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Ali Mostafavi

Publications and source records attributed to Ali Mostafavi.

At least 73 records · Page 4Linked to original sources
arXiv2024

Beyond Quantities: Machine Learning-based Characterization of Inequality in Infrastructure Quality Provision in Cities

The objective of this study is to characterize inequality in infrastructure quality across urban areas. While a growing of body of literature has recognized the importance of characterizing infrastructure inequality in cities and provided quantified metrics to inform urban development plans, the majority of the existing approaches focus primarily on measuring the quantity of infrastructure, assuming that more infrastructure is better. Also, the existing research focuses primarily on index-based approaches in which the status of infrastructure provision in urban areas is determined based on assumed subjective weights. The focus on infrastructure quantity and use of indices obtained from subjective weights has hindered the ability to properly examine infrastructure inequality as it pertains to urban inequality and environmental justice considerations. Recognizing this gap, we propose a machine learning-based approach in which infrastructure features that shape environmental hazard exposure are identified and we use the weights obtained by the model to calculate an infrastructure quality provision for spatial areas of cities and accordingly, quantify the extent of inequality in infrastructure quality. The implementation of the model in five metropolitan areas in the U.S. demonstrates the capability of the proposed approach in characterizing inequality in infrastructure quality and capturing city-specific differences in the weights of infrastructure features. The results also show that areas in which low-income populations reside have lower infrastructure quality provision, suggesting the lower infrastructure quality provision as a determinant of urban disparities. Accordingly, the proposed approach can be effectively used to inform integrated urban design strategies to promote infrastructure equity and environmental justice based on data-driven and machine intelligence-based insights.

cs.CY↗
arXiv2024

FloodGenome: Interpretable Machine Learning for Decoding Features Shaping Property Flood Risk Predisposition in Cities

Understanding the fundamental characteristics that shape the inherent flood risk disposition of urban areas is critical for integrated urban design strategies for flood risk reduction. Flood risk disposition specifies an inherent and event-independent magnitude of property flood risk and measures the extent to which urban areas are susceptible to property damage if exposed to a weather hazard. This study presents FloodGenome as an interpretable machine learning model for evaluation of the extent to which various hydrological, topographic, and built-environment features and their interactions shape flood risk disposition in urban areas. Using flood damage claims data from the U.S. National Flood Insurance Program covering the period 2003 through 2023 across four metropolitan statistical areas (MSAs), the analysis computes building damage ratios and flood claim counts by employing k-means clustering for classifying census block groups (CBGs) into distinct property flood risk disposition levels. Then a random forest model is created to specify property flood risk levels of CBGs based on various intertwined hydrological, topographic, and built-environment features. The model transferability analysis results show consistent performance across MSAs, revealing the universality of underlying features that shape city property flood risks. The FloodGenome model is then used to:(1) evaluate the extent to which future urban development would exacerbate flood risk disposition of urban areas; and (2) specify property flood risk levels at finer spatial resolution providing critical insights for flood risk management processes. The FloodGenome model and the findings provide novel tools and insights for improving the characterization and understanding of intertwined features that shape flood risk profiles of cities.

cs.CE↗
arXiv2024

Rethinking Urban Flood Risk Assessment By Adapting Health Domain Perspective

Inspired by ideas from health risk assessment, this paper presents a new perspective for flood risk assessment. The proposed perspective focuses on three pillars for examining flood risk: (1) inherent susceptibility, (2) mitigation strategies, and (3) external stressors. These pillars collectively encompass the physical and environmental characteristics of urban areas, the effectiveness of human-intervention measures, and the influence of uncontrollable external factors, offering a fresh point of view for decoding flood risks. For each pillar, we delineate its individual contributions to flood risk and illustrate their interactive and overall impact. The three-pillars model embodies a shift in focus from the quest to precisely model and quantify flood risk to evaluating pathways to high flood risk. The shift in perspective is intended to alleviate the quest for quantifying and predicting flood risk at fine resolutions as a panacea for enhanced flood risk management. The decomposition of flood risk pathways into the three intertwined pillars (i.e., inherent factors, mitigation factors, and external factors) enables evaluation of changes in factors within each pillar enhance and exacerbate flood risk, creating a platform from which to inform plans, decisions, and actions. Building on this foundation, we argue that a flood risk pathway analysis approach, which examines the individual and collective impacts of inherent factors, mitigation strategies, and external stressors, is essential for a nuanced evaluation of flood risk. Accordingly, the proposed perspective could complement the existing frameworks and approaches for flood risk assessment.

