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Review on modeling the societal impact of infrastructure disruptions due to disasters
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Abstract
Infrastructure systems play a critical role in providing essential products and services for the functioning of modern society; however, they are vulnerable to disasters and their service disruptions can cause severe societal impacts. To protect infrastructure from disasters and reduce potential impacts, great achievements have been made in modeling interdependent infrastructure systems in past decades. In recent years, scholars have gradually shifted their research focus to understanding and modeling societal impacts of disruptions considering the fact that infrastructure systems are critical because of their role in societal functioning, especially under situations of modern societies. Exploring how infrastructure disruptions impair society to enhance resilient city has become a key field of study. By comprehensively reviewing relevant studies, this paper demonstrated the definition and types of societal impact of infrastructure disruptions, and summarized the modeling approaches into four types: extended infrastructure modeling approaches, empirical approaches, agent-based approaches, and big data-driven approaches. For each approach, this paper organized relevant literature in terms of modeling ideas, advantages, and disadvantages. Furthermore, the four approaches were compared according to several criteria, including the input data, types of societal impact, and application scope. Finally, this paper illustrated the challenges and future research directions in the field.
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Community resilience (CR) has been studied as an indicator to measure how well a given community copes with and recovers from a given disaster. Social–psychological community resilience (SPCR) has been used as a basis to determine public policy directions based on priority. Although the impact of the COVID-19 has been serious all over the world and interferes every aspect of our daily life, some countries have handled this disaster better than others due to their different disaster management policies and perceptions about the disaster. In this work, we are interested in measuring and analyzing SPCR through social media information in five different countries which can reflect different disaster management policies and perceptions toward the COVID-19. In the literature, measuring SPCR has been discussed, but the key attributes have not been agreed upon. We propose to use two attributes for measuring SPCR, i.e., community wellbeing (CW) and community capital (CC), because social and psychological resilience can be the firm basis for a community to be restored and reinvented into the so-called transformative community to ensure sustainability in the future generation. We use Tweeter data and investigate how each country shows different trends of SPCR in response to real and fake tweets generated during a COVID-19 period using machine learning and text-mining tools. We employ tweets generated in Australia (AUS), Singapore (SG), Republic of Korea (ROK), the United Kingdom (UK), and the United States (US), during March–November 2020 and measure the SPCR of each country and its associated attributes for analyzing the overall trends. Our results show that ROK among the five countries in our study has the highest level in CW, CC, and the resulting SPCR on real tweets reflecting reality, a result that matches well with the fact that ROK is resilient to COVID-19 during March–November 2020. Further, our results indicate that SPCR on real tweets is up to 80% higher than SPCR on fake tweets, suggesting that a much stronger community resilience may be achieved on real tweets. Finally, our results show that there is a negative correlation between SPCR values on fake and real tweets overall when considering all the tweets of the five countries to derive the overall trends. However, for each country, we observe a different correlation, either positive or negative, depending on each country. This implies that there should be further investigation of analyzing SPCR by considering unique cultural and national characteristics of each country.
The recurrence of extreme weather events has led to the development of methods for assessing the vulnerability and interdependencies of physical and human systems. A case example is Hurricane Maria (H-Maria), where Puerto Rico experienced damage to 80% of its electrical power system, leading to massive disruptions of essential services for months. Here we evaluate the effectiveness of various interventions aimed at reducing vulnerability by considering power and water infrastructure and respective water–power dependencies while also considering the social vulnerability of affected communities associated with the physical infrastructure upgrades. On the basis of the current infrastructure configuration, we found that all communities suffered enormously from power and water outages. As one upgrade option, we show that incorporating regional energy grids would reduce outages in an H-Maria scenario. However, a large portion of disadvantaged communities will face service disruption under this option. In contrast, hardening transmission lines, as the second option, would improve service delivery and, most importantly, provide uninterrupted service to the higher portion of the vulnerable population.
As demand for electricity increases on an already strained electrical supply due to urbanization, population growth, and climate change, the likelihood of power outages will also increase. While researchers understand that the number of electrical grid disturbances is increasing, we do not adequately understand how increased power outages will affect a society that has become increasingly dependent on a reliable electric supply. This systematic review aims to understand how power outages have affected society, primarily through health impacts, and identify populations most vulnerable to power outages based on the conclusions from prior studies. Based on search parameters, 762 articles were initially identified, of which only 50 discussed the social impacts of power outages in North America. According to the existing research base, racial and ethnic minorities, especially Blacks or African Americans, those of lower socioeconomic status, children, older adults, and those living in rural areas experienced more significant impacts from previous power outages. Additionally, criminal activity increased during prolonged power outages, with both pro-social and anti-social behaviors observed. Providing financial assistance or resources to replace spoiled goods can reduce crime. Future research on this topic must consider the financial effects caused by power outages, how power outage impacts seasonally vary, and how power outage impacts vary by length and duration.
Communities in the United States are increasingly dependent upon aging infrastructure systems and challenged by more frequent and intense extreme weather events due in part to climate change. However, prioritizing resilience-related investments in these systems is hindered by the lack of performance metrics that objectively quantify the societal outcomes of infrastructure disruptions, such as power or water outages. This article outlines the process of developing an equity-focused resilience metric that captures the social consequences of infrastructure service disruptions on households. Theoretically grounded in the Capabilities Approach (CA) theory of human development, this metric focuses on estimating the burden of post-event adaptations taken by households to maintain their basic capabilities (e.g., ability to access food and water) and fulfill important household functionings (e.g., maintaining health and well-being). A travel cost method (TCM) that considers travel-related expenses, direct out-of-pocket expenses, and opportunity costs is presented as a way to measure the value of locations (e.g., grocery stores, emergency shelters, etc.) that provide services that enable households to maintain capabilities. A gravity-weighted model of accessibility is also discussed as a way to capture the value of having multiple potential service locations from which to choose and offers a way to capture important factors impacting a household’s ability to access important goods and services during outages. The proposed social burden metric equation incorporates the valuation principles of the TCM into the framework of the gravity model, resulting in a novel metric with strong methodological heritage. The article concludes by discussing the types of data needed to populate the proposed metric and future applications of this work that could inform the resilient infrastructure investments and planning necessary to mitigate the social burdens of power outages on vulnerable populations.
