Biden School of Public Policy & Administration

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Browsing Biden School of Public Policy & Administration by Author "Davidson, Rachel A."

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    Managing disaster risk associated with critical infrastructure systems: a system-level conceptual framework for research and policy guidance
    (Civil Engineering and Environmental Systems, 2022-04-25) Davidson, Rachel A.; Kendra, James; Ewing, Bradley; Nozick, Linda K.; Starbird, Kate; Cox, Zachary; Leon-Corwin, Maggie
    This paper presents a new conceptual framework of the disaster risk of critical infrastructure systems in terms of societal impacts. Much research on infrastructure reliability focuses on specific issues related to the technical system or human coping. Focusing on the end goal of infrastructure services – societal functioning – this framework offers a new way to understand how those more focused research areas connect and the current thinking in each. Following an overview of the framework, each component is discussed in turn, including the initial buildout of physical systems; event occurrence; service interruptions; service provider response; user adaptations to preserve or create needed services; and the ending deficit in societal function. Possible uses of the framework include catalysing and guiding a systematic research agenda that could ultimately lead to a computational framework and stimulating discussion on resilience within utility and emergency management organisations and the larger community.
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    Regional county-level housing inventory predictions and the effects on hurricane risk
    (Natural Hazards and Earth System Sciences, 2022-03-30) Williams, Caroline J.; Davidson, Rachel A.; Nozick, Linda K.; Trainor, Joseph E.; Millea, Meghan; Kruse, Jamie L.
    Regional hurricane risk is often assessed assuming a static housing inventory, yet a region's housing inventory changes continually. Failing to include changes in the built environment in hurricane risk modeling can substantially underestimate expected losses. This study uses publicly available data and a long short-term memory (LSTM) neural network model to forecast the annual number of housing units for each of 1000 individual counties in the southeastern United States over the next 20 years. When evaluated using testing data, the estimated number of housing units was almost always (97.3 % of the time), no more than 1 percentage point different than the observed number, predictive errors that are acceptable for most practical purposes. Comparisons suggest the LSTM outperforms the autoregressive integrated moving average (ARIMA) and simpler linear trend models. The housing unit projections can help facilitate a quantification of changes in future expected losses and other impacts caused by hurricanes. For example, this study finds that if a hurricane with characteristics similar to Hurricane Harvey were to impact southeastern Texas in 20 years, the residential property and flood losses would be nearly USD 4 billion (38 %) greater due to the expected increase of 1.3 million new housing units (41 %) in the region.