方法对比

	Baseline Resilience Indicators for Communities	Disaster Recovery Curve Analysis
领域	Disaster Studies	Disaster Studies
方法族	Process / pipeline	Process / pipeline
起源年份	2010	2006
提出者	Susan L. Cutter, Christopher G. Burton & Christopher T. Emrich	Scott B. Miles & Stephanie E. Chang
类型	Composite indicator pipeline for inherent community resilience	Time-trajectory model of functional recovery and resilience
开创性文献	Cutter, S. L., Burton, C. G., & Emrich, C. T. (2010). Disaster Resilience Indicators for Benchmarking Baseline Conditions. Journal of Homeland Security and Emergency Management, 7(1), Article 51.	Miles, S. B., & Chang, S. E. (2006). Modeling Community Recovery from Earthquakes. Earthquake Spectra, 22(2), 439-458.
别名	BRIC Resilience Index, BRIC Index, Community Resilience Indicator Index	Recovery Trajectory Analysis, Resilience Curve Analysis, Community Recovery Modeling
相关	3	3
摘要	The Baseline Resilience Indicators for Communities (BRIC) is a composite-index method, introduced by Susan Cutter, Christopher Burton, and Christopher Emrich in 2010, for benchmarking the inherent, pre-event resilience of places to hazards and disasters. Rather than measuring how a community actually performed after a specific event, BRIC measures the standing conditions — social, economic, community-capital, institutional, infrastructural, and environmental — that theory and evidence link to a community's capacity to prepare for, absorb, and recover from shocks. Indicators are normalized, sign-corrected so that higher always means more resilient, averaged within each dimension into subindices, and summed into a single comparable score for every place. The 2014 refinement standardized the dimensions and demonstrated the index across all U.S. counties, making BRIC one of the most widely used baseline resilience metrics in disaster studies.	Disaster recovery curve analysis represents the recovery of a community or system after a disaster as a trajectory of functionality over time and uses that trajectory to quantify resilience. Building on the resilience-triangle concept and formalized for community recovery by Scott Miles and Stephanie Chang in 2006, the approach tracks a performance indicator — population, employment, housing occupancy, service capacity, or composite functionality — from its pre-event baseline, through the abrupt drop caused by the disaster, along the path back toward (or beyond) the baseline. From the curve, analysts read the magnitude of the initial loss, the speed and shape of recovery, the time to return, and the cumulative resilience loss represented by the area between the baseline and the recovery path. Comparing curves across communities or scenarios reveals what drives faster, fuller recovery and complements loss-estimation models that stop at the moment of impact.