cs.AI↗
arXiv2024

Decoding the Pulse of Community during Disasters: Resilience Analysis Based on Fluctuations in Latent Lifestyle Signatures within Human Visitation Networks

Examining the impact of disasters on life activities of populations is critical for understanding community resilience dynamics, yet it remains insufficiently studied in the existing literature. In this study, we leveraged data from more than 1.2 million anonymized human mobility communications across 30 parishes in Louisiana to construct a temporal network that tracks visitation to places from which we characterized human lifestyle signatures before, during, and after Hurricane Ida in 2021. Utilizing the motif model, we distilled complex human lifestyles into identifiable patterns and clustered them into classes: commute, healthcare, dining out, and youth-oriented lifestyle. We defined two metrics to evaluate disruption and recovery fluctuations in lifestyle patterns during the perturbation period compared to the steady period: 1) frequency (daily number of motifs), and 2) proximity (daily average distance of motifs). The results indicate significant dynamics in lifestyle patterns due to the hurricane, with essential facilities (e.g., healthcare) demonstrating a swift recovery. The study underscores the heterogeneity of locations visited and the necessity of integrating both essential and non-essential facilities into disaster response initiatives. Furthermore, our study reveals sustained changes in lifestyle patterns, highlighting the long-term impact of the hurricane on daily life. These insights demonstrate the significance of examining lifestyle signatures and their fluctuations in evaluating disaster resilience patterns for affected communities. The outcomes of this study are poised to aid emergency managers and public officials to more effectively evaluate and monitor disaster impacts and recovery based on changes in lifestyle patterns in the community.

cs.SI↗
arXiv2023

Chasing Fairness in Graphs: A GNN Architecture Perspective

There has been significant progress in improving the performance of graph neural networks (GNNs) through enhancements in graph data, model architecture design, and training strategies. For fairness in graphs, recent studies achieve fair representations and predictions through either graph data pre-processing (e.g., node feature masking, and topology rewiring) or fair training strategies (e.g., regularization, adversarial debiasing, and fair contrastive learning). How to achieve fairness in graphs from the model architecture perspective is less explored. More importantly, GNNs exhibit worse fairness performance compared to multilayer perception since their model architecture (i.e., neighbor aggregation) amplifies biases. To this end, we aim to achieve fairness via a new GNN architecture. We propose \textsf{F}air \textsf{M}essage \textsf{P}assing (FMP) designed within a unified optimization framework for GNNs. Notably, FMP \textit{explicitly} renders sensitive attribute usage in \textit{forward propagation} for node classification task using cross-entropy loss without data pre-processing. In FMP, the aggregation is first adopted to utilize neighbors' information and then the bias mitigation step explicitly pushes demographic group node presentation centers together. In this way, FMP scheme can aggregate useful information from neighbors and mitigate bias to achieve better fairness and prediction tradeoff performance. Experiments on node classification tasks demonstrate that the proposed FMP outperforms several baselines in terms of fairness and accuracy on three real-world datasets. The code is available in {\url{https://github.com/zhimengj0326/FMP}}.

cs.LG↗
arXiv2023

Digital Divide in Disasters: Investigating Spatial and Socioeconomic Disparities in Internet Service Disruptions During Extreme Weather Events

