An Urban Earthquake Disaster Risk Index

The Earthquake Disaster Risk Index (EDRI) is a composite index that allows direct comparison of the relative overall earthquake disaster risk of cities worldwide, and describes the relative contributions of various factors to that overall risk. The development of the EDRI brings together a body of knowledge about earthquake disasters from a wide range of disciplines to provide three principal benefits.

First, the direct comparison of overall earthquake disaster risk provides a useful tool for inter-city allocation of mitigation resources and effort. Most previous work in earthquake risk assessment has focused on a single component of the risk, and/or on a single region. The EDRI provides a systematic way to directly compare the overall earthquake disaster risk across a large number of cities or regions. Second, the disaggregated EDRI will increase awareness of the wide range of factors on which a city's earthquake disaster risk depends, from the expected magnitude of ground shaking, to the number of structures, to a city’s current economic situation. A comprehensive EDRI will highlight the fact that even in urban regions with low seismicity, an earthquake may occur, and if it does, the other characteristics of the city could turn that single event into a major disaster. Third, by reevaluating the index periodically, the EDRI may be used to monitor trends in earthquake disaster risk over time.

The EDRI has been developed using the following six-step procedure: (1) create a conceptual framework of all the factors that contribute to earthquake disaster risk—geological, engineering, economic, social, political, and cultural factors; (2) identify simple, measurable indicators to represent each of the factors in the framework (e.g., population, per capita Gross Domestic Product, percentage of the urbanized area that is soft soil); (3) combine the indicators mathematically into the composite EDRI; (4) gather data and evaluate the EDRI for each of the world’s major cities, (5) perform a sensitivity analysis to determine the robustness of the results, (6) interpret the numerical findings to assess their reasonableness and implications, and present the results in a variety of easily understandable graphical forms. A ten-city sample analysis was conducted to explore the challenges associated with this process, and to illustrate its feasibility and the usefulness of the results.