Measuring resilience at the community scale: the peoples framework

POLITECNICO DI TORINO FINAL PROJECT MEASURING RESILIENCE AT THE COMMUNITY SCALE: THE PEOPLES FRAMEWORK Author: Joan Puigdellivol Goday Tutor: Gian Paolo Cimellaro Academic year: 2012-2013 MEASURING RESILIENCE AT THE COMMUNITY SCALE: THE PEOPLES FRAMEWORK Gian Paolo Cimellaro 1 , Chris Renschler 2 , Andrei M. Reinhorn 3 ABSTRACT The paper is proposing a holistic framework for defining and measuring disaster resilience for a community at various scales. Seven dimensions characterizing community functionality have been identified and are represented by the acronym PEOPLES: Population and Demographics, Environmental/Ecosystem, Organized Governmental Services, Physical Infrastructure, Lifestyle and Community Competence, Economic Development, and Social-Cultural Capital. The proposed framework provides the basis for development of quantitative and qualitative models that measure continuously the functionality and resilience of communities against extreme events or disasters in any or a combination of the above-mentioned dimensions. Over the longer term, this framework will enable the development of geospatial and temporal decisionsupport software tools that help planners and other key decision makers and stakeholders to assess and enhance the resilience of their communities. Keywords: Community functionality; disaster resilience; population and demographics; environment/ecosystem; organized governmental services; physical infrastructure; lifestyle, community competence; social and cultural services. 1 A As ss si is st ta an nt t P Pr ro of fe es ss so or r, , D De ep pa ar rt tm me en nt t o of f S St tr ru uc ct tu ur ra al l, , B Bu ui il ld di in ng g & & G Ge eo ot te ec ch hn ni ic ca al l E En ng gi in ne ee er ri in ng g ( (D DI IS SE EG G) ), , P Po ol li it te ec cn ni ic co o d di i T To or ri in no o, , 1 10 01 12 29 9 T To or ri in no o, , I It ta al ly y, , E E- -m ma ai il l: : g gi ia an np pa ao ol lo o. .c ci im me el ll la ar ro o@ @p po ol li it to o. .i it t 2 A As ss so oc ci ia at te e P Pr ro of fe es ss so or r, , D De ep pa ar rt tm me en nt t o of f G Ge eo og gr ra ap ph hy y, , U Un ni iv ve er rs si it ty y a at t B Bu uf ff fa al lo o, , S SU UN NY Y, , 1 11 16 6 W Wi il lk ke es so on n Q Qu ua ad d, , B Bu uf ff fa al lo o, , N NY Y 1 14 42 26 61 1, , U U. .S S. .A A. . E E- -m ma ai il l: : r re en ns sc ch h@ @b bu uf ff fa al lo o. .e ed du u. . 3 P Pr ro of fe es ss so or r E Em me er ri it tu us s, , D De ep pa ar rt tm me en nt t o of f C Ci iv vi il l, , S St tr ru uc ct tu ur ra al l & & E En nv vi ir ro on nm me en nt ta al l E En ng gi in ne ee er ri in ng g, , U Un ni iv ve er rs si it ty y a at t B Bu uf ff fa al lo o ( (S SU UN NY Y) ), , 1 13 35 5 K Ke et tt te er r H Ha al ll l, , N No or rt th h C Ca am mp pu us s, , 1 14 42 26 60 0- -4 43 30 00 0, , B Bu uf ff fa al lo o, , N NY Y, , U U. .S S. .A A. . r re ei in nh ho or rn n@ @b bu uf ff fa al lo o. .e ed du u 1.1 Introduction Over the past years, the concept of resilience has gained attention recognizing the fact that not all threats or disasters can be averted. In fact, communities around the world are turning their attention to efforts and ways that can enhance their resilience against extreme events of any kind. Resilience is becoming increasingly important for modern societies as States start accepting the fact that they cannot prevent every risk from being realized, but rather they must learn to adapt and manage risks in a way that minimizes impact on human and other systems. This paper intends to provide a framework which is able to manage risks in a community at different scales (local, regional etc.), minimizing all the possible consequences and reaching as soon as possible the initial conditions again. The framework represents a new step in risk prevention and resilience management. It is based on seven dimensions which encompass all the key parameters of a modern society. 1.2 Defining Resilience The concept of resilience does not have a unique definition, because of its broad utilization in the field of ecology, social science, economy, and engineering with different meanings and implications. As Klein et al. stated (2003), the root of the term has to be found in the Latin word ‘resilio’ that literary means ‘to jump back’. The field, in which it was originally used, first, is still contested, however, it has been claimed that the study of resilience evolved from the disciplines of psychology and psychiatry in the 1940s, and it is mainly accredited to Norman Garmezy, Emmy Werner and Ruth Smith. The concept of resilience was originally established in the field of ecology by Holling (1973) who stated that for ecological systems resilience is “a measure of the persistence of systems and of their ability to absorb change and disturbance and still maintain the same relationships between populations or state variables. Stability represents the ability of a system to