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Lifestyle recovery captures the collective effects of population activities as well as the restoration of infrastructure and business services. This study uses a novel approach to leverage privacy-enhanced location intelligence data, which is anonymized and aggregated, to characterize distinctive lifestyle patterns and to unveil recovery trajectories after 2017 Hurricane Harvey in Harris County, Texas (USA). The analysis integrates multiple data sources to record the number of visits from home census block groups (CBGs) to different points of interest (POIs) in the county during the baseline and disaster periods. For the methodology, the research utilizes unsupervised machine learning and ANOVA statistical testing to characterize the recovery of lifestyles using privacy-enhanced location intelligence data. First, primary clustering using k-means characterized four distinct essential and non-essential lifestyle patterns. For each primary lifestyle cluster, the secondary clustering characterized the impact of the hurricane into four possible recovery trajectories based on the severity of maximum disruption and duration of recovery. The findings further reveal multiple recovery trajectories and durations within each lifestyle cluster, which imply differential recovery rates among similar lifestyles and different demographic groups. The impact of flooding on lifestyle recovery extends beyond the flooded regions, as 59% of CBGs with extreme recovery durations did not have at least 1% of direct flooding impacts. The findings offer a twofold theoretical significance: (1) lifestyle recovery is a critical milestone that needs to be examined, quantified, and monitored in the aftermath of disasters; (2) spatial structures of cities formed by human mobility and distribution of facilities extend the spatial reach of flood impacts on population lifestyles. These provide novel data-driven insights for public officials and emergency managers to examine, measure, and monitor a critical milestone in community recovery trajectory based on the return of lifestyles to normalcy. [ABSTRACT FROM AUTHOR]

Infrastructure service disruptions impact households in an affected community disproportionally. To enable integrating social equity considerations in infrastructure resilience assessments, this study created a new computational multi‐agent simulation model, which enables integrated assessment of hazard, infrastructure system, and household elements and their interactions. With a focus on hurricane‐induced power outages, the model consists of three elements: (1) the hazard component simulates exposure of the community to a hurricane with varying intensity levels; (2) the physical infrastructure component simulates the power network and its probabilistic failures and restoration under different hazard scenarios; and (3) the households component captures the dynamic processes related to preparation, information‐seeking, and response actions of households facing hurricane‐induced power outages. We used empirical data from household surveys from three hurricanes (Harvey, Florence, and Michael) in conjunction with theoretical decision‐making models to abstract and simulate the underlying mechanisms affecting the experienced hardship of households when facing power outages. The multi‐agent simulation model was then tested in the context of Harris County, Texas, and verified and validated using empirical results from Hurricane Harvey in 2017. Then, the model was used to examine the effects of different factors—such as forewarning durations, social network types, and restoration and resource allocation strategies—on reducing the societal impacts of service disruptions in an equitable manner. The results show that improving the restoration prioritization strategy to focus on vulnerable populations is an effective approach, especially during high‐intensity events, to enhance equitable resilience. The results show the capability of the proposed computational model for capturing the dynamic and complex interactions in the nexus of households, hazards, and infrastructure systems to better integrate human‐centric aspects in resilience planning and assessment of infrastructure systems in disasters. Hence, the proposed model and its results could provide a new tool for infrastructure managers and operators, as well as for disaster managers, in devising hazard mitigation and response strategies to reduce the societal impacts of power outages in an equitable manner. [ABSTRACT FROM AUTHOR]

The objectives of this study are: (1) to specify evacuation return and home-switch stability as two critical milestones of short-term recovery during and in the aftermath of disasters; and (2) to understand the disparities among subpopulations in the duration of these critical recovery milestones. Using privacy-preserving fine-resolution location-based data, we examine evacuation and home move-out rates in Harris County, Texas in the context of the 2017 Hurricane Harvey. For each of the two critical recovery milestones, the results reveal the areas with short- and long-return durations and enable evaluating disparities in evacuation return and home-switch stability patterns. In fact, a shorter duration of critical recovery milestone indicators in flooded areas is not necessarily a positive indication. Shorter evacuation return could be due to barriers to evacuation and shorter home move-out rate return for lower-income residents is associated with living in rental homes. In addition, skewed and non-uniform recovery patterns for both the evacuation return and home-switch stability were observed in all subpopulation groups. All return patterns show a two-phase return progress pattern. The findings could inform disaster managers and public officials to perform recovery monitoring and resource allocation in a more proactive, data-driven, and equitable manner. [ABSTRACT FROM AUTHOR]

