction materials and skilled labourers, through comparison of different allocation scenarios.

In August 2012, Hurricane Isaac made landfall twice in Louisiana. Cumulatively, over 1 million customers lost electricity as a result of the hurricane, some for more than 10 days. As a disaster, Hurricane Isaac is relatively unique because of the opportunity to largely isolate impacts and decisions directly associated with the electricity outage and restoration from cascading impacts related to direct flood or wind damage. Louisiana emergency managers, business representatives, and public officials were interviewed to develop and analyze a case study of the outage and restoration event. Interviewees were asked about (1) the significant impacts from the outage, (2) what (if any) lasting effects would result from the outage, (3) the relative performance of power restoration, (4) the effectiveness of communication between stakeholders, and (5) factors influencing the public and political perception of restoration performance. Other collected data included content from news media, government documents...