return to an equilibrium state after a temporary disturbance; the more rapidly it returns to equilibrium and the less it fluctuates, the more stable it would be”. The researches in resilience have forced to study it deeper and in a wider way. An extended literature review has been elaborated about resilience for years (Table -1), each contribution has added new nuances. Primarily resilience has been defined in context to the speed of systems to go towards equilibrium (Adger, 2000), capability to cope and bounce back (Wildavsky, 1988), ability to adapt to new situations (Comfort, 1999), be inherently strong and flexible and adaptive (Tierney & Bruneau, 2007), ability to withstand external impacts and recover with least outside interferences (Mileti, 1999). After the original definition of resilience in ecological systems, the word expanded its meaning to engineering, social and economical fields. In engineering, resilience is defined as the capability of a system to maintain its functions and structure in the face of internal and external change and to degrade gracefully when it must (Allenby and Fink, 2005). The main difference in defining and understanding resilience arises between the engineering approach that resilient recovery occurs by moving towards the previous stable state (Bruneau et al., 2003), and the ecological approach that resilience is developed to move towards a different system state (Handmer & Dovers, 1996). Social resilience, explained by Adger (2000), is the ability of groups or communities to cope with external stresses and disturbances as a result of social, political, and environmental change. Economic resilience was first defined by Rose and Liao (2005) as the inherent ability and adaptive response that enables firms and regions to avoid maximum potential losses. It has mainly been studied in context to seismic response and recovery (Tierney, 1997; Bruneau et al., 2003), community behavior (Chang & Shinozuka, 2004) and disaster hazard analysis (Rose, 2004b), among others (Rose,2009a). From the literature described above it appears that even though there are different opinions in defining resilience, there is some consensus in the measurement of system resilience. Generally resilience is measured in terms of the amount by which a system is able to avoid maximum impact (static resilience (Rose, 2004a)/robustness (McDaniels et al., 2008)) and the speed at which the system recovers from a disruption (dynamic resilience (Rose, 2004a)/ rapidity (Zobel, 2010)). As the research advances, one realizes that resilience must be studied on a global level and not individually. Bruneau et al. (2003) consider four types of resilience: technical; organizational; social; and economical, (TOSE). They note that different measures of resilience are needed to adequately address these different dimensions. Technical and economic, are related to the resilience of physical systems, and organizational and social, are more related to the community affected by the physical systems. Technical resilience concerns the ability of a system to function. Some measures of technical resilience for electric power systems are the percentage of demand met, the ratio of supply to demand, time to restoration, time to full recovery, etc. Organizational resilience concerns the ability of the organization(s) to manage the system. For example, measures of organizational resilience could include how well emergency units function, how quickly spare parts are replaced, how quickly repair crews are able to reach the affected components of a system, etc. Social resilience concerns how well society copes with the loss of services as a result of a blackout. For severe blackouts, social resilience can be the most critical dimension of resilience. Finally, economic resilience concerns the ability to reduce direct and indirect economic losses. Rose and Liao (2005) note that direct costs manifest themselves in four ways: lost sales; equipment damage/restart costs; spoilage of variable inputs; and idle labor costs (in addition to the costs of measures to reduce potential losses, such as backup generators and capacity expansion). Indirect costs are multipliers that ripple through the economy, such as impacts on the customers and suppliers of a disrupted firm, decreased consumer spending, decreased investments in the disrupted firm, public-health problems (such as dysfunctional sewage treatment), and economic disorder (looting, etc.) After the 4 dimension framework provided by Bruneau et al. (2003) various studies have been carried out, with the goal of practically evaluate the concept of resilience and identify the main units of measurement of it (Miles and Chang, 2011). In this paper, is intended to expand the holistic resilience approach with a seven dimension framework known by the acronym PEOPLES: Population and Demographics, Environmental/Ecosystem, Organized Governmental Services, Physical Infrastructure, Lifestyle and Community Competence, Economic Development, and Social-Cultural Capital. The seven dimensions are used to characterize the community functionality for defining and measuring disaster resilience at various scales. 