The objective of this study is to create and test a hybrid deep learning (DL) model, FastGRNN‐FCN (fast, accurate, stable and tiny gated recurrent neural network‐fully convolutional network), for urban flood prediction and situation awareness using channel network sensors data. The study used Harris County, Texas, as the testbed, and obtained channel sensor data from three historical flood events (e.g., 2016 Tax Day Flood, 2016 Memorial Day Flood, and 2017 Hurricane Harvey Flood) for training and validating the hybrid DL model. The flood data are divided into a multivariate time series and used as the model input. Each input comprises nine variables, including information of the studied channel sensor and its predecessor and successor sensors in the channel network. Precision‐recall curve and F‐measure are used to identify the optimal set of model parameters. The optimal model with a weight of 1 and a critical threshold of 0.59 are obtained through 100 iterations based on examining different weights and thresholds. The test accuracy and F‐measure eventually reach 97.8% and 0.792, respectively. The model is then tested in predicting the 2019 Imelda Flood in Houston and the results show an excellent match with the empirical flood. The results show that the model enables accurate prediction of the spatial–temporal flood propagation and recession and provides emergency response officials with a predictive flood warning tool for prioritizing the flood response and resource allocation strategies. [ABSTRACT FROM AUTHOR]

The objective of this study is to examine the properties of actor collaboration networks and to analyze how they influence the coordination of hazard mitigation in resilience planning in Harris County, Texas. Effective resilience planning can only be achieved through the collective actions of various actors and the network structures unfold the collaboration among the actors. Understanding the structural properties of actor collaboration networks for hazard mitigation may hold the key to understanding and improving the resilience planning process. To this end, after Hurricane Harvey, we administered a stakeholder survey to actors in various urban sectors involved in hazard mitigation (e.g., flood control, transportation, and emergency response). The survey aimed to capture actor collaboration networks for hazard mitigation in Harris County, Texas prior to Harvey. The collaboration represents that the survey respondents worked with the actors in the survey roster for hazard mitigation. We asked the respondents the frequency of the collaboration in the survey (e.g., yearly, monthly, weekly and daily). We examined three network structural properties to study actor positions in the network: degree centrality, boundary spanners, and core-periphery structure, because degree centrality could indicate what actors had more collaborations; boundary spanners could reveal what actors were in strategic positions to connect otherwise separate actors; and core-periphery structure could identify what actors formed the core of actor collaboration network for hazard mitigation and whether the core was composed of actors from diverse sectors. The results showed: (1) governmental actors from different sectors had high degree centrality and betweenness centrality, which indicated that governmental actors had a more influential role in coordination and information dissemination in hazard mitigation planning and implementation; and (2) fewer flood control and non-governmental actors were at the core of the actor collaboration networks, which reduced the extent of hazard mitigation coordination. The results identify potential influential actors (such as City of Houston, Harris County, and Houston–Galveston Area Council) in coordination of hazard mitigation and yield recommendations for increased actor network cohesion for better coordination of hazard mitigation across diverse sectors in resilience planning. [ABSTRACT FROM AUTHOR]

