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The current characterization of flood exposure is largely based on residential location of populations; however, location of residence only partially captures the extent to which populations are exposed to flood hazards. An important, yet under-recognized aspect of flood exposure is associated with human mobility patterns and population visitation to places located in flood prone areas. In this study, we analyzed large-scale, high-resolution location-intelligence data collected from anonymous mobile phone users to characterize human mobility patterns and the resulting flood exposure in coastal counties of the United States. We developed the metric of mobility-based exposure based on dwell time in places located in the 100-year floodplain. The results of examining the extent of mobility-based flood exposure across demographic groups reveal significant disparities across race, income, and education level groups. The results show that Black and Asian, economically disadvantaged, and undereducated populations in US coastal cities are disproportionally exposed to flood due to their daily mobility activities, indicating a pattern contrary to that of residential flood exposure. The results suggest that mobility behaviors play an important role in extending flood exposure reach disproportionally among different socio-demographic groups. The results highlight that urban flood risk assessments should not only focus on the level of flood exposure to residences, but also should consider mobility-based exposure to better learn the disparities in flood exposure among social groups. Mobility-based flood exposure provides a new perspective regarding the extent to which floods could disrupt people's life activities and enable a better characterization of disparity in populations' exposure to flood hazards beyond their place of residence. The findings of this study have important implications for urban planners, flood managers, and city officials in terms of accounting for mobility-based flood exposure in flood risk management plans and actions. [ABSTRACT FROM AUTHOR]

Despite recognition of the influence of socio-spatial networks on disaster recovery, limited data-driven evidence exists regarding the nature of this relationship. To address this knowledge gap, this study investigated the duration of the human mobility recovery process and its relationship with hazard-exposure heterophily (as a measure for the resourcefulness of social ties) and various other socio-demographic characteristics. We applied the concept of hazard-exposure heterophily to the possibility of resource sharing, through social-spatial networks, between communities with different hazard exposures. We used human movement patterns to measure recovery duration. To understand the disparities during recovery, we examined the correlations between recovery duration and the social demographic characteristics of race and income caused by Hurricane Harvey which made landfall in Harris County, Texas, on August 25, 2017. We discerned high/low hazard-exposure heterophily through the use of a threshold-classification method, and we got four clusters according to the flood-inundation percentage and social connectedness values. Then we applied correlation analysis to analyze the relations between hazard-exposure heterophily/income and recovery weeks among different income groups. The results revealed that hazard-exposure heterophily considerably accelerates human mobility recovery in low-income areas, as the hazard-exposure heterophily allows for the increased exchange of knowledge and resources between members of diverse groups, ultimately accelerating the recovery process. The results of this study could benefit local agents to better allocate recovery resources. [ABSTRACT FROM AUTHOR]

• A quantitative approach to examine policy preference conflicts among diverse stakeholders across multiple plans. • Hazard mitigation plan incorporated the most overall stakeholder policy preferences among the four examined plans. • The regional transportation plan incorporated the fewest overall stakeholder policy preferences. • The hazard mitigation plan and the regional conservation plan had the highest level of policy consistency. • The hazard mitigation plan and regional transportation plan had the lowest level of policy consistency. The objective of this paper was to create and test a methodological framework for examining the extent to which various plans captured diverse stakeholder policy preferences related to resilience planning and management of interdependent urban systems. Policy preferences represent what were important for stakeholders and determine the priorities of stakeholders in resilience planning of urban systems. Stakeholders in different urban sectors may have conflicts of policy preferences in the resilience planning process. A comprehensive understanding of the extent to which plans incorporated and reflected policy preferences of different stakeholders would greatly improve the quality of resilience planning. Hence, we proposed a plan evaluation framework to examine the extent to which various plans captured diverse stakeholder policy preferences in resilience planning of interdependent infrastructure systems. We showed the application of the proposed framework in the evaluation of four plans affecting flood resilience planning in the Houston area. The proposed tool could help identify conflicted stakeholder policy preferences in planning and enable evaluation of policy consistency in networks of plans. [ABSTRACT FROM AUTHOR]

