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Ali Mostafavi

Publications and source records attributed to Ali Mostafavi.

At least 91 records · Page 5Linked to original sources
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↗
arXiv2023

Beyond Residence: A Mobility-based Approach for Improved Evaluation of Human Exposure to Environmental Hazards

Evaluating human exposure to environmental hazards is crucial for identifying susceptible communities and devising targeted health policies. Standard environmental hazard exposure assessment methods have been primarily based on place of residence, an approach which neglect individuals hazard exposures due to the daily life activities and mobility outside home neighborhood. To address this limitation, this study proposes a novel mobility-based index for hazard exposure evaluation. Using large-scale and fine-grained human mobility data, we quantify the extent of population dwell time in high-environmental-hazard places in 239 U.S. counties for three major environmental hazards: air pollution, heat, and toxic sites. Subsequently we explore the extent to which human mobility extends the reach of environmental hazards and also lead to the emergence of latent exposure for populations living outside high hazard areas with relatively considerable dwell time in high hazard areas. The findings help quantify environmental hazard exposure more reliably, considering the role of human mobility and activities. The interplay of spatial clustering in high-hazard regions and human movement trends creates environmental hazard traps intensifying exposure. Poor and ethnic minority residents disproportionately face multiple types of environmental hazards, aggravating potential health impacts. This data-driven evidence supports the severity of these injustices. We also studied latent exposure arising from visits outside residents' home areas, revealing millions population having 5% to10% of daily activities occur in high-exposure zones. Despite living in perceived safe areas, human mobility could expose millions of residents to different hazards. These findings provide crucial insights for targeted policies to mitigate these severe environmental injustices

stat.AP↗
arXiv2023

Urban Form and Structure Explain Variability in Spatial Inequality of Property Flood Risk among US Counties

Understanding the relationship between urban form and structure and spatial variation of property flood risk has been a longstanding challenge in urban planning and city flood risk management. Yet limited data-driven insights exist regarding the extent to which variation in spatial inequality of property flood risk in cities can be explained by heterogenous features of urban form and structure. In this study, we explore eight key features (i.e., population density, point of interest density, road density, minority segregation, income segregation, urban centrality index, gross domestic product, and human mobility index) related to urban form and structure to explain variability in spatial inequality of property flood risk among 2567 US counties. Using rich datasets related to property flood risk, we quantify spatial inequality in property flood risk and delineate features of urban form and structure using high-resolution human mobility and facility distribution data. We identify significant variation in spatial inequality of property flood risk among US counties with coastline and metropolitan counties having the greatest spatial inequality of property flood risk. The results also reveal variations in spatial inequality of property flood risk can be effectively explained based on principal components of development density, economic activity, and centrality and segregation. Using a classification and regression tree model, we demonstrate how these principal components interact and form pathways that explain levels of spatial inequality in property flood risk in US counties. The findings offer important insights for the understanding of the complex interplay between urban form and structure and spatial inequality of property flood risk and have important implications for integrated urban design strategies to address property flood risk as cities continue to expand and develop.

physics.soc-ph↗
arXiv2023

ELEV-VISION: Automated Lowest Floor Elevation Estimation from Segmenting Street View Images

We propose an automated lowest floor elevation (LFE) estimation algorithm based on computer vision techniques to leverage the latent information in street view images. Flood depth-damage models use a combination of LFE and flood depth for determining flood risk and extent of damage to properties. We used image segmentation for detecting door bottoms and roadside edges from Google Street View images. The characteristic of equirectangular projection with constant spacing representation of horizontal and vertical angles allows extraction of the pitch angle from the camera to the door bottom. The depth from the camera to the door bottom was obtained from the depthmap paired with the Google Street View image. LFEs were calculated from the pitch angle and the depth. The testbed for application of the proposed method is Meyerland (Harris County, Texas). The results show that the proposed method achieved mean absolute error of 0.190 m (1.18 %) in estimating LFE. The height difference between the street and the lowest floor (HDSL) was estimated to provide information for flood damage estimation. The proposed automatic LFE estimation algorithm using Street View images and image segmentation provides a rapid and cost-effective method for LFE estimation compared with the surveys using total station theodolite and unmanned aerial systems. By obtaining more accurate and up-to-date LFE data using the proposed method, city planners, emergency planners and insurance companies could make a more precise estimation of flood damage.