The resilience of internet service is crucial for ensuring consistent communication, facilitating emergency response in digitally-dependent society. Due to empirical data constraints, there has been limited research on internet service disruptions during extreme weather events. To bridge this gap, this study utilizes observational datasets on internet performance to quantitatively assess extent of internet disruption during two recent extreme weather events. Taking Harris County in United States as study region, we jointly analyzed the hazard severity and the associated internet disruptions in two extreme weather events. The results show that hazard events significantly impacted regional internet connectivity. There exists a pronounced temporal synchronicity between magnitude of disruption and hazard severity: as severity of hazards intensifies, internet disruptions correspondingly escalate, and eventually return to baseline levels post-event. Spatial analyses show internet service disruptions can happen even in areas not directly impacted by hazards, demonstrating that repercussions of hazards extend beyond immediate area of impact. This interplay of temporal synchronization and spatial variance underscores complex relationships between hazard severity and Internet disruption. Socio-demographic analysis suggests vulnerable communities, already grappling with myriad challenges, face exacerbated service disruptions during hazard events, emphasizing the need for prioritized disaster mitigation strategiesfor improving the resilience of internet services. To the best of our knowledge, this research is among the first studies to examine the Internet disruptions during hazardous events using a quantitative observational dataset. Insights obtained hold significant implications for city administrators, guiding them towards more resilient and equitable infrastructure planning.

stat.AP↗
arXiv2023

Unraveling Fundamental Properties of Power System Resilience Curves using Unsupervised Machine Learning

The standard model of infrastructure resilience, the resilience triangle, has been the primary way of characterizing and quantifying infrastructure resilience. However, the theoretical model merely provides a one-size-fits-all framework for all infrastructure systems. Most of the existing studies examine the characteristics of infrastructure resilience curves based on analytical models constructed upon simulated system performance. Limited empirical studies hindered our ability to fully understand and predict resilience characteristics in infrastructure systems. To address this gap, this study examined over 200 resilience curves related to power outages in three major extreme weather events. Using unsupervised machine learning, we examined different curve archetypes, as well as the fundamental properties of each resilience curve archetype. The results show two primary archetypes for power system resilience curves, triangular, and trapezoidal curves. Triangular curves characterize resilience behavior based on 1. critical functionality threshold, 2. critical functionality recovery rate, and 3. recovery pivot point. Trapezoidal archetypes explain resilience curves based on 1. duration of sustained function loss and 2. constant recovery rate. The longer the duration of sustained function loss, the slower the constant rate of recovery. The findings of this study provide novel perspectives enabling better understanding and prediction of resilience performance of power system infrastructures.

cs.LG↗
arXiv2023

Weaving Equity into Infrastructure Resilience Research and Practice: A Decadal Review and Future Directions

After about a decade of research in this domain, what is missing is a systematic overview of the research agenda across different infrastructures and hazards. It is now imperative to evaluate the current progress and gaps. This paper presents a systematic review of equity literature on disrupted infrastructure during a natural hazard event. Following a systematic review protocol, we collected, screened, and evaluated almost 3,000 studies. Our analysis focuses on the intersection within the dimensions of the eight-dimensional assessment framework that distinguishes focus of the study, methodological approaches, and equity dimensions (distributional-demographic, distributional-spatial, procedural, and capacity equity). To conceptualize the intersection of the different dimensions of equity, we refer to pathways, which identify how equity is constructed, analyzed, and used. Significant findings show that (1) the interest in equity in infrastructure resilience has exponentially increased, (2) the majority of studies are in the US and by extension in the global north, (3) most data collection use descriptive and open-data and none of the international studies use location-intelligence data. The most prominent equity conceptualization is distributional equity, such as the disproportionate impacts to vulnerable populations and spaces. The most common pathways to study equity connect distributional equity to the infrastructure's power, water, and transportation in response to flooding and hurricane storms. Other equity concepts or pathways, such as connections of equity to decision-making and building household capacity, remain understudied. Future research directions include quantifying the social costs of infrastructure disruptions and better integration of equity into resilience decision-making.

physics.soc-ph↗
arXiv2023

ML4EJ: Decoding the Role of Urban Features in Shaping Environmental Injustice Using Interpretable Machine Learning