Urban flooding disrupts traffic networks, affecting mobility and disrupting residents' access. Flooding events are predicted to increase due to climate change; therefore, understanding traffic network's flood-caused disruption is critical to improving emergency planning and city resilience. This study reveals the anatomy of perturbed traffic networks by leveraging high-resolution traffic network data from a major flood event and advanced high-order network analysis. We evaluate travel times between every pairwise junction in the city and assess higher-order network geometry changes in the network to determine flood impacts. The findings show network-wide persistent increased travel times could last for weeks after the flood water has receded, even after modest flood failure. A modest flooding of 1.3% road segments caused 8% temporal expansion of the entire traffic network. The results also show that distant trips would experience a greater percentage increase in travel time. Also, the extent of the increase in travel time does not decay with distance from inundated areas, suggesting that the spatial reach of flood impacts extends beyond flooded areas. The findings of this study provide an important novel understanding of floods' impacts on the functioning of traffic networks in terms of travel time and traffic network geometry. • High-order network analysis and reveals anatomy of perturbed traffic networks. • Network-wide persistent travel time increase can last for weeks. • Modest flooding causes significant temporal expansion of network. • Distant trips experience greater percentage increase in travel time. • Flood impacts extend beyond inundated areas. [ABSTRACT FROM AUTHOR]

The use of crowdsourced data has been finding practical use for enhancing situational awareness during disasters. While recent studies have shown promising results regarding the potential of crowdsourced data (such as user-generated flood reports) for flash flood mapping and situational awareness, little attention has been paid to data imbalance issues that could introduce biases in data and assessment. To address this gap, in this study, we examine biases present in crowdsourced reports to identify data imbalance with a goal of improving disaster situational awareness. Three biases are examined: sample bias, spatial bias, and demographic bias. To examine these biases, we analyzed reported flooding from 3-1-1 reports (which is a citizen hotline allowing the community to report problems such as flooding) and Waze reports (which is a GPS navigation app that allows drivers to report flooded roads) with respect to FEMA damage data collected in the aftermaths of Tropical Storm Imelda in Harris County, Texas, in 2019 and Hurricane Ida in New York City in 2021. First, sample bias is assessed by expanding the flood-related categories in 3-1-1 reports. Integrating other flooding related topics into the Global Moran's I and Local Indicator of Spatial Association (LISA) revealed more communities that were impacted by floods. To examine spatial bias, we perform the LISA and BI-LISA tests on the data sets—FEMA damage, 3-1-1 reports, and Waze reports—at the census tract level and census block group level. By looking at two geographical aggregations, we found that the larger spatial aggregations, census tracts, show less data imbalance in the results. Through a regression analysis, we found that 3-1-1 reports and Waze reports have data imbalance limitations in areas where minority populations and single parent households reside. The findings of this study advance understanding of data imbalance and biases in crowdsourced datasets that are growingly used for disaster situational awareness. Through addressing data imbalance issues, researchers and practitioners can proactively mitigate biases in crowdsourced data and prevent biased and inequitable decisions and actions. • By expanding the flood-related categories in 3-1-1 reports, the research identified more communities that were impacted by flooding, thus mitigating sample bias. • Spatial analysis techniques are preformed on 3-1-1 reports and Waze reports at the census tract and census block group level to identify which geographical aggregation causes a reduction of spatial data to mitigate spatial bias. • Regression models are preformed on 3-1-1 and Waze reports and found that social demographic traits such as minority status are not represented on crowdsourced platforms. • The application of the proposed analytical framework is demonstrated in two study areas: Harris County during Tropical Storm Imelda in 2019 and New York during Hurricane Ida in 2021. • The proposed framework provides insights on the types of biases that exists within crowdsourced data and how to identify biases. • The proposed method can help cities and flood plain managers identify biases within crowdsourced data to create an equitable flood recovery process. [ABSTRACT FROM AUTHOR]