Emergency services and utilities need appropriate planning tools to analyze and improve infrastructure and community resilience to disasters. Recognized as a key metric of community resilience is the social well-being of a community during a disaster, which is made up of mental and physical social health. Other factors influencing community resilience directly or indirectly are emotional health, emergency services, and the availability of critical infrastructures services, such as food, agriculture, water, transportation, electric power, and communications system. It turns out that in computational social science literature dealing with community resilience, the role of these critical infrastructures along with some important social characteristics is not considered. To address these weaknesses, we develop a new multi-agent based stochastic dynamical model, standardized by overview, design concepts, details, and decision (ODD+D) protocol and derived from neuro-science, psychological and social sciences, to measure community resilience in terms of mental and physical well-being. Using this model, we analyze the micro-macro level dependence between the emergency services and power systems and social characteristics such as fear, risk perception, information-seeking behaviour, cooperation, flexibility, empathy, and experience, in an artificial society. Furthermore, we simulate this model in two case studies and show that a high level of flexibility, experience, and cooperation enhances community resilience. Implications for both theory and practice are discussed.
Natural hazards cause disruptions in access to critical facilities, such as grocery stores, impeding residents’ ability to prepare for and cope with hardships during the disaster and recovery; however, disrupted access to critical facilities is not equal for all residents of a community. In this study, we examine disparate access to grocery stores in the context of the 2017 Hurricane Harvey in Harris County, Texas. We utilized high-resolution location-based datasets in implementing spatial network analysis and dynamic clustering techniques to uncover the overall disparate access to grocery stores for socially vulnerable populations during different phases of the disaster. Three access indicators are examined using network-centric measures: number of unique stores visited, average trip time to stores, and average distance to stores. These access indicators help us capture three dimensions of access: redundancy, rapidity, and proximity. The findings show the insufficiency of focusing merely on the distributional factors, such as location in a food desert and number of facilities, to capture the disparities in access, especially during the preparation and impact/short-term recovery periods. Furthermore, the characterization of access by considering combinations of access indicators reveals that flooding disproportionally affects socially vulnerable populations. High-income areas have better access during the preparation period as they are able to visit a greater number of stores and commute farther distances to obtain supplies. The conclusions of this study have important implications for urban development (facility distribution), emergency management, and resource allocation by identifying areas most vulnerable to disproportionate access impacts using more equity-focused and data-driven approaches.
Critical infrastructure systems derive their importance from the societal needs they help meet. Yet the relationship between infrastructure system functioning and societal functioning is not well-understood, nor are the impacts of infrastructure system disruptions on consumers. We develop two empirical measures of societal impacts—willingness to pay (WTP) to avoid service interruptions and a constructed scale of unhappiness, compare them to each other and others from the literature, and use them to examine household impacts of service interruptions. Focusing on household-level societal impacts of electric power and water service interruptions, we use survey-based data from Los Angeles County, USA, to fit a random effects within-between model of WTP and an ordinal logit with mixed effects to predict unhappiness, both as a function of infrastructure type, outage duration, and household attributes. Results suggest household impact increases nonlinearly with outage duration, and the impact of electric power disruptions is greater than water supply disruptions. Unhappiness is better able to distinguish the effects of shorter-duration outages than WTP is. Some people experience at least some duration of outage without negative impact. Increased household impact was also associated with using electricity for medical devices or water for work or business, perceived likelihood of an emergency, worry about an emergency, past negative experiences with emergencies, lower level of preparation, less connection to the neighborhood, higher income, being married, being younger, having pets, and having someone with a medical condition in the house. Financial, time/effort, health, and stress concerns all substantially influence the stated level of unhappiness.
Tropical cyclones (TCs) have caused extensive power outages. The impacts of TC-caused blackouts may worsen in the future as TCs and heatwaves intensify. Here we couple TC and heatwave projections and power outage and recovery process analysis to investigate how TC-blackout-heatwave compound hazard risk may vary in a changing climate, with Harris County, Texas as an example. We find that, under the high-emissions scenario RCP8.5, long-duration heatwaves following strong TCs may increase sharply. The expected percentage of Harris residents experiencing at least one longer-than-5-day TC-blackout-heatwave compound hazard in a 20-year period could increase dramatically by a factor of 23 (from 0.8% to 18.2%) over the 21st century. We also reveal that a moderate enhancement of the power distribution network can significantly mitigate the compound hazard risk. Thus, climate adaptation actions, such as strategically undergrounding distribution network and developing distributed energy sources, are urgently needed to improve coastal power system resilience. The study found that long-duration heatwaves are much more likely to follow power-damaging tropical cyclones in the future RCP8.5 climate, with the impact of longer-than-5-day tropical cyclone-blackout-heatwave compound hazard increasing by a factor of 23 over the 21st century.
While conceptual definitions have provided a foundation for measuring inequality of access and resilience in urban facilities, the challenge for researchers and practitioners alike has been to develop analytical support for urban system development that reduces inequality and improves resilience. Using 30 million large-scale anonymized smartphone-location data, here, we calibrate models to optimize the distribution of facilities and present insights into the interplay between equality and resilience in the development of urban facilities. Results from ten metropolitan counties in the United States reveal that inequality of access to facilities is due to the inconsistency between population and facility distributions, which can be reduced by minimizing total travel costs for urban populations. Resilience increases with more equitable facility distribution by increasing effective embeddedness ranging from 10% to 30% for different facilities and counties. The results imply that resilience and equality are related and should be considered jointly in urban system development. npj Urban Sustainability (2022) 2:9 ; https://doi.
