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1. Quantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data

   https://doi.org/10.1098/rsif.2021.0158  

   Podesta, Cristian; Coleman, Natalie; Esmalian, Amir; Yuan, Faxi; Mostafavi, Ali (April 2021, Journal of The Royal Society Interface)

   This research establishes a methodological framework for quantifying community resilience based on fluctuations in a population's activity during a natural disaster. Visits to points-of-interests (POIs) over time serve as a proxy for activities to capture the combined effects of perturbations in lifestyles, the built environment and the status of business. This study used digital trace data related to unique visits to POIs in the Houston metropolitan area during Hurricane Harvey in 2017. Resilience metrics in the form of systemic impact, duration of impact, and general resilience (GR) values were examined for the region along with their spatial distributions. The results show that certain categories, such as religious organizations and building material and supplies dealers had better resilience metrics—low systemic impact, short duration of impact, and high GR. Other categories such as medical facilities and entertainment had worse resilience metrics—high systemic impact, long duration of impact and low GR. Spatial analyses revealed that areas in the community with lower levels of resilience metrics also experienced extensive flooding. This insight demonstrates the validity of the approach proposed in this study for quantifying and analysing data for community resilience patterns using digital trace/location-intelligence data related to population activities. While this study focused on the Houston metropolitan area and only analysed one natural hazard, the same approach could be applied to other communities and disaster contexts. Such resilience metrics bring valuable insight into prioritizing resource allocation in the recovery process. 

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2. Examining spatial and socioeconomic disparities in internet resilience during extreme weather events: a case study of Hurricane Harvey and Winter Storm Uri

   https://doi.org/10.1007/s44212-024-00051-x  

   Gupta, Yuvraj; Liu, Zhewei; Mostafavi, Ali (December 2024, Urban Informatics)

   Abstract The resilience of internet service is crucial for ensuring consistent communication, situational awareness, facilitating emergency response in our digitally-dependent society. However, 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 the extent of internet disruption during two recent extreme weather events. Taking Harris County in the United States as the study region, we jointly analyzed the hazard severity and the associated internet disruptions in the context of two extreme weather events. The results show that the hazard events significantly impacted regional internet connectivity. There exists a pronounced temporal synchronicity between the magnitude of disruption and hazard severity: as the severity of hazards intensifies, internet disruptions correspondingly escalate, and eventually return to baseline levels post-event. The spatial analyses show that internet service disruptions can happen even in areas that are not directly impacted by hazards, demonstrating that the repercussions of hazards extend beyond the immediate area of impact. This interplay of temporal synchronization and spatial variance underscores the complex relationships between hazard severity and Internet disruption. Furthermore, the socio-demographic analysis suggests that vulnerable communities, already grappling with myriad challenges, face exacerbated service disruptions during these hazard events, emphasizing the need for prioritized disaster mitigation strategies and interventions for 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. The insights obtained hold significant implications for city administrators, guiding them towards more resilient and equitable infrastructure planning. 

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3. Modeling Regional and Local Resilience of Infrastructure Networks Following Disruptions from Natural Hazards

   https://doi.org/10.1061/(asce)is.1943-555x.0000694  

   Sanderson, Dylan; Cox, Daniel; Barbosa, Andre R; Bolte, John (September 2022, Journal of Infrastructure Systems)

   This paper presents a framework to evaluate the regional and local resilience of infrastructure networks following disruptions from natural hazards. Herein, the regional resilience of a network relates to the accessibility of a community within a larger network, whereas the local resilience concerns the ability of a network to provide its intended service within the boundaries of a community. Using this framework, a methodology is developed to demonstrate its application to a road and highway transportation network disrupted by ground shaking and inundation under a Cascadia Subduction Zone earthquake and tsunami scenario. The regional network extents encompass the entire coast of the US state of Oregon. Embedded within this regional network are 18 local networks associated with coastal communities. Regional and local connectivity indexes are defined to identify the initial damage and then track the postdisaster recovery of the transportation network, i.e., evaluate the network resilience. The study results identify the attributes that lead to a regionally or locally resilient network and highlight the importance of considering local infrastructure networks embedded within larger regional networks. It is shown that without regional considerations, the time to recover may be severely underpredicted. The methodology is further used as a decision support tool to demonstrate how mitigation options impact the transportation network’s resilience. The importance of strategically considering mitigation options is emphasized as some communities see significant reductions in time to recover, whereas others see little to no improvement. 

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4. Building community resilience through cross‐sector partnerships and interdisciplinary research

   https://doi.org/10.1111/puar.13697  

   Ge, Yue “Gurt”; Kapucu, Naim; Zobel, Christopher W.; Hasan, Samiul; Hall, Jeremy L.; Wang, Haizhong; Wang, Liqiang; Martín, Yago; Cechowski, Michelle (September 2023, Public Administration Review)

   Abstract Building community resilience has become a national imperative. Substantial uncertainties in dynamic environments of emergencies and crises require real‐time information collection and dissemination based on big data analytics. These, in turn, require networked communities and cross‐sector partnerships to build lasting resilience. This viewpoint article highlights an interdisciplinary approach to building community resilience through community‐engaged research and partnerships. This perspective leverages existing community partnerships and network resources, undertakes an all‐hazard and whole‐community approach, and evaluates the use of state‐of‐the‐art information communication technologies. In doing so, it reinforces the multifaceted intergovernmental and cross‐sector networks through which resilience can be developed and sustained. 

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5. Spatial Assessment of Community Resilience from 2012 Hurricane Sandy Using Nighttime Light

   https://doi.org/10.3390/rs13204128  

   Xu, Jinwen; Qiang, Yi (October 2021, Remote Sensing)

   Quantitative assessment of community resilience is a challenge due to the lack of empirical data about human dynamics in disasters. To fill the data gap, this study explores the utility of nighttime lights (NTL) remote sensing images in assessing community recovery and resilience in natural disasters. Specifically, this study utilized the newly-released NASA moonlight-adjusted SNPP-VIIRS daily images to analyze spatiotemporal changes of NTL radiance in Hurricane Sandy (2012). Based on the conceptual framework of recovery trajectory, NTL disturbance and recovery during the hurricane were calculated at different spatial units and analyzed using spatial analysis tools. Regression analysis was applied to explore relations between the observed NTL changes and explanatory variables, such as wind speed, housing damage, land cover, and Twitter keywords. The result indicates potential factors of NTL changes and urban-rural disparities of disaster impacts and recovery. This study shows that NTL remote sensing images are a low-cost instrument to collect near-real-time, large-scale, and high-resolution human dynamics data in disasters, which provide a novel insight into community recovery and resilience. The uncovered spatial disparities of community recovery help improve disaster awareness and preparation of local communities and promote resilience against future disasters. The systematical documentation of the analysis workflow provides a reference for future research in the application of SNPP-VIIRS daily images. 

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