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When Do Losses Count?

Six Fallacies of Natural Hazards Loss Data

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Current global and national databases that monitor losses from natural hazards suffer from a number of limitations, which in turn lead to misinterpretation and fallacies concerning the “truthfulness” of hazard loss data. These biases often go undetected by end users and are generally a product of the type of information stored in loss databases and how they are constructed. This paper highlights some common shortcomings and root causes for data misinterpretation by asking what biases are present in existing databases and how these then manifest themselves in actual loss figures. For illustrative purposes, four widely used, nonproprietary, Web-based hazard databases are examined: the international Emergency Events Database (EM-DAT), the international Natural Hazards Assessment Network (NATHAN), the Spatial Hazard Events and Losses Database for the United States (SHELDUS), and the National Weather Service's Storm Events. We identify six general biases: hazard bias, temporal bias, threshold bias, accounting bias, geographic bias, and systemic bias. To achieve resilient and sustainable communities, we need systematic and comprehensive inventories at the national as well as international level, and data that are temporally and geographically comparable.

Stephenson Disaster Management Institute, Louisiana State University, Baton Rouge, Louisiana

Environmental Studies/Department of Geography, Colgate University, Hamilton, New York

Department of Geography, Hazards and Vulnerability Research Institute, University of South Carolina, Columbia, South Carolina

CORRESPONDING AUTHOR: Dr. Susan L. Cutter, Department of Geography, University of South Carolina, 709 S Bull Street, Columbia, SC 29208, E-mail: scutter@sc.edu

Current global and national databases that monitor losses from natural hazards suffer from a number of limitations, which in turn lead to misinterpretation and fallacies concerning the “truthfulness” of hazard loss data. These biases often go undetected by end users and are generally a product of the type of information stored in loss databases and how they are constructed. This paper highlights some common shortcomings and root causes for data misinterpretation by asking what biases are present in existing databases and how these then manifest themselves in actual loss figures. For illustrative purposes, four widely used, nonproprietary, Web-based hazard databases are examined: the international Emergency Events Database (EM-DAT), the international Natural Hazards Assessment Network (NATHAN), the Spatial Hazard Events and Losses Database for the United States (SHELDUS), and the National Weather Service's Storm Events. We identify six general biases: hazard bias, temporal bias, threshold bias, accounting bias, geographic bias, and systemic bias. To achieve resilient and sustainable communities, we need systematic and comprehensive inventories at the national as well as international level, and data that are temporally and geographically comparable.

Stephenson Disaster Management Institute, Louisiana State University, Baton Rouge, Louisiana

Environmental Studies/Department of Geography, Colgate University, Hamilton, New York

Department of Geography, Hazards and Vulnerability Research Institute, University of South Carolina, Columbia, South Carolina

CORRESPONDING AUTHOR: Dr. Susan L. Cutter, Department of Geography, University of South Carolina, 709 S Bull Street, Columbia, SC 29208, E-mail: scutter@sc.edu
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