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.
This research establishes a methodological framework for quantifying community resilience based on fluctuations in a population's activity during a natural disaster. Visits to points-of-interests (POIs) over time serve as a proxy for activities to capture the combined effects of perturbations in lifestyles , the built environment and the status of business. This study used digital trace data related to unique visits to POIs in the Houston metropolitan area during Hurricane Harvey in 2017. Resilience metrics in the form of systemic impact, duration of impact, and general resilience (GR) values were examined for the region along with their spatial distributions. The results show that certain categories, such as religious organizations and building material and supplies dealers had better resilience metrics-low systemic impact, short duration of impact, and high GR. Other categories such as medical facilities and entertainment had worse resilience metrics-high sys-temic impact, long duration of impact and low GR. Spatial analyses revealed that areas in the community with lower levels of resilience metrics also experienced extensive flooding. This insight demonstrates the validity of the approach proposed in this study for quantifying and analysing data for community resilience patterns using digital trace/location-intelligence data related to population activities. While this study focused on the Hous-ton metropolitan area and only analysed one natural hazard, the same approach could be applied to other communities and disaster contexts. Such resilience metrics bring valuable insight into prioritizing resource allocation in the recovery process.
While conceptual definitions provide a foundation for the study of disasters and their impacts, the challenge for researchers and practitioners alike has been to develop objective and rigorous measures of resilience that are generalizable and scalable, taking into account spatiotemporal dynamics in the response and recovery of localized communities. In this paper, we analyze mobility patterns of more than 800,000 anonymized mobile devices in Houston, Texas, representing approximately 35% of the local population, in response to Hurricane Harvey in 2017. Using changes in mobility behavior before, during, and after the disaster, we empirically define community resilience capacity as a function of the magnitude of impact and time-to-recovery. Overall, we find clear socioeconomic and racial disparities in resilience capacity and evacuation patterns. Our work provides new insight into the behavioral response to disasters and provides the basis for data-driven public sector decisions that prioritize the equitable allocation of resources to vulnerable neighborhoods.
Healthcare and education systems have been identified by various national and international organizations as the main pillars of communities’ stability. Understanding the correlation between these main social services institutions is critical to determining the tipping point of communities following natural disasters. Despite being defined as social services stability indicators, to date, no studies have been conducted to determine the level of interdependence between schools and hospitals and their collective influence on their recoveries following extreme events. In this study, we devise an agent-based model to investigate the complex interaction between healthcare and education networks and their overall recovery, while considering other physical, social, and economic factors. We employ comprehensive models to simulate the functional processes within each facility and to optimize their recovery trajectories after earthquake occurrence. The results highlight significant interdependencies between hospitals and schools, including direct and indirect relationships, suggesting the need for collective coupling of their recovery to achieve full functionality of either of the two systems following natural disasters. Recognizing this high level of interdependence, we then establish a social services stability index, which can be used by policymakers and community leaders to quantify the impact of healthcare and education services on community resilience and social services stability.
Recent climatic disasters have shown the vulnerability of transportation infrastructures against natural hazards. To understand the risk of coastal hazards on urban travel activities, this study presents an activity-based modeling approach to evaluate the impacts of storm surge on the transportation network under sea-level rise in Miami-Dade County, FL. A Markov-Chain Monte Carlo (MCMC) based algorithm is applied to generate population attributes and travel diaries in the model simulation. Flooding scenarios in 2045 are developed based on different adaptation standards under the 100-year storm surge and population projections from the land-use conflict identification strategy (LUCIS) model. Our analysis indicates that about 29.3% of the transportation infrastructure, including areas of the US No. 1 highway, roadways in the south and southwest of the County, and bridges connecting Miami Beach and Miami-Dade County, will be damaged under the storm surge when a low-level adaptation standard is chosen. However, the high-level adaptation standard will significantly reduce the vulnerable infrastructures to 12.4%. Furthermore, the total increased travel time of the low-level adaptation standard could be as high as twice of the high-level adaptation standard during peak morning hours. Our model results also reveal that the average increased travel time due to future storm surge damage ranges between 14.2 and 62.8 minutes per trip.
In recent years, extreme shocks, such as natural disasters, are increasing in both frequency and intensity, causing significant economic loss to many cities around the world. Quantifying the economic cost of local businesses after extreme shocks is important for post-disaster assessment and pre-disaster planning. Conventionally, surveys have been the primary source of data used to quantify damages inflicted on businesses by disasters. However, surveys often suffer from high cost and long time for implementation, spatio-temporal sparsity in observations, and limitations in scalability. Recently, large scale human mobility data (e.g. mobile phone GPS) have been used to observe and analyze human mobility patterns in an unprecedented spatio-temporal granularity and scale. In this work, we use location data collected from mobile phones to estimate and analyze the causal impact of hurricanes on business performance. To quantify the causal impact of the disaster, we use a Bayesian structural time series model to predict the counterfactual performances of affected businesses (what if the disaster did not occur?), which may use performances of other businesses outside the disaster areas as covariates. The method is tested to quantify the resilience of 635 businesses across 9 categories in Puerto Rico after Hurricane Maria. Furthermore, hierarchical Bayesian models are used to reveal the effect of business characteristics such as location and category on the long-term resilience of businesses. The study presents a novel and more efficient method to quantify business resilience, which could assist policy makers in disaster preparation and relief processes.
Over 30 years have passed since activity-based travel demand models (ABMs) emerged to overcome the limitations of the preceding models which have dominated the field for over 50 years. Activity-based models are valuable tools for transportation planning and analysis, detailing the tour and mode-restricted nature of the household and individual travel choices. Nevertheless, no single approach has emerged as a dominant method, and research continues to improve ABM features to make them more accurate, robust, and practical. This paper describes the state of art and practice, including the ongoing ABM research covering both demand and supply considerations. Despite the substantial developments, ABM’s abilities in reflecting behavioral realism are still limited. Possible solutions to address this issue include increasing the inaccuracy of the primary data, improved integrity of ABMs across days of the week, and tackling the uncertainty via integrating demand and supply. Opportunities exist to test, the feasibility of spatial transferability of ABMs to new geographical contexts along with expanding the applicability of ABMs in transportation policy-making.