The major problems of the semiconductor nanomaterials in the photocatalytic applications for degradation of organic contaminants are fast recombination of photogenerated electron-hole pairs and low visible light harvesting. Therefore, we tried to resolve these drawbacks by coupling LaFeO 3 nanoparticles with Bi 2 WO 6 nanoflakes and constructing of binary Z-scheme heterojunction through the facile solvothermal synthesis approach. The photocatalytic activity of the LaFeO 3 /Bi 2 WO 6 heterojunction was studied through degradation of tetracycline (TC) upon visible light irradiation. The effects of experimental parameters, including LaFeO 3 /Bi 2 WO 6 dosage, initial concentration of TC, visible light irradiation time, and solution pH on the photocatalyst performance of binary nanocomposite were analyzed by response surface methodology. The results of analysis of variance confirmed that these parameters have a significant effect on the TC degradation efficiency. The value of the first-order kinetic rate constant for removal of TC using LaFeO 3 /Bi 2 WO 6 was calculated 0.1291 min−1, which was 20 and 10 times higher than pure LaFeO 3 and Bi 2 WO 6 , respectively. Besides, LaFeO 3 /Bi 2 WO 6 was shown the excellent photocatalytic stability after 4 cycles. Furthermore, the photocatalytic pathway for photodegradation of TC by binary LaFeO 3 /Bi 2 WO 6 nanocomposite was proposed according to the trapping experiments of active species and the results were signify that the h+ and . OH are the predominant reactive species for the photodegradation of TC. Overall, we believe that current novel research opens a promising route for the preparation of highly efficient LaFeO 3 /Bi 2 WO 6 photocatalyst and its usage for wastewater treatment. [ABSTRACT FROM AUTHOR]

The ability to measure and track the speed and trajectory of a community's post-disaster recovery is essential to inform resource allocation and prioritization. The current survey-based approaches to examining community recovery, however, have significant lags and put the burden of data collection on affected people. Also, the existing literature lacks quantitative measures for important milestones to inform the assessment of recovery trajectory. Recognizing these gaps, this study uses location-based data related to visitation patterns and credit card transactions to specify critical recovery milestones related to population activity recovery. Using data from 2017 Hurricane Harvey in Harris County (Texas), the study specifies four critical post-disaster recovery milestones and calculates quantitative measurements of the length of time between the end of a hazard event and when the spatial areas (census tracts) reached these milestones based on fluctuations in visits to essential and non-essential facilities, and essential and non-essential credit card transactions. Accordingly, an integrated recovery metric is created for an overall measurement of each spatial area's recovery progression. Exploratory statistical analyses were conducted to examine whether variations in community recovery progression in achieving the critical milestones is correlated to its flood status, socioeconomic characteristics, and demographic composition. Finally, the extent of spatial inequality is examined. The results show the presence of moderate spatial inequality in population activity recovery in Hurricane Harvey, based upon which the inequality of recovery is measured. Results of this study can benefit post-disaster recovery resource allocation as well as improve community resilience towards future natural hazards. [ABSTRACT FROM AUTHOR]

• Period of recovery was quantified from observational power outage data. • Findings evidence social and spatial inequalities in power outage events. • Need for future investment in data collection and analysis of power outages. • Importance of integrating equity practices in power restoration management. Due to the effects of climate change and urbanization, the severity and frequency of hazard events is expected to increase. The energy sector in the United States is ever more vulnerable to extreme climatic hazards. Hurricane winds can damage electrical lines, causing hazard-induced power outages. Extreme heat and freezing temperatures can imbalance the supply and demand for energy resulting in managed power outages. Utility companies reportedly prioritize the restoration of power systems based on the number of outages and the size of affected populations. This approach fails to account for unequal impacts of hazard-induced and managed power outages. Research in equitable infrastructure emphasizes that certain populations, such as lower income and racial-ethnic minority households, are disproportionately impacted by disruptions in the power system. Moreover, the connected network qualities of the power system suggests an element of spatial vulnerabilities. However, little empirical evidence exists regarding the presence and extent of energy inequality. A main roadblock is the data collection process, in that outage data is often perishable and not found at granular spatial scales to allow the undertaking of a comprehensive analysis on impacts of power losses. Recognizing this important gap, this study collected and analyzed observational data related to the managed power outages during Winter Storm Uri (2021) and the hazard-induced outages during Hurricane Ida (2021). The research quantified the period of recovery at a granular spatial scale using an equitable-focused analysis to detect social and spatial inequalities through an exploratory lens. In extreme cases of power outage, census tracts of lower income and higher percentage of Hispanic population had longer median durations of recovery during Winter Storm Uri. In the hazard-induced outages of Hurricane Ida, non-coastal zip codes with lower income had a 1.00-day longer median duration of recovery and higher percentage of Black population had a 2.00-day longer median duration of recovery while coastal zip codes with higher percentage of Black population had a 1.00-day longer median of recovery. Non-coastal regions had 63% greater spatial Gini values and 16% greater value in infrastructure inequality when compared to coastal regions. The managed power outages resulted in a 3% to 19% greater value of infrastructure inequality to the hazard-induced power outages. The findings provide evidence of pervasive social and spatial inequality in power outages during climate hazards and highlight the importance of integrating equity into the manner in which utility managers and emergency planners restore power outages. [ABSTRACT FROM AUTHOR]