cs.CV↗
arXiv2023

Collision of Environmental Injustice and Sea Level Rise: Assessment of Risk Inequality in Flood-induced Pollutant Dispersion from Toxic Sites in Texas

Global sea-level rise causes increasing threats of coastal flood and subsequent pollutant dispersion. However, there are still few studies on the disparity arising from such threats and the extent to which different communities could be exposed to flood-induced pollution dispersion from toxic sites under future sea level rise. To address this gap, this study selects Texas (a U.S. state with a large number of toxic sites and significant flood hazards) as the study area and investigates impacts of flood-induced pollutant dispersion on different communities under current (2018) and future (2050) flood hazard scenarios.The results show, currently, north coastline in Texas bears higher threats and vulnerable communities (i.e., low income, minorities and unemployed) are disproportionally exposed to these threats. In addition, the future sea-level rise and the exacerbated flood hazards will put additional threats on more (about 10%) Texas residents, among which vulnerable communities will still be disproportionately exposed to the increased threats. Our study reveals the facts that potential coastal pollutant dispersion will further aggravate the environmental injustice issues at the intersection of toxic sites and flood hazards for vulnerable populations and exacerbate risk inequalities. Given the dire impacts of flood-induced pollution dispersion on public health, the findings have important implications for specific actions from the policy makers to mitigate the inequitable risks.

cs.SI↗
arXiv2023

Mapping Inequalities in Activity-based Carbon Footprints of Urban Dwellers using Fine-grained Human Trajectory Data

Effective climate mitigation strategies in cities rely on understanding and mapping urban carbon footprints. One significant source of carbon is a product of lifestyle choices and travel behaviors of urban residents. Although previous research addressed consumption- and home-related footprints, activity-based footprints of urban dwellers have garnered less attention. This study relies on deidentified human trajectory data from 5 million devices to examine the activity-based carbon footprint in Harris County, Texas. Our analysis of the heterogeneity of footprints based on places visited and distance traveled reveals significant inequality: 10% of users account for 88% of visitation-based footprints and 71% of distance-traveled footprints. We also identify the influence of income on activity-based carbon footprint gap of users related to their travel behavior and lifestyle choices, with high-income users having larger footprints due to lifestyle choices, while low- to medium-income users' footprints are due to limited access. Our findings underscore the need for urban design adjustments to reduce carbon-intensive behaviors and to improve facility distribution. Our conclusions highlight the importance of addressing urban design parameters that shape carbon-intensive lifestyle choices and facility distribution, decisions which have implications for developing interventions to reduce carbon footprints caused by human activities.

physics.soc-ph↗
arXiv2023

AI-assisted Protective Action: Study of ChatGPT as an Information Source for a Population Facing Climate Hazards

ChatGPT has been emerging as a novel information source, and it is likely that the public might seek information from ChatGPT while taking protective actions when facing climate hazards such as floods and hurricanes. The objective of this study is to evaluate the accuracy and completeness of responses generated by ChatGPT when individuals seek information about aspects of taking protective actions. The survey analysis results indicated that: (1) the emergency managers considered the responses provided by ChatGPT as accurate and complete to a great extent; (2) it was statistically verified in evaluations that the generated information was accurate, but lacked completeness, implying that the extent of information provided is accurate; and (3) information generated for prompts related to hazard insurance received the highest evaluation, whereas the information generated related to evacuation received the lowest. This last result implies that, for complex, context-specific protective actions (such as evacuation), the information was rated as less complete compared with other protective actions. Also, the results showed that the perception of respondents regarding the utility of AI- assistive technologies (such as ChatGPT) for emergency preparedness and response improved after taking the survey and evaluating the information generated by ChatGPT. The findings from this study provide empirical evaluation regarding the utility of AI-assistive technologies for improving public decision-making and protective actions in disasters.

