Credibly assessing the resilience of energy infrastructure in the face of natural disasters is a salient concern facing researchers, government officials, and community members. Here, we explore the influence of the spatial distribution of disruptions due to hurricanes and other natural hazards on the resilience of power distribution systems. We find that incorporating information about the spatial distribution of disaster impacts has significant implications for estimating infrastructure resilience. Specifically, the uncertainty associated with estimated infrastructure resilience metrics to spatially distributed disaster-induced disruptions is much higher than determined by previous methods. We present a case study of an electric power distribution grid impacted by a major landfalling hurricane. We show that improved characterizations of disaster disruption drastically change the way in which the grid recovers, including changes in emergent system properties such as antifragility. Our work demonstrates that previous methods for estimating critical infrastructure resilience may be overstating the confidence associated with estimated network recoveries due to the lack of consideration of the spatial structure of disruptions.
The estimation of resilience and the determination of inequality growth after a disaster is often difficult. In this study, specific indicators were developed to analyze resilience, and the trajectories of resilience were compared. Energy production is relevant for describing variations in social and economic activities. This indicator was applied to a case study of a natural disaster, that is Hurricane Irma in 2017. The hurricane caused fatalities and destruction in the Caribbean islands of Saint Martin and Saint Barthelemy, which are two French overseas territories. Energy production after H…
Decoding the Pulse of Community during Disasters: Resilience Analysis Based on Fluctuations in Latent Lifestyle Signatures within Human Visitation Networks
Disaster Information Reachability of Medium-Wave AM Broadcasting as a Wide-Area Disaster Information Infrastructure: Lessons from the 2024 Noto Peninsula Earthquake
Location data obtained from smartphones is increasingly finding use cases in disaster risk management. Where traditionally, CDR has provided the predominant digital footprint for human mobility, GPS data now has immense potential in terms of improved spatiotemporal accuracy, volume, availability, and accessibility. GPS data has already proven invaluable in a range of pre- and post-disaster use cases, such as quantifying displacement, measuring rates of return and recovery, evaluating accessibility to critical resources, planning for resilience. Despite its popularity and potential, however, th…
Resilience is meant as the capability of a networked infrastructure to provide its service even if some components fail: in this paper we focus on how resilience depends both on net-wide measures of connectivity and the role of a single component. This paper has two objectives: first to show how a set of global measures can be obtained using techniques from network theory, in particular how the spectral analysis of the adjacency and Laplacian matrices and a similarity measure based on Jensen-Shannon divergence allows us to obtain a characteriza-tion of global connectivity which is both mathema…
This study aims to quantify community resilience based on fluctuations in the visits to various Point-of-Interest (POIs) locations. Visit to POIs is an essential indicator of human activities and captures the combined effects of perturbations in people lifestyles, built environment conditions, and businesses status. The study utilized digital trace data of unique visits to POIs in the context of the 2017 Hurricane Harvey in Houston (Texas, USA) to examine spatial patterns of impact and total recovery effort and utilized these measures to quantify community resilience. The results showed that c…
We propose a tool-supported framework to reason about requirements constraining resource distributions and devise strategies for routing essential services in a disaster-affected region. At the core of our approach is the Route Advisor for Disaster-Affected Regions (RADAR) framework that operates on high-level algebraic representations of the region, modelled as a cyber-physical system (cps) where resource distribution is carried out over an infrastructure connecting physical geographical locations. The Satisfiable-Modulo Theories (SMT) and graph-theoretic algorithms used by the framework supp…
The objective of this study is to examine spatial patterns of impacts and recovery of communities based on variances in credit card transactions. Such variances could capture the collective effects of household impacts, disrupted accesses, and business closures, and thus provide an integrative measure for examining disaster impacts and community recovery in disasters. Existing studies depend mainly on survey and sociodemographic data for disaster impacts and recovery effort evaluations, although such data has limitations, including large data collection efforts and delayed timeliness results. …