The objective of this study is to empirically and systematically assess the combination of inherent susceptibility factors, protective actions, and factors of hazard exposure that influence a household's level of hardship experienced due to disruptions in critical infrastructure services during disasters. Classification and regression tree (CART) decision tree models and survey data from three major hurricane events were used to: (1) identify the pathways leading to impact(s) due to service disruptions and explore the differences in pathways across vulnerable population groups; and (2) identify the points of intervention to mitigate well-being impacts in households due to disruptions in water, energy, food, and road transportation services. The results reveal how the associative pathways between these factors change between socioeconomic and demographic groups in the impacted community and for different infrastructure service system types. The findings suggest that not all vulnerable households experienced high hardship outcomes despite prolonged outages. Finally, the hardship pathways suggest recommendations for improving resilience in infrastructure systems in a more equitable manner. The findings can be used by emergency and infrastructure managers and operators to better prioritize resource allocation for hazard mitigation investments and restorations. Accordingly, this study contributes to the theory of human-centric infrastructure resilience. [ABSTRACT FROM AUTHOR]

This study explores the role of access to essential facilities and emergency services during hazard weather events in shaping individuals' risk perceptions. We develop a framework in which residents' views of required actions facing extreme weather are influenced by their level of access to essential facilities to obtain medical and emergency services. The effect of access is complemented by perceptions about the status of local infrastructure conditions as enabling access. Using a sample of Texas residents collected during April 2021, we demonstrate the role of restricted access and views of local infrastructure conditions as important predictors of increased concerns during natural disasters. The results demonstrate the effects of factors such as access and status of local infrastructure on the risk assessments of individuals in vulnerable communities who face increased risks from extreme weather. Accordingly, the findings advance our understanding of the unexplored relationship between access of essential facilities and risk perceptions, and could inform disaster managers and public officials regarding the importance of evaluating access as an element of public risk perceptions facing extreme weather events. [ABSTRACT FROM AUTHOR]

The importance of public perceptions and their role in climate change adaptation for infrastructure has been highlighted in previous studies. However, public perception of water infrastructure at risk of flooding has not been explicitly addressed. Therefore, the purpose of this study is to investigate flood risk perception, the factors that influence it, and evaluations of water infrastructure systems. To examine this, data were obtained from a public survey of 755 respondents in Miami-Dade County, Florida, United States. Risk perception is measured as three components: worry, awareness, and preparedness. Structural equation modeling was used to develop and test a model tracing the interrelationships between risk perception, disaster experience, satisfaction with infrastructure services, knowledge of water infrastructure, socioeconomic characteristics, and political views. Results show that flood risk perception elements (awareness, worry, preparedness) significantly influence public evaluation of water infrastructure conditions and mediate the impact of flood experience, service satisfaction, and knowledge. Evaluation of water infrastructure is positively associated with knowledge and service satisfaction but negatively with flood experience. The study also confirms the importance of socio-economic characteristics in shaping public risk perception and evaluation of infrastructure. These findings imply multiple ways that decision-makers may enhance flood risk management plans and adaptation processes of water infrastructure systems in coastal urban areas. [ABSTRACT FROM AUTHOR]

Social media are a rich source of situational information useful conveyed with more immediacy conventional by mass media. The content of social media posts and the embedded geo-coordinates in social media analytics can be harnessed to enhance disaster situational awareness; however, the informal nature of social media post content can compromise the accuracy of an event's location. This study explored the consistency of the locations mentioned in disaster-related tweets with their embedded geo-coordinates. Natural language processing and fine-grained analytics approaches enable us to retrieve important information from them. Relating this retrieved information with embedded geolocations promotes understanding of whether this information is reliable and actionable for disaster management teams, researchers, and disaster victims. The efficiency of rescue operations can be significantly improved if misleading or under-informed posts are separated from the correct information posts. Using a semi-supervised event detection approach and manual annotations, this study explored the consistency between the mentioned locations in social media posts and their geo-coordinates using a case study of 4227 tweets posted during Hurricane Harvey in Houston, Texas. Findings show that 64% of the total tweets have mentioned locations in their contents. Also, most tweets mentioning a specific location during Hurricane Harvey are about the location of the author. Specifically, we found a very high consistency level (94%) between the point-type mentioned locations and the geotags in the tweets. Our findings can support the disaster management teams and policymakers in the design and implementation of effective response strategies. [ABSTRACT FROM AUTHOR]

