A New Multicriteria Risk Mapping Approach Based on a Multiattribute Frontier Concept

作者：Yemshanov Denys^*; Koch Frank H; Ben Haim Yakov; Downing Marla; Sapio Frank; Siltanen Marty

来源：Risk Analysis, 2013, 33(9): 1694-1709.

DOI：10.1111/risa.12013

• pest risk mapping
• multicriteria aggregation
• robustness to uncertainty
• Agrilus biguttatus
• nondominant set
• multiattribute efficient frontier

Invasive species risk maps provide broad guidance on where to allocate resources for pest monitoring and regulation, but they often present individual risk components (such as climatic suitability, host abundance, or introduction potential) as independent entities. These independent risk components are integrated using various multicriteria analysis techniques that typically require prior knowledge of the risk components%26apos; importance. Such information is often nonexistent for many invasive pests. This study proposes a new approach for building integrated risk maps using the principle of a multiattribute efficient frontier and analyzing the partial order of elements of a risk map as distributed in multidimensional criteria space. The integrated risks are estimated as subsequent multiattribute frontiers in dimensions of individual risk criteria. We demonstrate the approach with the example of Agrilus biguttatus Fabricius, a high-risk pest that may threaten North American oak forests in the near future. Drawing on U.S. and Canadian data, we compare the performance of the multiattribute ranking against a multicriteria linear weighted averaging technique in the presence of uncertainties, using the concept of robustness from info-gap decision theory. The results show major geographic hotspots where the consideration of tradeoffs between multiple risk components changes integrated risk rankings. Both methods delineate similar geographical regions of high and low risks. Overall, aggregation based on a delineation of multiattribute efficient frontiers can be a useful tool to prioritize risks for anticipated invasive pests, which usually have an extremely poor prior knowledge base.

• 出版日期2013-9