Recent studies have discussed lifeline resilience factors for industrial sectors. However, these results are derived from business surveys on hypothetical disasters or estimations based on expert opinions: this is mainly due to a lack of data from businesses that actually experienced lifeline disruptions after a disaster. Therefore, this study proposes a modeling framework to estimate the remaining proportions of production capacity after lifeline disruptions due to a disaster, called lifeline resilience factors, in different business sectors. Lifeline resilience factors are estimated using a production function based on responses from a post-disaster business survey in areas affected by the 2011 Great East Japan Earthquake. A comparative study between the manufacturing and non-manufacturing sectors was conducted to understand their relative resilience and vulnerability. The results show the varying importance of different lifeline services among sectors and are consistent with those of existing studies. These findings make a significant contribution in confirming the reliability and stability of lifeline services for post-disaster economic impact analysis.
Natural disasters may have catastrophic and long‐lasting impacts on communities' physical, economic, and social infrastructure. Slow recovery of educational services following such events is likely to cause traumatic stress in children, lead families to out‐migrate, and affect the community's overall social stability. Methods for quantifying and assessing the restoration process of educational systems and their dependencies on other supporting infrastructure have not received adequate attention. This study introduces, for the first time, a new framework to evaluate the functionality, recovery, and resilience of a school system following severe earthquake events. The framework considers both the quantity and quality of education services provided, school enrollment, and staff employment, as well as the interaction between various agents such as staff, students, parents, administration, and community. A virtual testbed community, Centerville, is utilized to highlight the application of this framework. The impact of school reopening policies on the number of students enrolled as well as the potential for homeschooling is also considered. The availability of various enrollment alternatives for students, backup classroom space and functioning utility systems, and facilitation of staff and supplies transfer between schools substantially increase the resilience of the education service.
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.
Rapid identification of infrastructure disruptions during a disaster plays an important role in restoration and recovery operations. Due to the limitations of using physical sensing technologies, such as the requirement to cover a large area in a short period of time, studies have investigated the potential of social sensing for damage/disruption assessment following a disaster. However, previous studies focused on identifying whether a social media post is damage related or not. Hence, advanced methods are needed to infer actual infrastructure disruptions and their locations from such data. In this paper, we present a multilabel classification approach to identify the co‐occurrence of multiple types of infrastructure disruptions considering the sentiment toward a disruption—whether a post is reporting an actual disruption (negative), or a disruption in general (neutral), or not affected by a disruption (positive). In addition, we propose a dynamic mapping framework for visualizing infrastructure disruptions. We use a geo‐parsing method that extracts location from the texts of a social media post. We test the proposed approach using Twitter data collected during hurricanes Irma and Michael. The proposed multilabel classification approach performs better than a baseline method (using simple keyword search and sentiment analysis). We also find that disruption‐related tweets, based on specific keywords, do not necessarily indicate an actual disruption. Many tweets represent general conversations, concerns about a potential disruption, and positive emotion for not being affected by any disruption. In addition, a dynamic disruption map has potential in showing county and point/coordinate level disruptions. Identifying disruption types and their locations is vital for disaster recovery, response, and relief actions. By inferring the co‐occurrence of multiple disruptions, the proposed approach may help coordinate among infrastructure service providers and disaster management organizations.
Urban systems, composed of households, businesses, and infrastructures, are continuously evolving and expanding. This has several implications because the impacts of disruptions, and the complexity and interdependence of systems, are rapidly increasing. Hence, we face a challenge in how to improve our understanding about the interdependencies among those entities, as well as their responses to disruptions.
The aims of this study were to (1) create an agent that mimics the metabolism of a business or household that obtains supplies from and provides output to infrastructure systems; (2) implement a network of agents that exchange resources, as coordinated with a price mechanism; and (3) test the responses of this prototype model to disruptions.
Our investigation resulted in the development of a business/household agent and a dynamically self-organizing mechanism of network coordination under disruption based on costs for production and transportation. Simulation experiments confirmed the feasibility of this new model for analyzing responses to disruptions. Among the nine disruption scenarios considered, in line with our expectations, the one combining the failures of infrastructure links and production processes had the most negative impact. We also identified areas for future research that focus on network topologies, mechanisms for resource allocation, and disruption generation.
COVID-19 (Corona Virus Disease 2019) has significantly resulted in a large number of psychological consequences. The aim of this study is to explore the impacts of COVID-19 on people’s mental health, to assist policy makers to develop actionable policies, and help clinical practitioners (e.g., social workers, psychiatrists, and psychologists) provide timely services to affected populations. We sample and analyze the Weibo posts from 17,865 active Weibo users using the approach of Online Ecological Recognition (OER) based on several machine-learning predictive models. We calculated word frequency, scores of emotional indicators (e.g., anxiety, depression, indignation, and Oxford happiness) and cognitive indicators (e.g., social risk judgment and life satisfaction) from the collected data. The sentiment analysis and the paired sample t-test were performed to examine the differences in the same group before and after the declaration of COVID-19 on 20 January, 2020. The results showed that negative emotions (e.g., anxiety, depression and indignation) and sensitivity to social risks increased, while the scores of positive emotions (e.g., Oxford happiness) and life satisfaction decreased. People were concerned more about their health and family, while less about leisure and friends. The results contribute to the knowledge gaps of short-term individual changes in psychological conditions after the outbreak. It may provide references for policy makers to plan and fight against COVID-19 effectively by improving stability of popular feelings and urgently prepare clinical practitioners to deliver corresponding therapy foundations for the risk groups and affected people.
