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Assessing Tornado Impacts in the State of Kentucky with a Focus on Demographics and Roadways Using a GIS-Based Approach

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  • Mehmet Burak Kaya

    (Department of Civil and Environmental Engineering, FAMU–FSU College of Engineering, Tallahassee, FL 32310, USA)

  • Onur Alisan

    (Department of Civil and Environmental Engineering, FAMU–FSU College of Engineering, Tallahassee, FL 32310, USA)

  • Alican Karaer

    (Iteris, Inc., Tallahassee, FL 32304, USA)

  • Eren Erman Ozguven

    (Department of Civil and Environmental Engineering, FAMU–FSU College of Engineering, Tallahassee, FL 32310, USA)

Abstract

Although the literature provides valuable insight into tornado vulnerability and resilience, there are still research gaps in assessing tornadoes’ impact on communities and transportation infrastructure, especially in the wake of the rapidly changing frequency and strength of tornadoes due to climate change. In this study, we first investigated the relationship between tornado exposure and demographic-, socioeconomic-, and transportation-related factors in our study area, the state of Kentucky. Tornado exposures for each U.S. census block group (CBG) were calculated by utilizing spatial analysis methods such as kernel density estimation and zonal statistics. Tornadoes between 1950 and 2022 were utilized to calculate tornado density values as a surrogate variable for tornado exposure. Since tornado density varies over space, a multiscale geographically weighted regression model was employed to consider spatial heterogeneity over the study region rather than using global regression such as ordinary least squares (OLS). The findings indicated that tornado density varied over the study area. The southwest portion of Kentucky and Jefferson County, which has low residential density, showed high levels of tornado exposure. In addition, relationships between the selected factors and tornado exposure also changed over space. For example, transportation costs as a percentage of income for the regional typical household was found to be strongly associated with tornado exposure in southwest Kentucky, whereas areas close to Jefferson County indicated an opposite association. The second part of this study involves the quantification of the tornado impact on roadways by using two different methods, and results were mapped. Although in both methods the same regions were found to be impacted, the second method highlighted the central CBGs rather than the peripheries. Information gathered by such an investigation can assist authorities in identifying vulnerable regions from both transportation network and community perspectives. From tornado debris handling to community preparedness, this type of work has the potential to inform sustainability-focused plans and policies in the state of Kentucky.

Suggested Citation

  • Mehmet Burak Kaya & Onur Alisan & Alican Karaer & Eren Erman Ozguven, 2024. "Assessing Tornado Impacts in the State of Kentucky with a Focus on Demographics and Roadways Using a GIS-Based Approach," Sustainability, MDPI, vol. 16(3), pages 1-27, January.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:3:p:1180-:d:1329963
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    References listed on IDEAS

    as
    1. Mehmet Baran Ulak & Ayberk Kocatepe & Lalitha Madhavi Konila Sriram & Eren Erman Ozguven & Reza Arghandeh, 2018. "Assessment of the hurricane-induced power outages from a demographic, socioeconomic, and transportation perspective," 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. 92(3), pages 1489-1508, July.
    2. Chia-Hsien Lin & Tzai-Hung Wen, 2011. "Using Geographically Weighted Regression (GWR) to Explore Spatial Varying Relationships of Immature Mosquitoes and Human Densities with the Incidence of Dengue," IJERPH, MDPI, vol. 8(7), pages 1-18, July.
    3. David Wheeler & Michael Tiefelsdorf, 2005. "Multicollinearity and correlation among local regression coefficients in geographically weighted regression," Journal of Geographical Systems, Springer, vol. 7(2), pages 161-187, June.
    4. A. Stewart Fotheringham & Wenbai Yang & Wei Kang, 2017. "Multiscale Geographically Weighted Regression (MGWR)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1247-1265, November.
    5. Stephen M. Strader & Walker S. Ashley & Thomas J. Pingel & Andrew J. Krmenec, 2017. "Projected 21st century changes in tornado exposure, risk, and disaster potential," Climatic Change, Springer, vol. 141(2), pages 301-313, March.
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