cs.CY↗
arXiv2023

Unraveling Extreme Weather Impacts on Air Transportation and Passenger Delays using Location-based Data

Extreme weather poses significant threats to air transportation systems, causing flight rerouting and cancellations, as well as passenger travel delays. With the growing frequency of extreme weather hazards, it is essential to understand the extent to which disruptions in flights and subsequent cancellations impact passenger delays. This study focuses on quantifying the impacts of a recent extreme weather event (2022 Winter Storm Elliott) on the U.S. air transportation system by investigating passenger delays measured based on dwell time at airports using privacy-preserving location-based datasets. The study determines total dwell time and dwell time per anonymized user at airports during the extreme weather event and computes the impact based on changes in values compared to the same period in the previous year. The results show that the storm event caused passengers significant delays, as characterized by a substantial increase in airport dwell time. Factor analysis shows that airports with a greater passenger flow and a greater portion of flights from decentralized airlines aggravated passengers delays during the winter storm. The vulnerability of airports was mainly due to the direct storm exposure, and the influence of network cascading impacts were limited. The findings of this study provide novel insights and quantification of the extent of extreme weather impacts on air transportation at individual airports and national levels. These outcomes could inform airport owners and operators, as well as airlines, about the extent of vulnerability and provide useful information for weather-related risk assessment of air transportation systems.

physics.soc-ph↗
arXiv2023

Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Images

This paper presents \dahitra, a novel deep-learning model with hierarchical transformers to classify building damages based on satellite images in the aftermath of natural disasters. Satellite imagery provides real-time and high-coverage information and offers opportunities to inform large-scale post-disaster building damage assessment, which is critical for rapid emergency response. In this work, a novel transformer-based network is proposed for assessing building damage. This network leverages hierarchical spatial features of multiple resolutions and captures the temporal differences in the feature domain after applying a transformer encoder on the spatial features. The proposed network achieves state-of-the-art performance when tested on a large-scale disaster damage dataset (xBD) for building localization and damage classification, as well as on LEVIR-CD dataset for change detection tasks. In addition, this work introduces a new high-resolution satellite imagery dataset, Ida-BD (related to 2021 Hurricane Ida in Louisiana in 2021) for domain adaptation. Further, it demonstrates an approach of using this dataset by adapting the model with limited fine-tuning and hence applying the model to newly damaged areas with scarce data.

cs.CV↗
arXiv2023

Emergence of Urban Heat Traps from the Intersection of Human Mobility and Heat Hazard Exposure in Cities

Understanding the relationship between spatial structures of cities and environmental hazard exposures (such as urban heat) is essential for urban health and sustainability planning. However, a critical knowledge gap exists in terms of the extent to which socio-spatial networks shaped by human mobility exacerbate or alleviate urban heat exposures of populations in cities. In this study, we utilize location-based data to construct human mobility networks in twenty metropolitan areas in the U.S. The human mobility networks are analyzed in conjunction with the urban heat characteristics of spatial areas. We identify areas with high and low urban heat exposure and evaluate visitation patterns of populations residing in high and low urban heat areas to other spatial areas with similar and dissimilar urban heat exposure. The results reveal the presence of urban heat traps in the majority of the studied metropolitan areas in which populations residing in high heat exposure areas primarily visit areas with high heat exposure. The results also show a small percentage of human mobility to produce urban heat escalate (visitations from low heat areas to high heat areas) and heat escapes (movements from high heat areas to low heat areas). The findings from this study provide a better understanding of urban heat exposure in cities based on patterns of human mobility. These finding contribute to a broader understanding of the intersection of human network dynamics and environmental hazard exposures in cities to inform more integrated urban design and planning to promote health and sustainability.

physics.soc-ph↗
arXiv2022

Data-driven Tracking of the Bounce-back Path after Disasters: Critical Milestones of Population Activity Recovery and Their Spatial Inequality

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.

