Integrating spatial statistics tools for coastal risk management : A case-study of typhoon risk in mainland China

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Article number105018
Journal / PublicationOcean and Coastal Management
Online published18 Oct 2019
Publication statusPublished - 1 Feb 2020


Risk-informed planning and management are integral to the sustainability of hazard-prone areas at all spatial scales. This paper proposes a geographic information system- (GIS)-based approach to evaluate the spatial heterogeneities of coastal risk—using typhoons as a case. The study employs several spatial statistics-based distributional models to systematically evaluate the geographies of typhoon risk, its spatial patterns, and statistically significant hotspots of highest risk in coastal Mainland China. Additionally, we model the level of contribution of each risk parameter (i.e., hazard, vulnerability, and community resilience) towards overall risk. The results show that among 70% coastal counties exposed to typhoons, close to 30% are in the highest risk category (value ≥ 3rd quartile). The areas under the highest risk harbor more than 50 million people (~43%)—more than 7 million non-adults (0–14 years) (~42%), and approximately 2.5 million elderly people (above 65 years) (~31%)—which is critical. The Pearl-River-Delta region of Guangdong province in southern China is identified as the hotspot of highest typhoon risk, followed by Fujian and Zhejiang provinces—95% confidence. We propose the integration of spatial statistics and distributional models in coastal risk frameworks to evaluate and map the multi-level geographies of natural hazard risk to support risk management efforts. This study is novel to foster a GIS-based approach for risk management in the coastal regions of China, particularly in a disaster-risk-reduction context. The proposed approach and results have important policy implications and are useful for risk-informed decision-making such as prioritization for risk-relevant actions and resources treatment.

Research Area(s)

  • Coastal resilience, Geographic information systems (GIS), Natural hazards, Spatial distributional models, Tropical cyclones