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Changes in levels and patterns of physical activity might be a mechanism to assess and inform disaster recovery through the lens of wellbeing. However, few studies have examined disaster impacts on physical activity or the potential for physical activity to serve as an indicator of disaster recovery. In this exploratory study, we examined daily bicycle and pedestrian counts from four public bicycle/pedestrian trails in Houston, before and after Hurricane Harvey landfall, to assess if physical activity returned to pre-Harvey levels. An interrupted time series analysis was conducted to examine the immediate impact of Harvey landfall on physical activity, t-tests were performed to assess if trail usage returned to pre-Harvey levels. Hurricane Harvey was found to have a significant negative impact on daily pedestrian and bicycle counts for three of the four trails. Daily pedestrian and bicycle counts were found to return to pre-Harvey or higher levels at 6 weeks post-landfall at all locations studied. We discuss the potential for further research to examine the trends, feasibility, validity, and limitations of using bicycle and pedestrian use levels as a proxy for disaster recovery and wellbeing among affected populations.

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...

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.

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.

Community disaster resilience is commonly conceptualized as the capacity to reduce post-event loss and facilitate effective recovery. Technologies, such as data systems, computer models, and visualization tools, are more common and well developed for understanding immediate (and static) loss than for understanding dynamic processes of recovery. Most available technology for understanding post-disaster dynamics is specific to short-term emergency or crisis processes. As a result, development of simulation models of recovery is necessary to enable technology-supported decision making for realizing community disaster resilience. We present a proof of concept design for a home reconstruction discrete-event simulation (DES) to evaluate its potential for simulating disaster recovery in general. The design is implemented as a prototype using the SimPy discrete-event simulation Python library. Preliminary outputs from the prototype simulation suggest that DES is appropriate and promising for modeling home reconstruction. The ability to alter the quantities of shared resource stocks, event durations, and access qualifications can likely facilitate modeling of other types of recovery processes, as well as a variety of post-disaster scenarios. As such, DES appears to be a novel technological approach that can be developed to support pre-and post-disaster decision making for improved community disaster resilience.

This case study discusses post-event reconnaissance research of the September 8, 2011, power outage that left San Diego County, California, without electricity for up to 12 h. The objective of this case study is to synthesize and analyze the impacts of the outage and responses to the event. Understanding the outage’s impacts and responses helps to reveal restoration practices and contexts that promote meeting both technical and nontechnical goals. This study reveals several issues related to restoration decision-making and communication related to critical customers, particularly those responsible for health care, wastewater and potable water management, fuel provision, and food service. Restoration did not occur and was not communicated in such a way to avoid impacts to dependent critical infrastructure, reflect state restoration criteria, or meet expectations of a variety of power customers. Insight from this case study suggests three themes to guide research and development of best practices fo...

Understanding and improving the resilience of infrastructure is critical to the broader goal of community disaster resilience - minimization of loss and facilitation of recovery. Empirical research shows that infrastructure damage or disruption from hazard events and ensuing reduced service levels has a strong, negative impact on business and household performance. Conceptual and computer models relating the socio-technical facets of infrastructure to community disaster resilience are underdeveloped. Community disaster resilience does not make theoretical or practical sense without viewing infrastructure as the combination of capitals and services. Specifically, infrastructure is characterized by its relationship to other infrastructure, agents, possible disruptions, possible interventions, jurisdictions and markets. This paper presents a subset of evolving conceptual, algorithmic and geo-visual representations of infrastructure-dependent community disaster resilience. This builds upon existing work on ResilUS - a prototype simulation model of community disaster resilience - and other contributions from the literature. The ultimate goal of this research is to provide the means for making lifeline infrastructure mitigation planning and restoration decisions that account for socio-technical and recovery-based goals.

Each disaster reminds us that, from an economic standpoint, losses do not occur instantaneously, but are accumulated over the course of a sometimes long and complex recovery process. Moreover, disasters are spatial events that impact some places and some groups within those places more heavily than others. These effects can be observed in both natural disasters and human-induced events. Thus in the September 11th tragedy of 2001, the loss of the World Trade Center towers and the thousands of human lives do not in themselves constitute the economic impact of the disaster. Neither does the loss of gross regional product (GRP) on the 11th itself. Rather, the economic impact of the disaster is strongly influenced by the multitude of decisions made in the days and months following — decisions regarding whether to relocate an office to New Jersey and for how long, whether to lay off workers and how many, and whether to inject stimulus spending into the New York City economy and how much. Similar post-event decisions strongly influence recovery in natural disasters such as floods or earthquakes. In other words, the process of disaster recovery is critical to understanding the spatial economic impacts of disasters, yet the recovery process itself is extremely complex and uncertain.