Restimate:Recovery Estimation Tool for Resilience Planning

The U.S.National Institute of Standards and Technology(NIST)published the Community Resilience Planning Guide in 2016.The NIST Guide advocates for a participatory process for developing a performance measurement framework for the jurisdiction’s resilience against a scenario hazard.The framework centers around tables of expected and desired recovery times for selected community assets,such as electricity,water,and natural gas infrastructures.The NIST Guide does not provide a method for estimating the expected recovery times.However,building high-fidelity computer models for such estimations requires substantial resources that even larger ju-risdictions cannot cost-justify.The most promising approach to recovery time estimation is to systematically use data elicited from people to tap into the wisdom of the(knowledgeable)crowd.This paper describes a novel research-through-design project to enable the computer-supported elicitation of recovery time series data.This work is the first in the literature to examine people’s ability to estimate recovery curves and how design in-fluences such estimations.Its main contribution to resilience planning is three-fold:development of a new elicitation tool called Restimate,understanding its potential user base,and providing insights into how it can facilitate resilience planning.Restimate is the first tool to enable evidence-based expert elicitation in any community with limited resources for resilience planning.Beyond resilience planning,those who facilitate high-stakes planning activities under large uncertainties(e.g.,mission-critical system design and planning)will benefit from a similar research-through-design process.

Data-driven sparse polynomial chaos expansion for models with dependent inputs

Polynomial chaos expansions(PCEs)have been used in many real-world engineering applications to quantify how the uncertainty of an output is propagated from inputs by decomposing the output in terms of polynomials of the inputs.PCEs for models with independent inputs have been extensively explored in the literature.Recently,different approaches have been proposed for models with dependent inputs to expand the use of PCEs to more real-world applications.Typical approaches include building PCEs based on the Gram–Schmidt algorithm or transforming the dependent inputs into independent inputs.However,the two approaches have their limitations regarding computational efficiency and additional assumptions about the input distributions,respectively.In this paper,we propose a data-driven approach to build sparse PCEs for models with dependent inputs without any distributional assumptions.The proposed algorithm recursively constructs orthonormal polynomials using a set of monomials based on their correlations with the output.The proposed algorithm on building sparse PCEs not only reduces the number of minimally required observations but also improves the numerical stability and computational efficiency.Four numerical examples are implemented to validate the proposed algorithm.The source code is made publicly available for reproducibility.

COVID-19 economic policy effects on consumer spending and foot traffic in the U.S.

To battle with economic challenges during the COVID-19 pandemic,the US government implemented various measures to mitigate economic loss.From issuance of stimulus checks to reopening businesses,consumers had to constantly alter their behavior in response to government policies.Using anonymized card transactions and mobile device-based location tracking data,we analyze the factors that contribute to these behavior changes,focusing on stimulus check issuance and state-wide reopening.Our finding suggests that stimulus payment has a significant immediate effect of boosting spending,but it typically does not reverse a downward trend.State-wide reopening had a small effect on spending.Foot traffic increased gradually after stimulus check issuance,but only increased slightly after reopening,which also coincided or preceded several policy changes and confounding events(e.g.,protests)in the US.We also find differences in the reaction to these policies in different regions in the US.Our results may be used to inform future economic recovery policies and their potential consumer response.

U.S. Resilience to large-scale power outages in 2002–2019

Prolonged power outages debilitate the economy and threaten public health. Existing research is generally limitedin its scope to a single event, an outage cause, or a region. Here, we provide one of the most comprehensiveanalyses of large-scale power outages in the U.S. from 2002 to 2019. This analysis is based on the outage datacollected under U.S. federal mandates that concern large blackouts, typically of transmission systems and excludemuch more common but smaller blackouts, typically, of distribution systems. We categorized the data into fouroutage causes and computed reliability metrics, which are commonly used for distribution-level small outagesonly but useful for analyzing large blackouts. Our spatiotemporal analysis reveals six of the most resilient U.S.states since 2010, improvement of power resilience against natural hazards in the south and northeast regions,and a disproportionately large number of human attacks for its population in the Western Electricity CoordinatingCouncil region. Our regression analysis identifies several statistically significant predictors and hypotheses forU.S. resilience to large blackouts. Furthermore, we propose a novel framework for analyzing outage data usingdifferential weighting and influential points to better understand power resilience. We share curated data andcode as Supplementary Materials.