The increasing frequency of extreme weather events poses ever greater challenges to urban resilience and residents’ quality of life. Despite a growing trend advocating for an anthropocentric approach to urban resilience, there remains an inadequate understanding of the evolving hierarchical needs of residents during post-disaster periods, especially considering the interplay with infrastructure service restoration. This study aims to address the gap by elucidating how emergency governance and urban infrastructure repairs can effectively address critical residents’ needs, providing empirical insights for improved resource allocation in infrastructure rush-repair scenarios. First, we categorize residents’ needs into three layers (safety and health, social livelihood and civic engagement) using Latent Dirichlet Allocation topic modeling. Subsequently, we present an urban resilience assessment framework that traces the recovery of residents’ needs alongside dynamic infrastructural functionality restoration, benchmarking against pre-disaster levels. Additionally, hypernetwork analyses are adopted to identify critical and evolving patterns of residents’ post-disaster needs over time. The robustness of our proposed framework is validated through its application to a dataset comprising 220,567 records from residents’ appeals during three recurrent rainfall events in Beijing. Theoretically, this study models the dynamic interactions between residents’ needs and infrastructure response during urban post-disaster recovery. Practically, the pinpointed critical needs guide efficient infrastructure rush-repairs and proactive disaster prevention in infrastructure maintenance.
In 2015, the U.S National Institute of Standards and Technology (NIST) funded the Center of Excellence for Risk-Based Community Resilience Planning (CoE), a fourteen university-based consortium of almost 100 collaborators, including faculty, students, post-doctoral scholars, and NIST researchers. This paper highlights the scientific theory behind the state-of-the-art cloud platform being developed by the CoE - the Interdisciplinary Networked Community Resilience Modeling Environment (IN-CORE). IN-CORE enables communities, consultants, and researchers to set up complex interdependent models of an entire community consisting of people, businesses, social institutions, buildings, transportation networks, water networks, and electric power networks and to predict their performance and recovery to hazard scenario events, including uncertainty propagation through the chained models. The modeling environment includes a detailed building inventory, hazard scenario models, building and infrastructure damage (fragility) and recovery functions, social science data-driven household and business models, and computable general equilibrium (CGE) models of local economies. An important aspect of IN-CORE is the characterization of uncertainty and its propagation throughout the chained models of the platform.Three illustrative examples of community testbeds are presented that look at hazard impacts and recovery on population, economics, physical services, and social services. An overview of the IN-CORE technology and scientific implementation is described with a focus on four key community stability areas (CSA) that encompass an array of community resilience metrics (CRM) and support community resilience informed decision-making. Each testbed within IN-CORE has been developed by a team of engineers, social scientists, urban planners, and economists. Community models, begin with a community description, i.e., people, businesses, buildings, infrastructure, and progresses to the damage and loss of functions caused by a hazard scenario, i.e., a flood, tornado, hurricane, or earthquake. This process is accomplished through chaining of modular algorithms, as described. The baseline community characteristics and the hazard-induced damage sets are the initial conditions for the recovery models, which have been the least studied area of community resilience but arguably one of the most important. Communities can then test the effect of mitigation and/or policies and compare the effects of “what if” scenarios on physical, social, and economic metrics with the only requirement being that the change much be able to be numerically modeled in IN-CORE.
In this paper, a comprehensive community‐level model is developed advancing the use of agent‐based modeling for the purpose of community resilience planning. Agents to simulate businesses, healthcare system, and people are introduced, which are utilized along with a set of developed agents for the education system, utilities, and households to create an agent‐based model of the Centerville testbed subjected to tornado hazards. Resilience measures are also introduced to quantify the resilience of the community and its systems and make it possible to assess various strategies related to pre‐disaster preparedness for informed decision‐making. The results show the capability of the proposed computational model to capture the complex behaviors and dynamics of interdependent systems within a community and to evaluate the impact of disruptions on the community as well as a series of resilience measures (mitigation strategies) for the purposes of community planning. Therefore, the proposed model and its findings could provide a new tool for interested community stakeholders and decision‐makers assessing mitigation strategies to enhance the recovery time and resilience of the community.
This study integrates welfare, a measure of the impact of road network disruption on individual commuters’ well-being, with a probabilistic seismic risk assessment framework in a computationally tractable way. Welfare is a network performance measure that reflects the differential impacts of changes in commute time on various groups. For a case study of the San Francisco Bay Area, welfare loss is computed by augmenting an origin–destination matrix with publicly available information about commuters’ income levels, residences, and workplaces. While commuters from all income groups have similar risk of drivers’ delay due to road network disruption, commuters with low incomes have a substantially higher risk of welfare loss than those with high incomes. A comparison of bridge retrofit policies shows that disaggregation of welfare loss by income group is necessary to examine whether such policies reduce risk equitably. While a retrofit policy determined using drivers’ delay reduces the expected drivers’ delay, it increases the disparity in the per-capita welfare loss of commuters with low and high incomes relative to the network’s baseline state. In contrast, a retrofit policy that prioritizes low-income commuters reduces the difference in welfare loss of commuters with low and high incomes compared to the baseline network state.
Infrastructure outages after disasters can cause contrasting impacts throughout a community due to differing vulnerabilities. The identification of critical social impact nodes for use in infrastructure risk mitigation planning and restoration will allow for overall disaster impact reduction and lessening of impact disparity across a community. To achieve criticality identification, this paper presents a probabilistic methodology to quantify the service outage impact of every node. Modelling of impact due to outages is accomplished by fixating outage occurrence in existing impact models and randomly sampling other input variables. The impact variability due to each node’s outage is captured through conditional impact probability density functions (CIPDFs) that are first developed with Monte Carlo simulation at base nodes. Characterization of upstream CIPDFs is achieved through convolution of downstream nodes’ CIPDFs. Mean impact curves/surfaces are developed from CIPDF and nodal vulnerability information for all components to further characterize the impact expected at a certain hazard level. Criticality identification using CIPDFs and mean impact curves/surfaces is presented for both pre-event and post-event situations. The methodology is applied for electric power network criticality identification for household dislocation impact in Galveston, TX, USA in a hurricane event.