• An adaptive reinforcement learning model is developed to learn human travel behaviors in regular situations. • The model is capable of simulating the traffic conditions and evaluate the perturbations of disasters on urban mobility. • A case study of the 2017 Hurricane Harvey in Houston is conducted to examine the application of the model. • The model and outcomes of applications can inform the public and decision-makers about the response strategies and resilience planning to reduce the impacts of crises on urban mobility. The objective of this study is to propose and test an adaptive reinforcement learning model that can learn the patterns of human mobility in a normal context and simulate the mobility during perturbations caused by crises, such as flooding, wildfire, and hurricanes. Understanding and evaluating human mobility patterns, such as destination and trajectory selection, can inform emerging congestion and road closures raised by disruptions in emergencies. Data related to human movement trajectories are scarce, especially in the context of emergencies, which places a limitation on applications of existing urban mobility models learned from empirical data. Models with the capability of learning the mobility patterns from data generated in normal situations and which can adapt to emergency situations are needed to inform emergency response and urban resilience assessments. To address this gap, this study creates and tests an adaptive reinforcement learning model that can predict the destinations of movements, estimate the trajectory for each origin and destination pair, and examine the impact of perturbations on humans' decisions related to destinations and movement trajectories. Employing millions of trajectory data from INRIX, the application of the proposed model is shown in the context of Houston and the flooding scenario caused by Hurricane Harvey in August 2017. The results show that the model can achieve more than 76% precision and recall at the model learning stage. The mean percentage error of the travel distance in predicted trajectories is 4.29%, compared to the travel distances in empirical data. In addition, predicted density of vehicles in grid cells are negatively associated with the traffic speed on road segments and inundation intensity in grid cells during the flooding. The results from the simulation further show that the model could predict traffic patterns and congestion resulting from urban flooding. The outcomes of the analysis demonstrate the capabilities of the model for analyzing urban mobility during crises, which can inform the public and decision-makers about the response strategies and resilience planning to reduce the impacts of crises on urban mobility. [ABSTRACT FROM AUTHOR]

Hurricane season brings new and complex challenges as we continue to battle the COVID-19 pandemic. In May 2020, the National Oceanic and Atmospheric Administration has predicted nearly twice the normal number of tropical storms and hurricanes this season, while projections of COVID-19 models continue to rise in the United States as the Atlantic hurricane season progresses. Our research examines the critical intersection of hurricane response and public health in Harris County, Texas. We examine a hypothetical case of the 2017 Hurricane Harvey occurring amid the current pandemic. This research uses point of interest visitations as location intelligence data provided by SafeGraph together with Social Vulnerability Index and historical flood data to examine the critical intersection of natural hazard planning and response and the COVID-19 pandemic to assess the risks of a compound hazard situation. COVID-19 transmission hotspots and businesses in a community due to storm preparation activity were identified. The main drivers of transmission risk arise from overall pandemic exposure and increased interpersonal contact during hurricane preparation. Residents of health-risk areas will need to make logistical arrangements to visit alternative medical facilities for treatments related to either COVID-19 or physical impacts, such as injuries, due to the hurricane risks. Points of interest needed for disaster preparation are more likely to be situated in high-risk areas, therefore making cross-community spread more likely. Moreover, greater susceptibility could arise from social vulnerability (socioeconomic status and demographic factors) and disrupted access to healthcare facilities. Results from this study can be used to identify high-risk areas for COVID-19 transmission for prioritization in planning for temporary healthcare centers and other essential services in low-risk areas. Understanding the interplay between disaster preparation and the restrictive environment laid out by the pandemic is critical for community leaders and public health officials for ensuring the population has sufficient access to essential infrastructure services. The findings from this study can help guide the direction of disaster planning and pandemic response strategies and policies. [ABSTRACT FROM AUTHOR]