cs.SI↗
arXiv2022

Characterizing Equitable Access to Grocery Stores During Disasters Using Location-based Data

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 preparation 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.

physics.soc-ph↗
arXiv2022

Graph Attention Networks Unveil Determinants of Intra- and Inter-city Health Disparity

Understanding the determinants underlying variations in urban health status is important for informing urban design and planning, as well as public health policies. Multiple heterogeneous urban features could modulate the prevalence of diseases across different neighborhoods in cities and across different cities. This study examines heterogeneous features related to socio-demographics, population activity, mobility, and the built environment and their non-linear interactions to examine intra- and inter-city disparity in prevalence of four disease types: obesity, diabetes, cancer, and heart disease. Features related to population activity, mobility, and facility density are obtained from large-scale anonymized mobility data. These features are used in training and testing graph attention network (GAT) models to capture non-linear feature interactions as well as spatial interdependence among neighborhoods. We tested the models in five U.S. cities across the four disease types. The results show that the GAT model can predict the health status of people in neighborhoods based on the top five determinant features. The findings unveil that population activity and built-environment features along with socio-demographic features differentiate the health status of neighborhoods to such a great extent that a GAT model could predict the health status using these features with high accuracy. The results also show that the model trained on one city can predict health status in another city with high accuracy, allowing us to quantify the inter-city similarity and discrepancy in health status. The model and findings provide novel approaches and insights for urban designers, planners, and public health officials to better understand and improve health disparities in cities by considering the significant determinant features and their interactions.

cs.LG↗
arXiv2022

Anatomy of Perturbed Traffic Networks during Urban Flooding

Urban flooding disrupts traffic networks, affecting the mobility and disrupting access of residents. Since flooding events are predicted to increase due to climate change, and given the criticality of traffic networks, understanding the flood-caused disruption of traffic networks is critical to improve emergency planning and city resilience. Leveraging high-resolution traffic network data from a major flood event and advanced high-order network analysis, this study reveals characteristics of the anatomy of perturbed traffic networks. First, the findings show network-wide persistent increased travel times could last for weeks after flood water has receded, even after modest flood failure. During disaster event period, modest flooding of 1.3% road segments caused 8% temporal expansion of the entire traffic network. The results also show distant trips would experience a greater percentage increase in travel time. Also, the extent of increase in travel time does not decay with distance from inundated areas, suggesting that the spatial reach of flood impacts extends beyond flooded areas. Departing from the existing literature, which primarily informs about physical vulnerability in road networks to floods, the findings of this study provide important novel understanding of floods impacts on the functioning of traffic networks in terms of travel time and traffic network geometry. The persistent travel time increase in the entire network can translate to significant social and economic impacts in terms of user costs, additional CO2 emissions, and lost productivity. Due to the importance of traffic networks functioning for the operation of cities, the results have significance implications for city managers, transportation planners, and emergency managers to deal with the impacts of urban flooding.

physics.soc-ph↗
arXiv2022

Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypes

The spread of COVID-19 revealed that transmission risk patterns are not homogenous across different cities and communities, and various heterogeneous features can influence the spread trajectories. Hence, for predictive pandemic monitoring, it is essential to explore latent heterogeneous features in cities and communities that distinguish their specific pandemic spread trajectories. To this end, this study creates a network embedding model capturing cross-county visitation networks, as well as heterogeneous features to uncover clusters of counties in the United States based on their pandemic spread transmission trajectories. We collected and computed location intelligence features from 2,787 counties from March 3 to June 29, 2020 (initial wave). Second, we constructed a human visitation network, which incorporated county features as node attributes, and visits between counties as network edges. Our attributed network embeddings approach integrates both typological characteristics of the cross-county visitation network, as well as heterogeneous features. We conducted clustering analysis on the attributed network embeddings to reveal four archetypes of spread risk trajectories corresponding to four clusters of counties. Subsequently, we identified four features as important features underlying the distinctive transmission risk patterns among the archetypes. The attributed network embedding approach and the findings identify and explain the non-homogenous pandemic risk trajectories across counties for predictive pandemic monitoring. The study also contributes to data-driven and deep learning-based approaches for pandemic analytics to complement the standard epidemiological models for policy analysis in pandemics.