The objective of this study is to examine spatial patterns of disaster impacts and recovery of communities based on fluctuations in credit card transactions (CCTs). Such fluctuations could capture the collective effects of household impacts, disrupted accesses, and business closures and thus provide an integrative measure for examining disaster impacts and community recovery. Existing studies depend mainly on survey and sociodemographic data for disaster impacts and recovery effort evaluations, although such data has limitations, including large data collection efforts and delayed timeliness results. Also, there are very few studies have concentrated on spatial patterns of disaster impacts and short-term recovery of communities, although such investigation can enhance situational awareness during disasters and support the identification of disparate spatial patterns of disaster impacts and recovery in the impacted regions. This study examines CCTs data Harris County (Texas, USA) during Hurricane Harvey in 2017 to explore spatial patterns of disaster impacts and recovery duration from the perspective of community residents and businesses at ZIP-code and county scales, respectively, and to further investigate their spatial disparities across ZIP codes. The results indicate that individuals in ZIP codes with populations of higher income experienced more severe disaster impact and recovered more quickly than those located in lower income ZIP codes for most business sectors. Our findings not only enhance the understanding of spatial patterns and disparities in disaster impacts and recovery for better community resilience assessment but also could benefit emergency managers, city planners, and public officials in enhanced situational awareness and resource allocation.
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 © 2022 Computer-Aided Civil and Infrastructure Engineering Comput Aided Civ Inf. 2022;1-30. wileyonlinelibrary.com/journal/mice 1
This paper introduces an agent-based modeling (ABM) approach to model a community as a system of interdependent systems for studying education systems resilience, one of the least-studied components of communities in the quantitative disaster literature. In this ABM approach, autonomous entities, called agents, simulate the components of a system, while internal interactions among them shape the system and external interactions among systems shape the community. To study the education system resilience subject to tornado hazard, a library of agents is proposed including school, household, electric power network, water supply network, and construction companies agents. The proposed ABM approach is applied on the virtual Centerville community. A Monte Carlo sampling analysis for various tornado intensities is conducted to account for and quantify the inherent uncertainties. Moreover, an education system resilience measure based on the education quality and quantity is proposed and computed for Centerville. The probabilities of the education system falling in different resilience levels are also computed for different tornado intensities. Taking advantage of the comprehensive quantitative model proposed, decisions for enhancing the education system resilience can be evaluated, which is demonstrated by assessing the effect of providing backup utilities for schools on the education system resilience.
Transportation infrastructure are critical systems supporting community well-being. Its impairment or failure due to hazardous events can lead to societal consequences which are typically challenging to identify, properly model, and quantify. Additionally, these consequences can exacerbate pre-existing distributive inequalities. The Capability Approach has been used to identify and quantify societal consequences in post-disaster scenarios. This paper introduces novel connectivity-based metrics within a Capability Approach framework to quantify well-being and distributive justice in the aftermath of a hazardous event. The paper proposes a methodology to link the ability of individuals to maintain health, be sheltered, be mobile, be educated, or earn income with the loss or reduction of functionality of transportation infrastructure, investigating both the short-term response (immediate impact), and the long-term recovery on the capabilities. As an example, the paper applies the proposed metrics to a real community subject to a seismic hazard. Results show that the different capabilities are impacted both in the response and recovery phases to different degrees.
The well-being of society can be severely impacted by infrastructure disruptions. This study proposes a novel mathematical model to estimate the societal impact of water disruption quantitatively from two aspects: the percentage of people who can perform certain water-related activities and the percentage of people who are intolerant to disrupted activities. The model incorporates the tolerance level (TL) to establish a suffering level function of the disrupted activity. Then, from the individual's perspective, an activity estimation model is developed to predict an individual's choice for activities with limited water due to infrastructure disruptions, and this model is mainly driven by prioritizing activities with the maximum suffering level. To quantify the societal impact in regions, a Monte Carlo simulation is adopted to take samplings for TL of simulated residents following lognormal or Weibull distributions, and the activity estimation model is conducted for each simulated resident; consequently, societal impacts can be aggregated and derived. Additionally, an illustrative case study of Osaka and sensitivity analyses are performed; the results validated the model's effectiveness and applicability. The proposed model provides insightful information to support emergency management and can be integrated with resilience models of infrastructure to better build human-centric sustainable and resilient cities.
Flood management is particularly important for many urban territories. In order to assess the interest of different risk mitigation strategies, it is necessary to consider the human behavior. Different models and approaches are available to model and simulate people behaviors during a flood event. Despite the considerable recent progress of these models, none of them succeeds in answering all the required challenges: (1) simulate the flood event, (2) integrate geographical information, (3) take into account complex behaviors for inhabitants considering emotion and partial knowledge, (4) enable different strategies for inhabitants, (5) take into account the degradation of infrastructures and its impact on their functioning, and (6) ensure the genericity and flexibility of the model in order to be able to apply it to any territory. In order to meet these challenges, we provide a new Agent based Model, called SiFlo. In this paper, we present this model and an application to La Ciotat (France).
Metro systems serve large populations and form extensive networks. Incidents such as signal failure, train failure, and power failure, pose great challenges to the reliable operation of metro systems around the world. For the Beijing metro system, incidents caused 408 disruptions of train services from 2014 to 2018. These incidents are investigated in detail, and a Monte Carlo approach and incident parameter functions are used to generate stochastically simulated incident events. Based on the simulated incidents, combined passenger flow data and an input-output model, we estimate the risk associated with the Beijing metro system in terms of disrupted passenger flows by considering risk propagation in the network, where both direct passenger loss and indirect passenger flow loss considering interchanges between different lines are considered. Lines at high risk are identified, and a sensitivity analysis is performed to investigate the effects of risk mitigation measures. This study provides a generic risk modeling method for urban metro systems and can improve decision making to manage metro system risk.
