A Stochastic Approach to Model Household Re-occupancy in A Community Following A Natural Hazard

additionally, based on available social science studies, we further define a set of time-dependent conditional re-occupancy probability functions (CRPFs) that give the probability of a household re-occupying its pre-event dwelling units at any time conditional on the change in households JFS and its income level. Finally, the time-variant household-level re-occupancy probability is derived by solving the DTMC with partially absorbing boundary conditions described by the CRPFs. The community-level HRO is then obtained through aggregating the household-level re-occupancy state across the community over the recovery time horizon. The model will be further calibrated by data collected in ongoing field studies with an ultimate goal of supporting further researches on community resilience planning.The re-occupancy of displaced households in a community following a hazard event is a complex social process driven collectively by the functionality states of community building portfolios and supporting lifelines. This study presents a novel approach for household re-occupancy (HRO) modeling using discrete state, Discrete Time Markov Chain (DTMC). Our hypothesis is that the reoccupancy state of a displaced household at a post-event time is collectively determined by the joint functionality status (JFS) (of its related school(s), workplace(s) and pre-event dwelling unit) and by the resourcefulness of the household largely determined by its income level. Accordingly, we construct a one-step transition probability matrix of the DTMC modeling the households JFS as a function of the functionality states of its school(s), workplace(s) and pre-event dwelling unitThis research was supported by the National Key R&D Program of China (Grant No. 2016YFC0800200) and by the US National Institute of Standards and Technology (NIST) under Cooperative Agreement No. 70NANB15H044

Computational environment for modeling and enhancing community resilience: Introducing the center for risk-based community resilience planning

The resilience of a community is defined as its ability to prepare for, withstand, recover from and adapt to the effects of natural or human-caused disasters, and depends on the performance of the built environment and on supporting social, economic and public institutions that are essential for immediate response and long-term recovery and adaptation. The performance of the built environment generally is governed by codes, standards, and regulations, which are applicable to individual facilities and residences, are based on different performance criteria, and do not account for the interdependence of buildings, transportation, utilities and other infrastructure sectors. The National Institute of Standards and Technology recently awarded a new Center of Excellence (NIST-CoE) for Risk-Based Community Resilience Planning, which is headquartered at Colorado State University and involves nine additional universities. Research in this Center is focusing on three major research thrusts: (1) developing the NIST-Community Resilience Modeling Environment known as NIST-CORE, thereby enabling alternative strategies to enhance community resilience to be measured quantitatively; (2) developing a standardized data ontology, robust data architecture and data management tools in support of NIST-CORE; and (3) performing a comprehensive set of hindcasts on disasters to validate the data architecture and NIST-CORE

Flood Performance and Dislocation Assessment for Lumberton Homes after Hurricane Matthew

In order to better understand community resilience following a disaster, a multidisciplinary research team from the Center of Excellence (CoE) for Risk-Based Community Resilience Planning and the National Institute of Standards and Technology (NIST) jointly conducted a series of longitudinal field studies in the U.S. city of Lumberton, North Carolina following major flooding from Hurricane Matthew (2016). Damage surveys on structures and interviews with households were conducted during the first field study to explore physical, economic, and social impacts of major riverine flooding on this small, tri-racial community. This paper is focused on damage to housing and subsequent household dislocation. Empirical damage fragilities were developed for residential buildings using a comprehensive set of engineering damage inspection data collected by the team. Multi-variate models were developed to assess the consequences of physical damage to housing units for household dislocation, including socio-demographic factors. The goal was not to develop the definitive model of household dislocation, but rather to show how engineering and social science data can be combined to better understand the broader social impacts of disasters – in this case, household dislocation. This study may help inform assessments of flood damage and dislocation patterns for other U.S. communities as a function of construction, social, and economic makeup

Trust in Emergency Management Authorities and Individual Emergency Preparedness for Tornadoes

The risks associated with disasters can be significantly reduced if individuals are well prepared according to the orders and recommendations of emergency management authorities such as the Federal Emergency Management Agency (FEMA) and local government. Despite this fact, there is evidence that individuals are not cooperative with these authorities and are therefore underprepared for an emergency. This article argues that individual trust in emergency management authorities may affect their cooperation with emergency preparedness recommendations. Using unique survey data, this study finds a nuanced relationship between individual emergency preparedness for tornadoes and trust in emergency management authorities. Although trust in FEMA in isolation does not explain variations in individual preparedness for tornadoes, increased preparation for a tornado is explained by trust in local government contingent upon a low baseline level of trust in FEMA. This article concludes with some practical and theoretical implications for emergency management authorities and scholars