Community-scale big data reveals disparate impacts of the Texas winter storm of 2021 and its managed power outage

Self Cite

Natural hazards cause disruptions in access to critical facilities, such as grocery stores, impeding residents’ ability to prepare for and cope with hardships during the disaster and recovery; however, disrupted access to critical facilities is not equal for all residents of a community. In this study, we examine disparate access to grocery stores in the context of the 2017 Hurricane Harvey in Harris County, Texas. We utilized high-resolution location-based datasets in implementing spatial network analysis and dynamic clustering techniques to uncover the overall disparate access to grocery stores for socially vulnerable populations during different phases of the disaster. Three access indicators are examined using network-centric measures: number of unique stores visited, average trip time to stores, and average distance to stores. These access indicators help us capture three dimensions of access: redundancy, rapidity, and proximity. The findings show the insufficiency of focusing merely on the distributional factors, such as location in a food desert and number of facilities, to capture the disparities in access, especially during the preparation and impact/short-term recovery periods. Furthermore, the characterization of access by considering combinations of access indicators reveals that flooding disproportionally affects socially vulnerable populations. High-income areas have better access during the preparation period as they are able to visit a greater number of stores and commute farther distances to obtain supplies. The conclusions of this study have important implications for urban development (facility distribution), emergency management, and resource allocation by identifying areas most vulnerable to disproportionate access impacts using more equity-focused and data-driven approaches.

mentioning

confidence: 99%

Characterizing equitable access to grocery stores during disasters using location-based data

Esmalian

Coleman

Yuan

et al. 2022

Self Cite

“…This result is significant in two aspects: first, the results reveal one mechanism causing lower-income areas recover more slowly after flood events. A number of studies 33,34 have reported the slow recovery of low-income areas after flood events in Harris County. The result presents one mechanism that negatively affects such slow recovery due to hazardexposure homophily.…”

Section: Resultsmentioning

confidence: 99%

“…The theoretical significance of this finding can be viewed from two aspects: first, this finding reveals one possible mechanism that hinders low-income areas from coping and recovering from flood impacts. The literature 33,34,37 provides strong evidence about greater impacts and slower recovery of low-income areas during flood events and has attributed the greater impacts and slower recovery to socio-economic characteristics [38][39][40] . The finding in this study however, reveals that hazard-exposure homophily (or lack of heterophily) reduces the number of resourceful social links available to low-income areas, and thus could negatively affect their resilience and recovery.…”

Section: Groupmentioning

confidence: 99%

Revealing hazard-exposure heterophily as a latent characteristic of community resilience in social-spatial networks

Liu

Mostafavi

2023

Self Cite

We present a latent characteristic in socio-spatial networks, hazard-exposure heterophily, to capture the extent to which populations with dissimilar hazard exposure could assist each other through social ties. Heterophily is the tendency of unlike individuals to form social ties. Conversely, populations in hazard-prone 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 in hazard-prone areas due to their 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.

“…In addition to flood reports and social media activities, recent studies show that telemetry-based human activity fluctuations, which is registered by the concentration of aggregated usage of cellphone users in specific areas can signal flood inundation or other disaster-related impacts 82,83 . To incorporate information regarding human activity in our flood nowcasting model, we obtained digital traces of human activities for the study timeframe from Mapbox.…”

Section: Overview Of the Model Development And Evaluationmentioning

confidence: 99%

A spatial–temporal graph deep learning model for urban flood nowcasting leveraging heterogeneous community features

Farahmand

Mostafavi

2023

Self Cite

Flood nowcasting refers to near-future prediction of flood status as an extreme weather event unfolds to enhance situational awareness. The objective of this study was to adopt and test a novel structured deep-learning model for urban flood nowcasting by integrating physics-based and human-sensed features. We present a new computational modeling framework including an attention-based spatial–temporal graph convolution network (ASTGCN) model and different streams of data that are collected in real-time, preprocessed, and fed into the model to consider spatial and temporal information and dependencies that improve flood nowcasting. The novelty of the computational modeling framework is threefold: first, the model is capable of considering spatial and temporal dependencies in inundation propagation thanks to the spatial and temporal graph convolutional modules; second, it enables capturing the influence of heterogeneous temporal data streams that can signal flooding status, including physics-based features (e.g., rainfall intensity and water elevation) and human-sensed data (e.g., residents’ flood reports and fluctuations of human activity) on flood nowcasting. Third, its attention mechanism enables the model to direct its focus to the most influential features that vary dynamically and influence the flood nowcasting. We show the application of the modeling framework in the context of Harris County, Texas, as the study area and 2017 Hurricane Harvey as the flood event. Three categories of features are used for nowcasting the extent of flood inundation in different census tracts: (i) static features that capture spatial characteristics of various locations and influence their flood status similarity, (ii) physics-based dynamic features that capture changes in hydrodynamic variables, and (iii) heterogeneous human-sensed dynamic features that capture various aspects of residents’ activities that can provide information regarding flood status. Results indicate that the ASTGCN model provides superior performance for nowcasting of urban flood inundation at the census-tract level, with precision 0.808 and recall 0.891, which shows the model performs better compared with other state-of-the-art models. Moreover, ASTGCN model performance improves when heterogeneous dynamic features are added into the model that solely relies on physics-based features, which demonstrates the promise of using heterogenous human-sensed data for flood nowcasting. Given the results of the comparisons of the models, the proposed modeling framework has the potential to be more investigated when more data of historical events are available in order to develop a predictive tool to provide community responders with an enhanced prediction of the flood inundation during urban flood.