This paper presents a Bayesian network model to assess the vulnerability of the flood control infrastructure and to simulate failure cascade based on the topological structure of flood control networks along with hydrological information gathered from sensors. Two measures are proposed to characterize the flood control network vulnerability and failure cascade: (a) node failure probability (NFP), which determines the failure likelihood of each network component under each scenario of rainfall event, and (b) failure cascade susceptibility, which captures the susceptibility of a network component to failure due to failure of other links. The proposed model was tested in both single watershed and multiple watershed scenarios in Harris County, Texas using historical data from three different flooding events, including Hurricane Harvey in 2017. The proposed model was able to identify the most vulnerable flood control network segments prone to flooding in the face of extreme rainfall. The framework and results furnish a new tool and insights to help decision‐makers to prioritize infrastructure enhancement investments and actions. The proposed Bayesian network modeling framework also enables simulation of failure cascades in flood control infrastructures, and thus could be used for scenario planning as well as near‐real‐time inundation forecasting to inform emergency response planning and operation, and hence improve the flood resilience of urban areas. [ABSTRACT FROM AUTHOR]

This paper proposes and tests a multilayer framework for simulating the network dynamics of inter-organizational coordination among interdependent infrastructure systems (IISs) in resilience planning. Inter-organizational coordination among IISs (such as transportation, flood control, and emergency management) would greatly affect the effectiveness of resilience planning. Hence, it is important to examine and understand the dynamics of coordination in networks of organizations within and across various systems in resilience planning. To capture the dynamic nature of coordination frequency and the heterogeneity of organizations, this paper proposes a multilayer network simulation framework enabling the characterization of inter-organizational coordination dynamics within and across IISs. In the proposed framework, coordination probabilities are utilized to approximate the varying levels of collaboration among organizations. Based on these derived collaborations, the simulation process perturbs intra-layer or inter-layer links and unveils the level of inter-organizational coordination within and across IISs. To test the proposed framework, the study examined a multilayer collaboration network of 35 organizations from five infrastructure systems within Harris County, Texas, based on the data gathered from a survey in the aftermath of Hurricane Harvey. The results indicate that prior to Hurricane Harvey: (1) coordination among organizations across different infrastructure systems is less than the coordination within the individual systems; (2) organizations from the community development system had a low level of coordination for hazard mitigation with organizations in flood control and transportation systems; (3) achieving a greater level of coordination among organizations across infrastructure systems is more difficult and would require a greater frequency of interaction (compared to within-system coordination). The results show the capability of the proposed multilayer network simulation framework to examine inter-organizational coordination dynamics at the system level (e.g., within and across IISs). The assessment of inter-organizational coordination within and across IISs sheds light on important organizational interdependencies in IISs and leads to recommendations for improving the resilience planning process. [ABSTRACT FROM AUTHOR]

• Bayesian network flood model integrating network structure and empirical flood data. • Failure characterization of road network considering cascading failure from channels. • Simulation of flood propagation on the road network with high accuracy. • Quantitative flood risk measurement in road networks with a tiered representation. • Assist decision-makers to prioritize infrastructure protections for risk reduction. This paper presents a probabilistic model for assessing risk of cascading failures in co-located road and channel networks. The proposed Bayesian network analysis framework integrates network structural properties and empirical flood propagation data to model the spread of flooding. The model was tested in a multiple watershed scenario in Harris County, Texas (USA), using historical flood data from past events. The results show the capability of the proposed Bayesian network model to quantitatively characterize the failure (i.e., inundation) of road network considering the cascading failure (i.e., overflow) from the channel network. The proposed model also enables simulating the risk of flood cascades (i.e., flood propagation) on the road network with high accuracy. The generic design of the algorithm also enables the adaptation of the proposed framework in other cities and regions. Accordingly, the proposed model provides a new tool to help decision-makers prioritize infrastructure protection plans and emergency response actions. [ABSTRACT FROM AUTHOR]

