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Abstract A resilient community is one that does not experience serious degradation in critical services when a hazard occurs and, in the event of degradation or failure, recovers to a similar or better level of service in a reasonable amount of time. The most efficient means of making a community resilient is to make its critical services and capital robust – minimize damage/loss probability or the consequences from damage/loss through mitigation. If a community's critical services and capital are not robust, efforts must be put into recovery. Based on the measurable aspects of community capital, we have developed a simulation model called ResilUS that operationalizes community resilience across multiple, hierarchical scales in relation to a range of policy and decision variables associated with each scale. ResilUS is implemented using fragility curves to model loss and Markov chains to model recovery with respect to time. ResilUS was applied to the 1994 Northridge earthquake disaster in order to calibrate several output variables with empirical data. ResilUS represents a significant step forward for spatial decision support for disaster mitigation and recovery planning, in comparison to existing loss estimation models. Keywords: RESILIENCERECOVERYMODELINGDISASTERSNORTHRIDGE EARTHQUAKE
Miles et al. (Sat,) studied this question.