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

Jiang, Yuqin; Yuan, Faxi; Farahmand, Hamed; Acharya, Kushal; Zhang, Jingdi; Mostafavi, Ali

doi:10.2139/ssrn.4292735

Cited by 3 publications

(7 citation statements)

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“…The third component of the coupled human-infrastructure system performance was population activity recovery. The speed at which affected populations resume their normal life activities has been shown in prior studies to provide an important indicator for community recovery (Jiang et al, 2022). Population activities are considered to recover when people settle into a pre-disaster lifestyle after coping with impacts, disrupted infrastructure is restored, and businesses resume operations.…”

Section: Coupled Human-infrastructure Systems Performance Featuresmentioning

confidence: 99%

Machine Learning-based Approach for Ex-post Assessment of Community Risk and Resilience Based on Coupled Human- infrastructure Systems Performance

Li,

Mostafavi

2024

Preprint

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While current approaches primarily focus on anticipatory or predictive resilience assessments of natural events, there is a limitation in the literature of data-driven analyses for the ex-post evaluation of community risk and resilience, particularly using features related to the performance of coupled human-infrastructure systems. To address this gap, in this study we created a machine learning-based method for the ex-post assessment of community risk and resilience and their interplay based on features related to the coupled human-infrastructure systems performance. Utilizing feature groups related to population protective actions, infrastructure/building performance features, and recovery features, we examined the risk and resilience performance of communities in the context of the 2017 Hurricane Harvey in Harris County, Texas. These features related to the coupled human-infrastructure systems performance were processed using the K-means clustering method to classify census block groups into four distinct clusters then, based on feature analysis, these clusters were labeled and designated into four quadrants of risk-resilience archetypes. Finally, we analyzed the disparities in risk-resilience status of spatial areas across different clusters as well as different income groups. The findings unveil the risk-resilience status of spatial areas shaped by their coupled human-infrastructure systems performance and their interactions. The results also inform about features that contribute to high resilience in high-risk areas. For example, the results indicate that in high-risk areas, evacuation rates contributed to a greater resilience, while in low-risk areas, preparedness contributed to greater resilience. In addition, the findings reveal disparities in the risk and resilience status of spatial areas where low-income residents reside. The outcomes of this study provide researchers and practitioners with new data-driven and machine intelligence-based methods and insights to better evaluate the risk and resilience status of communities during a disaster to inform future plans and policies.

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Section: Coupled Human-infrastructure Systems Performance Featuresmentioning

confidence: 99%

Machine Learning-based Approach for Ex-post Assessment of Community Risk and Resilience Based on Coupled Human- infrastructure Systems Performance

Li,

Mostafavi

2024

Preprint

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“…Population activity recovery measure. The essential activity recovery measure of the population was computed based on a three-step approach as proposed by Jiang et al (2022), study results represented in SM Figure S1. A number of recent studies have used a similar methodology to assess either visitations to POIs (Yuan et al, 2022a;Li et al, 2021) or CBG-to-POI (Li and Mostafavi, 2022;Coleman et al, 2022a) in a disaster setting.…”

Section: Data and Measuresmentioning

confidence: 99%

“…Fluctuations in urban population activities capture the combined effect of disaster impacts on households, infrastructure (e.g., road inundations), and businesses (e.g., business closures) (Podesta et al, 2021). To effectively measure population activity recovery we can examine the fluctuations in time needed for population activities to return to steady state by utilizing location-intelligent data (Jiang et al, 2022). For this study, we focused on patterns of visitations to essential facilities to measure activity recovery durations at the census block group (CBG) level.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, existing knowledge and models of community recovery are rather limited in enabling predictive recovery monitoring due to: (1) absence of data-driven methods to quantify recovery at fine spatial scales; (2) limited understanding of the underlying factors (such as physical vulnerability and access, hazard exposure, and population attributes) influencing the variations in duration of recovery across different spatial areas; and (3) lack of consideration of spillover effects in spatial diffusion of recovery. Recognizing these gaps, recent studies (Jiang et al, 2022; Lee et al (2022){Yuan, 2022 #208}) have proposed critical milestones of population activity recovery based on examination of population visitation patterns, as well as credit card transactions. While these studies provide quantitative measures for tracking the trajectory of recovery, they do not provide a model for predictive evaluation of recovery duration based on CBG features and their spillover effects.…”