Pluvial flash floods are fast-moving hazards and causes significant disruptions in urban areas. With the increase in heavy precipitations, the ability to proactively identify flash floods hotspots in cities is critical for flood nowcasting and predictive monitoring of risks. While rainfall runoff models and hydrologic models are useful models for flash flood prediction, these models are computationally expensive and effort intensive to be used for flood nowcasting. To address this challenge, this study presents interpretable machine learning models for predicting urban flash flood hotspots based on intertwined land and built environment features. The task of predicting flash flood hotspots is formulated as a binary classification problem, and three recent flash flood events in U.S. cities are selected for data collection and model validation. Various features related to land and built environment characteristics are constructed using diverse datasets, and the occurrences of flash floods are captured using crowdsource data from the events. Using these features and datasets, the flash flood hotspots of cities are predicted with two ensemble models based on decision trees. The results demonstrate that the models can achieve good accuracy (0.8) in identifying flooded/non-flooded locations. Especially, the models can achieve high true positive rate (0.83–0.89) and low missing rate, demonstrating the methods' practicability for accurately predicting flooded hotspots. The model interpretation results indicate that land features related to hydrological and topological features have greater impacts on flash flood risk, than built environment features. Further analysis reveals that the feature importance, model performance, and model transferability performance vary among cities and localized specifications of the models are needed for accurate prediction of flash flood for a particular city. The data-driven machine learning models presented in this study provide a useful tool for predicting flash flood hotspots based on the intertwined features of land and the built environment in cities to enable nowcasting and proactive monitoring of flash flood hotspots for emergency response and also inform integrated urban design and development towards flash flood risk reduction. • Interpretable machine learning models are utilized to provide reliable prediction of flash flood hotspots. • Built environment variables are extracted for constructing model features. • The models can achieve very good accuracy in identifying flooded/non-flooded locations. • Hydrological and topological features have greater impacts on flash flood risk than other features • Localized specifications of models are needed for accurate flash flood prediction for particular cities [ABSTRACT FROM AUTHOR]

Traffic congestion occurs daily but the transportation network still functions to meet people's travel needs. We propose that a road meets operation requirements as long as the quality of its links (i.e., speed or travel time) satisfies an acceptable threshold. This paper incorporates the link reliability concept into the road travel performance using the link quality threshold to offer new perspectives on network resilience measurement. We introduce two aggregated macroscopic metrics: network reliability scale index, and network stability, based on the link reliability to quantify the road travel performance change in facing external disturbance. We use the temporal traffic-embedded Harris County, TX transportation network during the 2017 Hurricane Harvey as a case study. We show that the proposed metrics can well capture the transportation network performance variation during flooding. Also, we develop an integrated resilience metric that encapsulates the network resistance, recoverability, and rapidity in facing flooding. The results move us closer to better understanding transportation network resilience behavior in different link quality conditions (q). The findings of this research also provide important insights for city planners and traffic operators to examine transportation network resilience through a reliability and stability lens. • Empirical traffic during disaster reveals unique reliability and stability pattern. • Link reliability captures transportation network resilience reduction and recovery. • Distribution of all link's dysfunction time ratio follows a power-law distribution. • Fluctuation of link reliability exceeds the normal range during Hurricane Harvey. • Each city's transportation resilience is differently sensitive to varying link quality thresholds. [ABSTRACT FROM AUTHOR]

Resilience assessment of road networks is essential to ensure the continuity of critical services following hazard events. Regional transportation resilience assessment requires detailed datasets and advanced computational modeling, which are often unavailable in assessments performed in the Global South. In this study, we present a probabilistic regional resilience assessment framework for road networks in contexts where detailed data are not available. The framework captures agency costs, user costs, and environmental costs. The framework enables benefit-cost analysis as well as incorporating climate projection scenarios for resilience investment analysis. The application of the framework is demonstrated in a case study for regional resilience analysis in Haiti as part of the Resilient Urban Transport and Accessibility Project (RUTAP) by the World Bank. The findings show the capabilities of the framework in providing quantitative insights for informed decision-making to improve regional resilience of road networks in the context of Global South. [ABSTRACT FROM AUTHOR]

