RESILIENCE QUANTIFICATION OF COMMUNITIES BASED ON PEOPLES FRAMEWORK

This paper presents a new methodology for computing community resilience. This topic has gained attention quickly due to the recent unexpected natural and man-made disasters; nevertheless, measuring resilience is still one of the most challenging tasks due to the complexity involved in the process. In previous studies, several attempts have been made to measure resilience, but none of them could outline a simple, yet exhaustive approach to reach this goal. Since “indicators” are perceived as important instruments to measure the resilience, in this correspondence, a complete indicator-based approach for measuring community resilience within the PEOPLES framework is proposed. PEOPLES is a holistic framework for defining and measuring disaster resilience of communities at various scales. It is divided into seven dimensions, and each dimension is further divided into several sub-components. Our method starts by collecting all the indicators available in the literature then classifying them under the seven dimensions of PEOPLES, creating a condensed list of indicators. Each indicator is accompanied by a measure, allowing the quantitative description of the indicator. To make the process quasi-dynamic, the measures are not characterized by a scalar value, but rather a normalized continuous function that marks out the functionality of the measure in time. If the measure could only be described by one value, a uniform function is considered. The service-time function of each measure could be obtained in two ways: the first is through a set of parameters that define the outline of the serviceability function (e.g. initial capacity, initial demand, capacity drop, recovery speed, etc.), while the second is by taking a group of serviceability measurements (snapshots) over the defined time window, and the line connecting all measurements is the serviceability function. All serviceability functions are weighted according to their contribution to the overall goal of achieving resilience and then aggregated into a single service-time function whose parameters are known. The final function (i.e., resilience function) describes the serviceability of a community over time and can be compared with the resilience functions of other communities. The present work contributes to this growing area of research as it provides a universal tool to quantitatively assess the resilience of communities at multiple scales.