• Framework for assessing physical infrastructure system interactions in households. • Disruption vulnerability is a factor of complex socio-physical system interactions. • Accessibility and urban, social and behavioral attributes influence vulnerability. Water, energy, and food systems are highly interconnected, where disruptions in one system have direct or indirect impacts on others. Little has been studied regarding the nexus interactions at the household level, let alone in a disaster setting. Measuring household vulnerability to their disruptions is an important determinant of resilience and societal risk in the face of natural hazards. This study proposes a new framework based on disaster risk theory and Food-Energy-Water (FEW) Nexus systems thinking to analyze the collective influence of integrated infrastructure disruptions and socioeconomic factors on household vulnerability during disasters. ANOVA one-way tests are used to determine the disparity in disaster risk measures across non-vulnerable and highly vulnerable households. Structural Equation Modeling (SEM) is employed to test the proposed relationships between infrastructure disruptions, urban attributes, household preparation behaviors in the context of the 2017 Hurricane Harvey in Harris County, Texas. Overall, the pre-existing conditions of communities in terms of its physical attributes, preparation behaviors, and the coupled durations of FEW infrastructure disruptions were each found to have statistically significant associations with heightened household vulnerability to FEW service disruptions. Physical attributes (β = 0.134, p = 0.001) and prior experience with disasters (β = -0.103, p = 0.000) were found to be the most significant indicators of poor preparation behavior. households with children, racial minority status, and low income and educational attainment of households were associated with having lower levels of preparedness. The framework developed in this study can serve as a foundation to expand the transdisciplinary research of infrastructure and community resilience to better address the needs of the population in an emergency. [ABSTRACT FROM AUTHOR]

Using quantitative measures to assess road network resilience, this study proposes a system-level framework that offers insights into existing road network resilience that could inform the planning and development processes of transportation agencies. This study identified and implemented four quantitative metrics to classify the criticality of road segments based on dimensions of road network resilience. Integrating the four metrics of classification using two mathematical approaches, this study arrived at overall resilience performance metrics for assessing road segment criticality. A case study was conducted on Texas road networks to demonstrate the effectiveness of implementing this framework in a practical scenario. The data used in this study is available to other states and countries; thus, the framework presented in this study can be adopted by transportation agencies for regional transportation resilience assessments. [ABSTRACT FROM AUTHOR]

This paper presents an integrated framework that combines a community's physical vulnerability to access disruption to critical facilities and their tolerance for such access disruption to services in order to inform the targeted communities protection and build an equitable resilience enhancement plan. The first component of the proposed framework includes a percolation simulation model capable of integrating the road network disruption probability into the flood propagation and encapsulates the road network's access to critical facilities (i.e., healthcare facilities). The discovered spatial reach of an areas' physical vulnerability dependence is 9 miles. Besides, physical disruptions in road networks and loss of access to emergency services (such as healthcare) have varying impacts on different sub-populations. To consider this aspect, the second component of the proposed framework involves a disruption tolerance index (DTI) to examine communities' tolerance towards access disruption to healthcare facilities in the face of the flooding. The proposed framework recognizes the importance of both infrastructure and human perspective of the vulnerability assessment and is tested using empirical data from Harris County, Texas, in the case of road network disruptions due to fluvial flooding. Houston, the fourth-largest city in the United States, is within Harris County. Integrated spatial analysis result reveals different spatial clusters of vulnerability across the study region and provides important insights regarding the critical infrastructure protection prioritization and hazard mitigation planning. The spatial clusters also unveil the existence of a homogeneous spatial pattern where similar vulnerable areas stay together. The proposed framework could be adopted by other cities and different critical facilities to enable decision-makers, infrastructure managers, and city planners to better evaluate their community vulnerability. [ABSTRACT FROM AUTHOR]