Understanding the key factors shaping environmental hazard exposures and their associated environmental injustice issues is vital for formulating equitable policy measures. Traditional perspectives on environmental injustice have primarily focused on the socioeconomic dimensions, often overlooking the influence of heterogeneous urban characteristics. This limited view may obstruct a comprehensive understanding of the complex nature of environmental justice and its relationship with urban design features. To address this gap, this study creates an interpretable machine learning model to examine the effects of various urban features and their non-linear interactions to the exposure disparities of three primary hazards: air pollution, urban heat, and flooding. The analysis trains and tests models with data from six metropolitan counties in the United States using Random Forest and XGBoost. The performance is used to measure the extent to which variations of urban features shape disparities in environmental hazard levels. In addition, the analysis of feature importance reveals features related to social-demographic characteristics as the most prominent urban features that shape hazard extent. Features related to infrastructure distribution and land cover are relatively important for urban heat and air pollution exposure respectively. Moreover, we evaluate the models' transferability across different regions and hazards. The results highlight limited transferability, underscoring the intricate differences among hazards and regions and the way in which urban features shape hazard exposures. The insights gleaned from this study offer fresh perspectives on the relationship among urban features and their interplay with environmental hazard exposure disparities, informing the development of more integrated urban design policies to enhance social equity and environmental injustice issues.

cs.LG↗
arXiv2023

Untangling The Relationship Between Power Outage and Population Activity Recovery in Disasters

Despite recognition of the relationship between infrastructure resilience and community recovery, very limited empirical evidence exists regarding the extent to which the disruptions in and restoration of infrastructure services contribute to the speed of community recovery. To address this gap, this study investigates the relationship between community and infrastructure systems in the context of hurricane impacts, focusing on the recovery dynamics of population activity and power infrastructure restoration. Empirical observational data were utilized to analyze the extent of impact, recovery duration, and recovery types of both systems in the aftermath of Hurricane Ida. The study reveals three key findings. First, power outage duration positively correlates with outage extent until a certain impact threshold is reached. Beyond this threshold, restoration time remains relatively stable regardless of outage magnitude. This finding underscores the need to strengthen power infrastructure, particularly in extreme weather conditions, to minimize outage restoration time. Second, power was fully restored in 70\% of affected areas before population activity levels normalized. This finding suggests the role infrastructure functionality plays in post-disaster community recovery. Interestingly, quicker power restoration did not equate to rapid population activity recovery due to other possible factors such as transportation, housing damage, and business interruptions. Finally, if power outages last beyond two weeks, community activity resumes before complete power restoration, indicating adaptability in prolonged outage scenarios. This implies the capacity of communities to adapt to ongoing power outages and continue daily life activities...

physics.data-an↗
arXiv2023

Dissecting Resilience Triangle: Unravelling Resilience Curve Archetypes and Properties in Human Systems Facing Weather Hazards

Resilience curves have been the primary approach for conceptualizing and representing the resilience behavior of communities during hazard events; however, the use of resilience curves has remained as a mere conceptual and visual tool with limited data-driven characterization and empirical grounding. Empirical characterizations of resilience curves provide essential insights regarding the manner in which differently impacted systems of communities absorb perturbations and recover from disruptions. To address this gap, this study examines human mobility resilience patterns following multiple weather-related hazard events in the United States by analyzing more than 2000 empirical resilience curves constructed from high-resolution location-based mobility data. These empirical resilience curves are then classified using k-means clustering based on various features into archetypes. Three main archetypes of human mobility resilience are identified: Type I, with rapid recovery after mild impact; Type II, exhibiting bimodal recovery after moderate impact; and Type III, showing slower recovery after severe impact. The results also reveal critical thresholds, such as the bimodal recovery breakpoint at a 20% impact extent, at which the recovery rate decreases, and the critical functional threshold at a 60% impact extent, above which recovery rate would be rather slow. The results show that a critical functional recovery rate of 2.5% per day is necessary to follow the bimodal resilience archetype when impact extent exceeds more than 20%. These findings provide novel and important insights into different resilience curve archetypes and their fundamental properties. Departing from using resilience curves as a mere concept and visual tool, the data-driven specification of resilience curve archetypes and their properties improve our understanding of the resilience patterns...

physics.soc-ph↗
arXiv2023

MaxFloodCast: Ensemble Machine Learning Model for Predicting Peak Inundation Depth And Decoding Influencing Features