Increasing reliance on uninterrupted electricity supply against emerging threats such as climate change and cyberattacks calls for higher resilience of societies against power disruptions. A better understanding of social and economic impacts during these disruptions would be important for planning of resilience improvements. However, traditional energy system models rarely include these aspects. This paper presents an integrated framework containing a geospatial power system operation model, capable of emulating system component failures and restoration according to environmental conditions, with a link to spatial social and economic values such as population, economic activity, critical services and facilities. The framework was applied for analyzing the effects of uncontrolled and controlled power outages for two windy winter weeks in Finland. This case illustrated how controlled optimization could reduce the societal costs of such outage by shifting power shortage to regions where such costs are lower and in part by shifting the costs to other factors.
In natural hazard engineering, fragility curves are used to determine the likelihood of damage to an engineered system under different magnitudes of hazard intensity. Analogous to fragility curves for engineered systems, survival models developed in the present study determine the extent of disturbances for shelter-in-place households caused by infrastructure service disruptions during disasters. This study used empirical data from household surveys collected in the aftermath of Hurricane Harvey, Hurricane Florence, and Hurricane Michael to create empirical survival models for determining household-level disturbances related to eight infrastructure services: power, water, communication, sewer systems, transportation, solid waste collection, grocery stores, and healthcare facilities. The survival models considered various influencing factors, such as sociodemographic factors, previous experience, risk perception, and access to resources to determine what percentage of households in a community would experience considerable hardship under varying durations of service disruptions. The developed curves suggested that although the susceptibility patterns are similar for short durations of infrastructure service disruptions, prolonged service disruptions pose varying levels of disturbance in different communities based on the household characteristics and contextual factors. Susceptibility curves could be implemented with current tools for assessing the reliability and resilience of infrastructure systems to promote understanding of the societal impacts that disruptions in these services pose to the affected communities. The resulting empirical survival models provide necessary tools and insights for determining the susceptibility of communities to disruptions of various infrastructure services during disasters. Hence, the outcomes of this study provide new empirical insights and models enabling decision-makers to integrate human-centric dimensions into infrastructure retrofit and restoration processes to more equitably reduce societal impacts of service disruptions. Such human-centric approaches enable designing socially resilient cities and contribute to designing sustainable infrastructure systems.
This paper provides empirical information for understanding the susceptibility of households to the infrastructure service disruptions caused by natural hazards. Understanding household-level susceptibility is critical to determine the risks associated with staying shelter-in-place during disasters. This information is essential for various stakeholders such as community leaders, emergency planners, and utility managers to prioritize and restore infrastructure services for the public. However, there is limited empirical information regarding the susceptibility of shelter-in-place households to the disruptions of different services. Hence, this study presents an exploratory analysis of empirical data collected from affected communities to identify the influencing factors of the households’ susceptibility. We utilized survey data collected from Hurricane Harvey (850 respondents), Hurricane Florence (362 respondents), and Hurricane Michael (583 respondents) to study the anatomy of susceptibility to eight infrastructure service disruptions. The descriptive analysis compared the similarities, such as rating the sewer and water systems as most important services, and differences, such as the varying levels of expectation for the service disruptions, of the three regions impacted by the disasters. Correlation analysis considered which underlying factors, including sociodemographic characteristics, protective actions and adjustments, previous experience and previous damage, social capital, and need for service of individual households along with the contextual and communal factors of the community, such as urbanization and previous disaster declarations, were associated with the ability of residents to respond to and withstand service disruptions. Although there were consistencies in the relationship of influencing factors to the level of susceptibility, the findings highlight that some variation in the influence of these factors was event-specific or service-specific. Finally, the contextual and communal factors of a community can bring unique insights to the anatomy of susceptibility to the service disruptions, as each location has inherent characteristics that would, directly and indirectly, influence households’ susceptibility to service disruptions. These findings provide the necessary empirical information to inform infrastructure prioritization decisions and emergency response actions to reduce the societal impacts of infrastructure service disruptions on vulnerable populations.
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.
Building resilience in critical infrastructures for smart and connected cities requires consideration of different types of interdependencies. Previous research has mainly conceptualized three types of interdependencies including cyber, physical, and social. To develop resilient and sustainable design, operations, and managerial strategies, domain knowledge for each infrastructure along with its organizational characteristics needs to be integrated with those of other infrastructures. In this review paper, an infrastructure-oriented approach is taken to systematically examine different types of interdependencies and resilience quantification techniques for water, transportation, and cyber infrastructures. Design, operations, and managerial strategies are identified and categorized into short-term, mid-term, and long-term plans that can potentially improve the resilience of the underlying infrastructures. Future research needs, in terms of resilience metrics, interdependency, and strategies, are discussed.