Section: Introductionmentioning

confidence: 99%

“…Location-based data provide insights into the spatial distribution of disaster impacts by gathering information people’s movement. Using location-based data during different disaster stages has been used to reveal disrupted access, hurricane preparedness, and disaster impacts, as well as the specification of critical recovery milestones (Jiang et al, 2022; Esmalian et al, 2022; Li and Mostafavi, 2022; Lee et al, 2022; Yuan et al, 2022b; Coleman et al, 2022b; Podesta et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Spatial model for predictive recovery monitoring based on hazard, built environment, and population features and their spillover effects

Patrascu

Mostafavi

2023

Environment and Planning B: Urban Analytics and City Science

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The ability to proactively monitor the trajectory of post-disaster recovery is valuable for resource allocation prioritization. Existing knowledge, however, lacks models and insights for quantifying and proactively monitoring post-disaster community recovery. This study examines models that could predict population activity recovery at the scale of the census block group (CBG). Population activity recovery is measured by using location-based human mobility visitation patterns to essential points-of-interest (POIs) in the context of the 2017 Hurricane Harvey in Harris County, Texas. The study examined the association between the population activity recovery duration and 32 features split into four categories: (1) physical vulnerability and access, (2) hazard exposure and impact, (3) proactive actions and (4) population features. Several types of spatial regression models were evaluated to determine their ability to capture this relationship. The Spatial Durbin Model was identified as the best fit for assessing direct, spillover, and total effects of features on population activity recovery at the CBG level. The results show the extent of physical vulnerability, measured by road network density, prolongs the duration of population activity recovery by a combination of direct and spillover effects. Also, the extent of access to essential facilities, measured based on the number of POIs, shortens the duration of population activity recovery. Correspondingly, the extent of flooding is not a significant feature in explaining the population recovery duration in CBGs. The results show that better preparedness, measured by extent of POIs visitations prior to hurricane landing, is associated with faster population activity recovery. In terms of population attributes, the total number of people, the percentage of minorities, and the percentage of Black and Asian subpopulations are significant features in the model for predicting the duration of population activity recovery. The study outcome offers data-driven insights for understanding the determinants of population activity recovery and provides a new model tool for predictive recovery monitoring based on evaluating the direct, spillover, and total effects of features. These findings can identify areas with slower or more rapid recovery to inform emergency managers and public officials in ensuring equitable resource allocation prioritization.

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Spillover Effects of Built-Environment Vulnerability on Resilience of Businesses in Urban Crises

Hsu,

Liu,

Stearns

et al. 2024

Preprint

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Exploring the intricate dynamics between the vulnerability of the built environment and the resilience of businesses is not only pivotal for equitable recovery resource allocation, especially among underprivileged populations but also remains a relatively unexplored area within the realm of urban resilience studies. This study uses fine-resolution human mobility and property-level flood damage claim data to uncover the rather unknown effects of built-environment vulnerability on the recovery of businesses and local economies. Using data from the 2017 Hurricane Harvey in Houston, Texas, we examine the extent of nearby residential flood damage on the differential recovery trajectories of essential businesses: grocery stores, gasoline stations, and healthcare retail stores, and physicians' offices. The analysis evaluates what types of businesses are more sensitive to residential flood damage and to what extent this sensitivity varies based on socio-demographic spectrum. The findings reveal the heterogeneity of sensitivity of businesses to residential property damage across business types. Certain businesses, such as grocery stores and offices of physicians, are more sensitive to nearby residential property damage. Also, the findings reveal that businesses located in low-income areas show a heightened sensitivity in the form of slowed recovering trajectories. The greater coupling of residential damage and business recovery in low-income areas creates a feedback mechanism that slows the overall recovery of those communities. The findings contribute to a better understanding, with implications for considering the ripple effect of built-environment vulnerability on economic resilience as a factor for evaluating urban crisis management strategies.

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

Cited by 3 publications

References 0 publications

Machine Learning-based Approach for Ex-post Assessment of Community Risk and Resilience Based on Coupled Human- infrastructure Systems Performance

Machine Learning-based Approach for Ex-post Assessment of Community Risk and Resilience Based on Coupled Human- infrastructure Systems Performance

Spatial model for predictive recovery monitoring based on hazard, built environment, and population features and their spillover effects

Spillover Effects of Built-Environment Vulnerability on Resilience of Businesses in Urban Crises

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