Timely, accurate, and reliable information is essential for decision-makers, emergency managers, and infrastructure operators during flood events. This study demonstrates a proposed machine learning model, MaxFloodCast, trained on physics-based hydrodynamic simulations in Harris County, offers efficient and interpretable flood inundation depth predictions. Achieving an average R-squared of 0.949 and a Root Mean Square Error of 0.61 ft on unseen data, it proves reliable in forecasting peak flood inundation depths. Validated against Hurricane Harvey and Storm Imelda, MaxFloodCast shows the potential in supporting near-time floodplain management and emergency operations. The model's interpretability aids decision-makers in offering critical information to inform flood mitigation strategies, to prioritize areas with critical facilities and to examine how rainfall in other watersheds influences flood exposure in one area. The MaxFloodCast model enables accurate and interpretable inundation depth predictions while significantly reducing computational time, thereby supporting emergency response efforts and flood risk management more effectively.

cs.LG↗
arXiv2023

FairMobi-Net: A Fairness-aware Deep Learning Model for Urban Mobility Flow Generation

Generating realistic human flows across regions is essential for our understanding of urban structures and population activity patterns, enabling important applications in the fields of urban planning and management. However, a notable shortcoming of most existing mobility generation methodologies is neglect of prediction fairness, which can result in underestimation of mobility flows across regions with vulnerable population groups, potentially resulting in inequitable resource distribution and infrastructure development. To overcome this limitation, our study presents a novel, fairness-aware deep learning model, FairMobi-Net, for inter-region human flow prediction. The FairMobi-Net model uniquely incorporates fairness loss into the loss function and employs a hybrid approach, merging binary classification and numerical regression techniques for human flow prediction. We validate the FairMobi-Net model using comprehensive human mobility datasets from four U.S. cities, predicting human flow at the census-tract level. Our findings reveal that the FairMobi-Net model outperforms state-of-the-art models (such as the DeepGravity model) in producing more accurate and equitable human flow predictions across a variety of region pairs, regardless of regional income differences. The model maintains a high degree of accuracy consistently across diverse regions, addressing the previous fairness concern. Further analysis of feature importance elucidates the impact of physical distances and road network structures on human flows across regions. With fairness as its touchstone, the model and results provide researchers and practitioners across the fields of urban sciences, transportation engineering, and computing with an effective tool for accurate generation of human mobility flows across regions.

cs.LG↗
arXiv2023

Mobility Behaviors Shift Disparity in Flood Exposure in U.S. Population Groups

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. An important, though yet under-recognized aspect of flood exposure is associated with human mobility patterns and population visitation to places located in flood prone areas. This study analyzed large-scale, high-resolution location-intelligence data to characterize human mobility patterns and the resulting flood exposure in 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 reveal a significant disparity across race, income , and education level groups. Black and Asian, economically disadvantaged, and undereducated populations are disproportionally exposed to flood due to their daily mobility activities, indicating a pattern contrary to residential flood exposure. The results suggest that mobility behaviors play an important role in extending flood exposure reach disproportionally among socio-demographic groups. Mobility-based flood exposure provides a new perspective regarding the extent to which floods could disrupt people's life activities and enables a better characterization of disparity in populations' exposure to flood hazards beyond their place of residence.

stat.AP↗
arXiv2023

Decoding Urban-health Nexus: Interpretable Machine Learning Illuminates Cancer Prevalence based on Intertwined City Features

This study investigates the interplay among social demographics, built environment characteristics, and environmental hazard exposure features in determining community level cancer prevalence. Utilizing data from five Metropolitan Statistical Areas in the United States: Chicago, Dallas, Houston, Los Angeles, and New York, the study implemented an XGBoost machine learning model to predict the extent of cancer prevalence and evaluate the importance of different features. Our model demonstrates reliable performance, with results indicating that age, minority status, and population density are among the most influential factors in cancer prevalence. We further explore urban development and design strategies that could mitigate cancer prevalence, focusing on green space, developed areas, and total emissions. Through a series of experimental evaluations based on causal inference, the results show that increasing green space and reducing developed areas and total emissions could alleviate cancer prevalence. The study and findings contribute to a better understanding of the interplay among urban features and community health and also show the value of interpretable machine learning models for integrated urban design to promote public health. The findings also provide actionable insights for urban planning and design, emphasizing the need for a multifaceted approach to addressing urban health disparities through integrated urban design strategies.

cs.LG↗
arXiv2023

Hazard Exposure Heterophily: A Latent Characteristic in Socio-Spatial Networks Influencing Community Resilience