Social media platforms are playing increasingly critical roles in disaster response and rescue operations. During emergencies, users can post rescue requests along with their addresses on social media, while volunteers can search for those messages and send help. However, efficiently leveraging social media in rescue operations remains challenging because of the lack of tools to identify rescue request messages on social media automatically and rapidly. Analyzing social media data, such as Twitter data, relies heavily on Natural Language Processing (NLP) algorithms to extract information from texts. The introduction of bidirectional transformers models, such as the Bidirectional Encoder Representations from Transformers (BERT) model, has significantly outperformed previous NLP models in numerous text analysis tasks, providing new opportunities to precisely understand and classify social media data for diverse applications. This study developed and compared ten VictimFinder models for identifying rescue request tweets, three based on milestone NLP algorithms and seven BERT-based. A total of 3191 manually labeled disaster-related tweets posted during 2017 Hurricane Harvey were used as the training and testing datasets. We evaluated the performance of each model by classification accuracy, computation cost, and model stability. Experiment results show that all BERT-based models have significantly increased the accuracy of categorizing rescue-related tweets. The best model for identifying rescue request tweets is a customized BERT-based model with a Convolutional Neural Network (CNN) classifier. Its F1-score is 0.919, which outperforms the baseline model by 10.6%. The developed models can promote social media use for rescue operations in future disaster events. • Manually labeled a dataset that trains NLP models for classifying rescue requesting tweets. • Evaluated the performance of 10 different models, including three based on milestone NLP algorithms and seven BERT-based models. • Designed a customized CNN classifier placed on the top of BERT model that achieved best performance. • Laid the foundation of developing future victim finding applications. [ABSTRACT FROM AUTHOR]

The burgeoning availability of sensing technology and location-based data is driving the expansion of analysis of human mobility networks in science and engineering research, as well as in epidemic forecasting and mitigation, urban planning, traffic engineering, emergency response, and business development. However, studies employ datasets provided by different location-based data providers, and the extent to which the human mobility measures and results obtained from different datasets are comparable is not known. To address this gap, in this study, we examined three prominent location-based data sources—Spectus, X-Mode, and Veraset—to analyze human mobility networks across metropolitan areas at different scales: global, sub-structure, and microscopic. Dissimilar results were obtained from the three datasets, suggesting the sensitivity of network models and measures to datasets. This finding has important implications for building generalized theories of human mobility and urban dynamics based on different datasets. The findings also highlighted the need for ground-truthed human movement datasets to serve as the benchmark for testing the representativeness of human mobility datasets. Researchers and decision-makers across different fields of science and technology should recognize the sensitivity of human mobility results to dataset choice and develop procedures for ground-truthing the selected datasets in terms of representativeness of data points and transferability of results. • Analyzed human mobility networks across metropolitan areas at different scales: global, sub-structure, and microscopic with three prominent location-based data sources—Spectus, X-Mode, and Veraset. • Recognize the sensitivity of human mobility results to dataset choice and transferability of results and representativeness of human mobility datasets. [ABSTRACT FROM AUTHOR]

This study uses mobility data in the context of 2017 Hurricane Harvey in Harris County to examine the impact of flooding on access to dialysis centers. We examined access dimensions using static and dynamic metrics. The static metric is the shortest distance from census block groups to the closest centers. Dynamic metrics are: 1) redundancy (daily unique number of centers visited), 2) frequency (daily number of visits to dialysis centers), and 3) proximity (visits weighted by distance to dialysis centers). The results show that: the extent of dependence of regions on dialysis centers varies; flooding significantly reduces access redundancy and frequency of dialysis centers; regions with a greater minority percentage and lower household income were likely to experience extensive disruptions; high-income regions more quickly revert to pre-disaster levels; larger centers located in non-flooded areas are critical to absorbing the unmet demand from disrupted facilities. [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]

The objective of this study is to predict the near-future flooding status of road segments based on their own and adjacent road segments' current status through the use of deep learning framework on fine-grained traffic data. Predictive flood monitoring for situational awareness of road network status plays a critical role to support crisis response activities such as evaluation of the loss of access to hospitals and shelters. Existing studies related to near-future prediction of road network flooding status at road segment level are missing. Using fine-grained traffic speed data related to road sections, this study designed and implemented three spatio-temporal graph convolutional network (STGCN) models to predict road network status during flood events at the road segment level in the context of the 2017 hurricane Harvey in Harris County (Texas, USA). Model 1 consists of two spatio-temporal blocks considering the adjacency and distance between road segments, while model 2 contains an additional elevation block to account for elevation difference between road segments. Model 3 includes three blocks for considering the adjacency and the product of distance and elevation difference between road segments. The analysis tested the STGCN models and evaluated their prediction performance. Our results indicated that model 1 and model 2 have reliable and accurate performance for predicting road network flooding status in near future (e.g., 2–4 h) with model precision and recall values larger than 98% and 96%, respectively. With reliable road network status predictions in floods, the proposed model can benefit affected communities to avoid flooded roads and the emergency management agencies to implement evacuation and relief resource delivery plans • Using high-resolution traffic data to predict near-future road inundation status in urban floods • Adjusting spatio-temporal graph convolutional networks for road network inundation status predictions • Contributing near-future infrastructure failure prediction capability to the smart flood resilience [ABSTRACT FROM AUTHOR]