Hurricanes are one of the most catastrophic natural hazards faced by residents of the United States. Improving the public's hurricane preparedness is essential to reduce the impact and disruption of hurricanes on households. Inherent in traditional methods for quantifying and monitoring hurricane preparedness are significant lags, which hinder effective monitoring of residents' preparedness in advance of an impending hurricane. This study establishes a methodological framework to quantify the extent, timing, and spatial variation of hurricane preparedness at the census block group level using high-resolution location intelligence data. Anonymized cell phone data on visits to points-of-interest for each census block group in Harris County before 2017 Hurricane Harvey were used to examine residents' hurricane preparedness. Four categories of points-of-interest, grocery stores, gas stations, pharmacies and home improvement stores, were identified as they have close relationship with hurricane preparedness, and the daily number of visits from each CBG to these four categories of POIs were calculated during preparation period. Two metrics, extent of preparedness and proactivity, were calculated based on the daily visit percentage change compared to the baseline period. The results show that peak visits to pharmacies often occurred in the early stage of preparation, whereas the peak of visits to gas stations happened closer to hurricane landfall. The spatial and temporal patterns of visits to grocery stores and home improvement stores were quite similar. However, correlation analysis demonstrates that extent of preparedness and proactivity are independent of each other. Combined with synchronous evacuation data, CBGs in Harris County were divided into four clusters in terms of extent of preparedness and evacuation rate. The clusters with low preparedness and low evacuation rate were identified as hotspots of vulnerability for shelter-in-place households that would need urgent attention during response. Hence, the research findings provide a new data-driven approach to quantify and monitor the extent, timing, and spatial variations of hurricane preparedness. Accordingly, the study advances data-driven understanding of human protective actions during disasters. The study outcomes also provide emergency response managers and public officials with novel data-driven insights to more proactively monitor residents' disaster preparedness, making it possible to identify under-prepared areas and better allocate resources in a timely manner., (© 2022. The Author(s).)

The objective of this article is to systematically assess and identify factors affecting risk disparity due to infrastructure service disruptions in extreme weather events. We propose a household service gap model that characterizes societal risks at the household level by examining service disruptions as threats, level of tolerance of households to disruptions as susceptibility, and experienced hardship as an indicator for the realized impacts of risk. The concept of "zone of tolerance" for the service disruptions was encapsulated to account for different capabilities of the households to endure the adverse impacts. The model was tested and validated in the context of power outages through survey data from the residents of Harris County in the aftermath of Hurricane Harvey in 2017. The results show that households' need for utility service, preparedness level, the existence of substitutes, possession of social capital, previous experience with disasters, and risk communication affect the zone of tolerance within which households cope with service outages. In addition, sociodemographic characteristics, such as race and residence type, are shown to influence the zone of tolerance, and hence the level of hardship experienced by the affected households. The results reveal that population subgroups show variations in the tolerance level of service disruptions. The findings highlight the importance of integrating social dimensions into the resilience planning of infrastructure systems. The proposed model and results enable human-centric hazards mitigation and resilience planning to effectively reduce the risk disparity of vulnerable populations to service disruptions in disasters., (© 2021 Society for Risk Analysis.)

The objective of this paper is to empirically examine the impacts of infrastructure service disruptions on the well-being of vulnerable populations during disasters. There are limited studies that empirically evaluate the extent to which disruptions in infrastructure system services impact subpopulation groups differently and how these impacts relate to the wellbeing of households. Being able to systematically capture the differential experiences of sub-populations in a community due to infrastructure disruptions is necessary to highlight the differential needs and inequities that households have. In order to address this knowledge gap, this study derives an empirical relationship between sociodemographic factors of households and their subjective well-being impacts due to disruptions in various infrastructure services during and immediately after Hurricane Harvey. Statistical analysis driven by spearman-rank order correlations and fisher-z tests indicated significant disparities in well-being due to service disruptions among vulnerable population groups. The characterization of subjective well-being is used to explain to what extent infrastructure service disruptions influence different subpopulations. The results show that: (1) disruptions in transportation, solid waste, food, and water infrastructure services resulted in more significant well-being impact disparities as compared to electricity and communication services; (2) households identifying as Black and African American experienced well-being impact due to disruptions in food, transportation, and solid waste services; and (3) households were more likely to feel helpless, difficulty doing daily tasks and feeling distance from their community as a result of service disruptions. The findings present novel insights into understanding the role of infrastructure resilience in household well-being and highlights why it is so important to use approaches that consider various factors. Infrastructure resilience models tend to be monolithic. The results provide empirical and quantitative evidence of the inequalities in well-being impacts across various sub-populations. The research approach and findings enable a paradigm shift towards a more human-centric approach to infrastructure resilience., Competing Interests: The authors have declared that no competing interests exist.