We present a latent characteristic in socio-spatial networks, hazard-exposure heterophily, to capture the extent to which populations with similar hazard exposure could assist each other through social ties. Heterophily is the tendency of unlike individuals to form social ties. Conversely, populations in spatial areas with significant hazard exposure similarity, homophily, would lack sufficient resourcefulness to aid each other to lessen the impact of hazards. In the context of the Houston metropolitan area, we use Meta's Social Connectedness data to construct a socio-spatial network in juxtaposition with flood exposure data from National Flood Hazard Layer to analyze flood hazard exposure of spatial areas. The results reveal the extent and spatial variation of hazard-exposure heterophily in the study area. Notably, the results show that lower-income areas have lower hazard-exposure heterophily possibly caused by income segregation and the tendency of affordable housing development to be located in flood zones. Less resourceful social ties due to high hazard-exposure homophily may inhibit low-income areas from better coping with hazard impacts and could contribute to their slower recovery. Overall, the results underscore the significance of characterizing hazard-exposure heterophily in socio-spatial networks to reveal community vulnerability and resilience to hazards.

cs.SI↗
arXiv2023

Network Diffusion Model Reveals Recovery Multipliers and Heterogeneous Spatial Effects in Post-Disaster Community Recovery

Community recovery from hazards and crises occurs through various diffusion processes within social and spatial networks of communities. Existing knowledge regarding the diffusion of recovery in community socio-spatial networks, however, is rather limited. To bridge this gap, in this study, we created a network diffusion model to characterize the unfolding of population activity recovery in spatial networks of communities. Using data related to population activity recovery durations calculated from location-based data in the context of 2017 Hurricane Harvey in the Houston area, we parameterized the threshold-based network diffusion model and evaluated the extent of homogeneity in spatial effects. Then we implemented the network diffusion model along with the genetic algorithm to simulate and identify recovery multipliers. The results show that the spatial effects of recovery are rather heterogeneous across spatial areas; some spatial areas demonstrate a greater spatial effect (spatial interdependence) in their recovery compared with others. Also, the results show that low-income areas demonstrate a greater spatial effect in their recovery. The greater spatial effects in recovery of low-income areas imply more reliance on resources and facilities of neighboring areas and also explain the existence of slow recovery hotspots in areas where socially vulnerable populations reside. Also, the results show that low-income and minority areas are community recovery multipliers; the faster the recovery of these recovery multipliers; the faster the recovery of the entire community. Hence, prioritizing these areas for recovery resource allocation could expedite the recovery of the overall community and promote recovery equality and equity.

cs.SI↗
arXiv2023

Equitable Optimization of Patient Re-allocation and Temporary Facility Placement to Maximize Critical Care System Resilience in Disasters

End-stage renal disease patients face a complicated sociomedical situation and rely on various forms of infrastructure for life-sustaining treatment. Disruption of these infrastructures during disasters poses a major threat to their lives. To improve patient access to dialysis treatment, there is a need to assess the potential threat to critical care facilities from hazardous events. In this study, we propose optimization models to solve critical care system resilience problems including patient and medical resource allocation. We use human mobility data in the context of Harris County (Texas) to assess patient access to critical care facilities, dialysis centers in this study, under the simulated hazard impacts, and we propose models for patient re-allocation and temporary medical facility placement to improve critical care system resilience in an equitable manner. The results show (1) the capability of the optimization model in efficient patient re-allocation to alleviate disrupted access to dialysis facilities; (2) the importance of large facilities in maintaining the functioning of the system. The critical care system, particularly the network of dialysis centers, is heavily reliant on a few larger facilities, making it susceptible to targeted disruption. (3) The consideration of equity in the optimization model formulation reduces access loss for vulnerable populations in the simulated scenarios. (4) The proposed temporary facilities placement could improve access for the vulnerable population, thereby improving the equity of access to critical care facilities in disaster. The proposed patient re-allocation model and temporary facilities placement can serve as a data-driven and analytic-based decision support tool for public health and emergency management plans to reduce the loss of access and disrupted access to critical care facilities and would reduce the dire social costs.

math.OC↗