While several non-pharmacological measures have been implemented for a few months in an effort to slow the coronavirus disease (COVID-19) pandemic in the United States, the disease remains a danger in a number of counties as restrictions are lifted to revive the economy. Making a trade-off between economic recovery and infection control is a major challenge confronting many hard-hit counties. Understanding the transmission process and quantifying the costs of local policies are essential to the task of tackling this challenge. Here, we investigate the dynamic contact patterns of the populations from anonymized, geo-localized mobility data and census and demographic data to create data-driven, agent-based contact networks. We then simulate the epidemic spread with a time-varying contagion model in ten large metropolitan counties in the United States and evaluate a combination of mobility reduction, mask use, and reopening policies. We find that our model captures the spatial-temporal and heterogeneous case trajectory within various counties based on dynamic population behaviors. Our results show that a decision-making tool that considers both economic cost and infection outcomes of policies can be informative in making decisions of local containment strategies for optimal balancing of economic slowdown and virus spread. • Data-driven contact network models are developed to learn social contact network patterns in COVID-19. • The model simulates the epidemic spread with a time-varying contagion model in ten large metropolitan counties in the United States. • The model is capable of evaluating the effectiveness of combinations of mobility reduction, mask use, and reopening policies. • A decision-making tool is provided for making decisions for optimal balancing of economic slowdown and virus spread. [ABSTRACT FROM AUTHOR]

• Observability considerations is neglected in the design of flood sensor networks. • A framework for the assessment of flood control network observability is proposed. • The framework enables monitoring flood status across critical regions. • The framework is tested on flood control network in Harris County, TX. • Results reveal insights from observability consideration in sensor network design. Monitoring the state of infrastructure systems proactively is crucial to ensure their proper functionality during extreme events. Flood control networks are designed to keep communities safe from inundation. Accurately monitoring the inundation status of flood control components could enhance flood situational awareness and risk management during extreme events. However, the design and placement of sensor networks that collect data to monitor the status of flooding do not often consider the principles of observability. We bridge the gap by creating a framework for the assessment of flood control network observability and determining the minimum number and locations of the flood gauges to achieve maximum monitoring across critical regions. We first delineate critical regions that are needed to be observed, then identify feasible solutions on sensor sets, and finally determine the candidate sensor set based on the importance of the nodes that exist in each sensor set. We tested the framework in the context of Harris County, Texas, as a case study. The results show that the current flood gauge placement is not sufficient for comprehensively monitoring the flooding status of critical areas, nor informed by the network observability principles. Results also offer insights to decision-makers to extend the current flood gauge network or design new flood gauge networks more effectively. [ABSTRACT FROM AUTHOR]

This paper presents an integrated infrastructure-policy framework to analyze policy attention on addressing road infrastructure network vulnerability in terms of accessing critical facilities in the aftermath of a flood. Coping with network vulnerability, particularly physical access to various critical facilities and the services they provide, is an essential step in achieving a resilient community. However, the extent to which the network of local plans addresses such vulnerability remains unclear. To bridge this gap, this paper uses the Plan Integration for Resilience Scorecard method to examine the infrastructure-related policy attention in relation to community vulnerability vis-a-vis disrupted access to critical facilities. The proposed framework is tested in a set of super neighborhoods in Houston, Texas. Findings reveal a discrepancy between the policy effort and network vulnerability and identifies strengths and weaknesses of various plans in addressing disrupted access to critical facilities. The framework introduced in this paper provides a tool for stakeholders to evaluate an existing network of plans and identify gaps for future resilience improvement. • Network analysis reveals disparities in community post-disaster access to critical facilities. • Resilience scorecard method can help examine the infrastructure-related policy attention. • Analysis show that policy scores do not target vulnerable districts with low critical facility access. • This paper enables an infrastructure-plan integration analysis framework. [ABSTRACT FROM AUTHOR]