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Spatially explicit mapping of hurricane risk in New England, USA using ArcGIS

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  • H. Poulos

Abstract

Hurricanes are one of the major natural disturbances affecting human livelihoods in coastal zones worldwide. Assessing hurricane risk is an important step toward mitigating the impact of tropical storms on human life and property. This study uses NOAA’s historical tropical cyclone database (HURDAT or ‘best-track’), geographic information systems, and kernel smoothing techniques to generate spatially explicit hurricane risk maps for New England. Southern New England had the highest hurricane risk across the region for all storm intensities. Long Island, western Connecticut, western Massachusetts, and southern Cape Cod, Martha’s Vineyard, and Nantucket had high storm probabilities and wind speeds. Results from this study suggest that these locations may be of central importance for focusing risk amelioration resources along the Long Island and New England coastlines. This paper presents a simple methodology for hurricane risk assessment that could be applied to other regions where long-term spatial storm track data exist. Copyright Springer Science+Business Media B.V. 2010

Suggested Citation

  • H. Poulos, 2010. "Spatially explicit mapping of hurricane risk in New England, USA using ArcGIS," 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. 54(3), pages 1015-1023, September.
    • Handle: RePEc:spr:nathaz:v:54:y:2010:i:3:p:1015-1023
    DOI: 10.1007/s11069-010-9502-0
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    References listed on IDEAS

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    1. Rob L. Hyndman & Xibin Zhang & Maxwell L. King,, 2004. "Bandwidth Selection for Multivariate Kernel Density Estimation Using MCMC," Econometric Society 2004 Australasian Meetings 120, Econometric Society.
    2. Zhang, Xibin & King, Maxwell L. & Hyndman, Rob J., 2006. "A Bayesian approach to bandwidth selection for multivariate kernel density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 3009-3031, July.
    3. Jonas Rumpf & Helga Weindl & Peter Höppe & Ernst Rauch & Volker Schmidt, 2009. "Tropical cyclone hazard assessment using model-based track simulation," 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. 48(3), pages 383-398, March.
    4. Wangli Xu & Lixing Zhu, 2009. "Kernel‐based Generalized Cross‐validation in Non‐parametric Mixed‐effect Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(2), pages 229-247, June.
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