Suchergebnisse

The U.S. Department of Homeland Security (DHS) has mandated all regions to "carefully weigh the benefit of each homeland security endeavor and only allocate resources where the benefit of reducing risk is worth the amount of additional cost" (DHS, 2006, p. 64). This mandate illuminates the need to develop methods for systemic valuation of preparedness measures that support strategic decision making. This article proposes an analysis method that naturally emerges from the structure of the inoperability input‐output model (IIM) through which various regional‐ and sector‐specific impact analyses can be cost‐effectively integrated for natural and man‐made disasters. The IIM is described extensively in a companion paper (Lian et al., 2007). Its reliance on data classifications structured by the U.S. Census Bureau and its extensive accounting of economic interdependencies enables us to decompose a risk analysis activity, perform independent assessments, and properly integrate the assessment for a systemic valuation of risk and risk management activity. In this article, we account for and assess some of the major impacts of Hurricanes Katrina and Rita to demonstrate this use of the IIM and illustrate hypothetical, reduced impacts resulting from various strategic preparedness decisions. Our results indicate the capability of the IIM to guide the decision‐making processes involved in developing a preparedness strategy.

Governments around the world have implemented policies, programs, and technologies to reduce human suffering and help residents in the aftermath of disasters. The effectiveness of these programs has generally gauged by measures such as the amount of funding committed and the quantities of supplies moved. While these measures are useful for determining the effectiveness of logistical operations, they are poor surrogates for measuring the fundamental, desired outcomes - the well-being of individuals and families and their ability to reenter, prosper, and thrive in their communities. This paper identifies 12 community magnets through a meta-analysis of literature and personal experience. These community magnets describe the conditions of a community as it transitions from a survival state, to a stable state with limited capacity, and finally to a post-disaster normal. We also show how government and community functions can be mapped to how they motivate state-transitions across the community magnets of interest. This paper illustrates how this methodology can provide a foundation to unify approaches, integrate model and simulation work, promote systemic decision making, and ultimately improve how response decisions affect the recovery of whole communities.