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Statistical and Spatial Analysis of Hurricane-induced Roadway Closures and Power Outages

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  • Mahyar Ghorbanzadeh

    (Department of Civil and Environmental Engineering, Florida Agricultural and Mechanical University-Florida State University College of Engineering, Florida State University, Tallahassee, FL 32310, USA)

  • Mohammadreza Koloushani

    (Department of Civil and Environmental Engineering, Florida Agricultural and Mechanical University-Florida State University College of Engineering, Florida State University, Tallahassee, FL 32310, USA)

  • Mehmet Baran Ulak

    (Department of Civil Engineering, College of Engineering and Applied Sciences, Stony Brook University, Stony Brook, NY 11794, USA)

  • Eren Erman Ozguven

    (Department of Civil and Environmental Engineering, Florida Agricultural and Mechanical University-Florida State University College of Engineering, Florida State University, Tallahassee, FL 32310, USA)

  • Reza Arghandeh Jouneghani

    (Department of Computing, Mathematics, and Physics, Faculty of Engineering and Science, Western Norway University of Applied Sciences, 5020 Bergen, Norway)

Abstract

Hurricanes lead to substantial infrastructure system damages, such as roadway closures and power outages, in the US annually, especially in states like Florida. As such, this paper aimed to assess the impacts of Hurricane Hermine (2016) and Hurricane Michael (2018) on the City of Tallahassee, the capital of Florida, via exploratory spatial and statistical analyses on power outages and roadway closures. First, a geographical information systems (GIS)-based spatial analysis was conducted to explore the power outages and roadway closure patterns in the city including kernel density estimation (KDE) and density ratio difference (DRD) methods. In order to provide a more detailed assessment on which population segments were more affected, a second step included a statistical analysis to identify the relationships between demographic- and socioeconomic-related variables and the magnitude of power outages and roadway closures caused by these hurricanes. The results indicate that the high-risk locations for roadway closures showed different patterns, whereas power outages seemed to have similar spatial patterns for the hurricanes. The findings of this study can provide useful insights and information for city officials to identify the most vulnerable regions which are under the risk of disruption. This can lead to better infrastructure plans and policies.

Suggested Citation

  • Mahyar Ghorbanzadeh & Mohammadreza Koloushani & Mehmet Baran Ulak & Eren Erman Ozguven & Reza Arghandeh Jouneghani, 2020. "Statistical and Spatial Analysis of Hurricane-induced Roadway Closures and Power Outages," Energies, MDPI, vol. 13(5), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1098-:d:327104
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    References listed on IDEAS

    as
    1. Ayberk Kocatepe & Mehmet Baran Ulak & Grzegorz Kakareko & Eren Erman Ozguven & Sungmoon Jung & Reza Arghandeh, 2019. "Measuring the accessibility of critical facilities in the presence of hurricane-related roadway closures and an approach for predicting future roadway disruptions," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 95(3), pages 615-635, February.
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    Cited by:

    1. Alam, Md. Shaharier & Kim, Kyusik & Horner, Mark W. & Alisan, Onur & Antwi, Richard & Ozguven, Eren Erman, 2024. "Large-scale modeling of hurricane flooding and disrupted infrastructure impacts on accessibility to critical facilities," Journal of Transport Geography, Elsevier, vol. 116(C).
    2. Mirosław Kornatka & Anna Gawlak, 2021. "An Analysis of the Operation of Distribution Networks Using Kernel Density Estimators," Energies, MDPI, vol. 14(21), pages 1-12, October.
    3. Jinghui (Jove) Hou & Laura Arpan & Yijie Wu & Richard Feiock & Eren Ozguven & Reza Arghandeh, 2020. "The Road toward Smart Cities: A Study of Citizens’ Acceptance of Mobile Applications for City Services," Energies, MDPI, vol. 13(10), pages 1-15, May.
    4. Yürüşen, Nurseda Y. & Uzunoğlu, Bahri & Talayero, Ana P. & Estopiñán, Andrés Llombart, 2021. "Apriori and K-Means algorithms of machine learning for spatio-temporal solar generation balancing," Renewable Energy, Elsevier, vol. 175(C), pages 702-717.

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