cs.LG↗
arXiv2022

Roadmap Towards Responsible AI in Crisis Resilience Management

Novel data sensing and AI technologies are finding practical use in the analysis of crisis resilience, revealing the need to consider how responsible artificial intelligence (AI) practices can mitigate harmful outcomes and protect vulnerable populations. In this paper, we present a responsible AI roadmap that is embedded in the Crisis Information Management Circle. This roadmap includes six propositions to highlight and address important challenges and considerations specifically related to responsible AI for crisis resilience management. We cover a wide spectrum of interwoven challenges and considerations pertaining to the responsible collection, analysis, sharing, and use of information such as equity, fairness, biases, explainability and transparency, accountability, privacy and security, inter-organizational coordination, and public engagement. Through examining issues around AI systems for crisis resilience management, we dissect the inherent complexities of information management and decision-making in crises and highlight the urgency of responsible AI research and practice. The ideas laid out in this paper are the first attempt in establishing a roadmap for researchers, practitioners, developers, emergency managers, humanitarian organizations, and public officials to address important considerations for responsible AI pertaining to crisis resilience management.

cs.SI↗
arXiv2022

Unveiling Vulnerability and Inequality in Disrupted Access to Dialysis Centers During Urban Flooding

Despite the criticality of dialysis facilities, limited knowledge exists regarding the extent and inequality of disrupted access caused by weather events. This study uses mobility data in the context of the 2017 Hurricane Harvey in Harris County to examine the impact of flooding on access to dialysis centers. We examined access dimensions using multiple static and dynamic metrics. By analyzing fluctuations in dynamic metrics during the flood event, we derived spatiotemporal patterns of access disruptions and inequity in mobility from CBGs to dialysis centers. The results show that: (1) the extent of dependence of CBGs on dialysis centers varies; (2) flooding significantly reduces access redundancy and frequency of dialysis center visits; access disruptions persist for more than one month after the flood event; (3) CBGs with a greater minority percentage and lower household income were more likely to experience more extensive access disruptions; (4) high-income CBGs more quickly revert to their pre-disaster levels; (5) larger dialysis centers located in non-flooded areas are critical to absorbing the unmet demand from disrupted facilities; these larger facilities contribute to the absorptive capacity of the network of dialysis facilities in the region.

physics.soc-ph↗
arXiv2022

Human Mobility Networks Manifest Dissimilar Resilience Characteristics at Macroscopic, Substructure, and Microscopic Scales

Human mobility networks can reveal insights into resilience phenomena, such as population response to, impacts on, and recovery from crises. The majority of human mobility network resilience characterizations, however, focus mainly on macroscopic network properties; little is known about variation in measured resilience characteristics (i.e., the extent of impact and recovery duration) across macroscopic, substructure (motif), and microscopic mobility scales. To address this gap, in this study, we examine the human mobility network in eight parishes in Louisiana (USA) impacted by the 2021 Hurricane Ida. We constructed human mobility networks using location-based data and examined three sets of measures: (1) macroscopic measures, such as network density, giant component size, and modularity; (2) substructure measures, such motif distribution; and (3) microscopic mobility measures, such as the radius of gyration and average travel distance. To determine the extent of impact and duration of recovery, for each measure, we established the baseline values and examined the fluctuation of measures during the perturbation caused by Hurricane Ida. The results reveal the variation of impact extent and recovery duration obtained from different sets of measures at different scales. Macroscopic measures, such as giant components, tend to recover more quickly than substructure and microscopic measures. In fact, microscopic measures tend to recover more slowly than measures in other scales. These findings suggest that resilience characteristics in human mobility networks are scale-variant, and thus, a single measure at a particular scale may not be representative of the perturbation impacts and recovery duration in the network as a whole. These results spotlight the need to use measures at different scales to properly characterize resilience in human mobility networks.

physics.soc-ph↗