The objective of this study was to examine social inequality in exposure and hardship experienced by various groups due to infrastructure service disruptions in disasters. After more than two decades, the existing literature related to infrastructure resilience mainly focuses on system performance and considers the impacts of service disruptions to be equal for the public. The public, however, is not a monolithic entity, and different subpopulations have distinct needs and expectations of infrastructure systems. Thus, the same duration of service loss will not be experienced equally by the affected residents. Social subpopulations in a community have preexisting differences, or sociodemographic characteristics, which account for differential variations in disaster experience, and often socially vulnerable groups are disproportionally affected. Unfortunately, there is limited empirical information regarding inequity in the societal impacts of infrastructure service disruptions during disasters. This study addresses this knowledge gap by developing an equitable infrastructure resilience approach that integrates both the physical characteristics of the infrastructure systems and the sociodemographic characteristics that contribute to risk disparity experienced by individual households. The risk disparity was assessed by considering both the duration of the service disruptions (exposure) and people's ability to withstand disruptions (zone of tolerance). The study investigated empirical data related to the transportation, power, communication, and water service disruptions caused by Hurricane Harvey in 2017 for Harris County residents. The results concluded that certain socially vulnerable groups reported significant disparity in the hardship people experienced due to infrastructure service disruptions caused by the disaster. The significant experienced hardship was rooted in the group's having a lower zone of tolerance for service disruptions, experiencing a significantly higher duration of service outages, or a coupling effect when there was both greater exposure and lower zone of tolerance. The findings further revealed the following: (1) households with low socioeconomic status reported a coupling effect for communication and water disruptions and reported a lower zone of tolerance for transportation and power disruptions; (2) racial minority groups reported a coupling effect for transportation, communication, and water disruptions and a lower zone of tolerance for power disruption; and (3) households with younger residents reported a coupling effect for communication disruption, greater exposure to transportation and water disruptions, and lower zone of tolerance for power disruption. The findings uncovered existing inequalities in exposure and hardship experienced due to infrastructure service disruptions for various vulnerable subpopulations. Hence, the study establishes the fundamental knowledge and empirical information needed for an equitable resilience approach in infrastructure systems in order to better prioritize investments and therefore effectively reduce the risk disparity of vulnerable populations during service disruptions.
The premise of community resilience hinges on preventing extreme events from becoming disasters through minimizing initial disruptions and ensuring quick recovery of the various community sectors. Recovery of critical facilities and public assemblies, such as the healthcare systems, is particularly important since they are vital for short-term and long-term functioning of communities. This article outlines a new framework for estimating full functionality and recovery of healthcare systems in a community following earthquake occurrence. The presented framework includes estimation of both quantity and quality components of the offered healthcare service overtime and quantification of patient demand on each healthcare facility while accounting for their interdependence as well as their interaction with other community infrastructure. When estimating the recovery of healthcare services, stochastic modeling and dynamic optimization are utilized to account for limited repair resources, repair sequences, and change in demand over time. The presented approach is applied to Centerville, a virtual testbed community, to highlight the capabilities of the proposed framework and the impact of decisions made on the recovery trajectory. It is observed that high level of interaction between the healthcare system components is essential to reduce patient demands on hospitals. It is also shown that proper allocation and distribution of repair resources are key to achieving the desired level of functionality for the hospitals.
An agent-based object-oriented model for household displacements is presented and used to analyze household decision-making after a hypothetical earthquake in the City of Vancouver, Canada. Temporary displacements and permanent relocation are accounted for. The model for households include considerations of socioeconomic demographics, social networks, and disaster preparedness. The analysis results indicate that nearly 70,000 persons are expected to be displaced by the earthquake. Of those, close to 19,000 will need public sheltering. In addition, nearly 40,000 persons are expected to relocate in the years following the earthquake. Among the displaced persons, occupants of multi-family pre-code and low-code buildings are over-represented. Among those needing public shelter or relocation, there is a disproportionately high number of renters and low-income households. The models in this paper can help the development of pre-disaster plans by suggesting optimal location of public shelters, and by identifying decisions that reduce the number of households relocating.
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.
This paper presents a methodology that generates and links high-resolution spatial data on households and housing units with heterogeneous characteristics (i.e., size, tenure status, occupied, and vacant) to residential buildings which in turn are linked to critical infrastructure. The methodology utilizes areal demographic data from the US Census, which are probabilistically linked to an inventory of housing units located in residential buildings. By allocating high-resolution household socio-demographic data to housing units in single and multi-family residential structures themselves linked to critical infrastructure systems, coupled engineering-social science modeling is possible. This paper presents a workflow for linking social science and engineering data to enable integrated models for community resilience. The methodology is applied to Seaside, Oregon, a coastal community with a year-round population of over 6,000 persons. The application highlights the benefits of integrating social science and engineering data. Benefits include facilitating coupled modeling, accounting for uncertainty, visualization, and spatial exploration of modeled results.
Despite an abundance of frameworks and toolkits for assessing resilience performance of interdependent infrastructure systems in recent years, they are generally underpinned by the topological-based approaches with limited consideration on commodities being flowed and supplied to consumers through the entire system. Besides, previous studies on resilience assessment have failed to consider the pre-event component condition of infrastructure systems by implicitly regarding the condition of components as pristine without any deterioration. These could lead to undermine the accuracy of resilience assessment results. To fill these research gaps, the paper synthetically assesses the resilience performance of interdependent infrastructure systems with the condition-varying components leveraging the physics-based approaches to delineate the intricate operating scheme of each infrastructure system. A case study strives to investigate the pre-event resilience performance of interdependent stormwater drainage system and road transport system in Hong Kong against heavy rainfall. The results show that the component deterioration in the stormwater drainage system could cause incremental amount of inundated rainwater in certain manholes and thus lead to substantial degradation in traffic performance in the vicinity when compared to the scenario without considering the pre-event component condition. Simultaneously, certain intersections of the network as well as the spatially subordinated small-scaled network do not seem to be affected while the traffic performance could even be advanced at those intersections. This phenomenon explains the heterogeneity of spatial and temporal traffic patterns within the same network. The results should help guide decision-makers identifying the vulnerabilities, planning pre-event mitigation strategies, and prioritizing recovery resources actions in case hazards occur.
The objective of this paper is to model and examine the impacts of different levels of infrastructure service losses caused by disasters on the households’ well-being residing in a community. An agent-based simulation model was developed to capture complex mechanisms underlying households’ tolerance for the service outages, including household characteristics (e.g., sociodemographic, social capital, resources, and previous disaster experience), physical infrastructure attributes, and extreme disruptive events. The rules governing these mechanisms were determined using empirical survey data collected from the residents of Harris County affected by Hurricane Harvey as well as the existing models for power outages and service restoration times. The analysis results highlighted the spatial diffusion of service risks among households living in affected areas in disasters. The proposed simulation model will provide utility agencies with an analytical tool for prioritization of infrastructure service restoration actions to effectively mitigate the societal impacts of service losses.