1.3 Mathematical definition of Resilience The resilience index is illustrated graphically in Figure -1 as the normalized shaded area underneath the functionality function of a system Q(t). Analytically, Resilience is defined as     OE LC OE tT TOT LC t R r Q t T dt   (1) where QTOT(t) is the global functionality of the region considered which will be described in the next paragraph; TLC is the control time of the period of interest; r is a position vector defining the position P in the selected region where the resilience index is evaluated (Cimellaro et al. 2009, 2010). The community functionality is the combination of all functionalities related to different facilities, lifelines, etc. 1.4 Spatial vs. temporal scale of Community Resilience Resilience can be considered as a dynamic quantity that changes over time and across space. It can be applied to engineering, economic, social, and institutional infrastructure, and it can be used for various geographic scales. The first step to quantify the resilience index (R) is to define the spatial scale (e.g. building, structure, community, city, region, etc.) of the problem of interest (Figure -2). It is important to mention that the entire recovery process is affected by the spatial scale of the disaster. Huge disasters will take longer recovery process. The spatial scale will also be used to define the performance measures for the global functionality of the system. The second step is to define the temporal scale (short term emergency response, long term reconstruction phase, midterm reconstruction phase, etc.) of the problem of interest. The selection of the control period TLC will affect the R index, therefore when comparing different scenarios the same control period should be considered. 1.5 The seven dimensions of Community Resilience In order to emphasize the primary role of the human system in community sustainability, the acronym “PEOPLES” (Renschler et al. 2010, 2011) has been adopted to describe a framework that is built on and expands previous research at the Multidisciplinary Center of Earthquake Engineering Research (MCEER). This framework linked several previously identified resilience characteristics (technical, organizational, societal, and economic) and resilience attributes (r4: robustness, redundancy, resourcefulness, and rapidity) (Bruneau et al. 2003; Bruneau and Reinhorn, 2007; Cimellaro et al. 2010b). These are the four attributes along which resilience can be improved. Further details about the description of these attributes can be found in Cimellaro et al. (2010b). PEOPLES incorporates MCEER’s definitions of service functionality, and its components (assets, services, demographics) and parameters influencing resilience. The seven dimensions of the PEOPLES framework are the following: (1) Population and demographics; (2) Environment/ecosystem; (3) Organized government services; (4) Physical infrastructure; (5) Lifestyle and community competence; (6) Economic development; (7) Social-cultural capital In Table -2 is shown the complete list of components and sub-components of the “PEOPLES Framework”. The dimensions will be explained in the next points but further details about the description of each one can be found in Renschler et al. (Renschler et al., 2010, 2011). 1.6 Population and demographics The first dimension Population and demographics is used to describe and differentiate communities using for example the median income and age distribution which might be critical for understanding its economic, health and potential resilience. One measure of the functionality of this dimension (Qp) can be quantified for example by using the social vulnerability index (SoVI) proposed by Cutter (1996). Social vulnerability is defined as the inability of people, organizations, and societies to withstand adverse impacts from multiple stressors to which they are exposed. These impacts are due in part to characteristics inherent in social interactions, institutions, and systems of cultural values. Social vulnerability is a pre-existing condition of the community that affects the society’s ability to prepare for and recover from a disruptive event. Resilience focuses on the quality of life of the people at risk and develops opportunities to enhance a better outcome, while vulnerability places stress on the production of nature to resist the natural hazard. Manyena (2006) evaluates all the possible definitions provided from the 90’s up until the present, and compares the concept of resilience as the opposite of vulnerability. This dimension of vulnerability can be measured using a social index that describes the socioeconomic status, the composition of the population (elderly and children), 1.10 Lifestyle and Community Competence Lifestyle Community competence dimension deals with community action, critical reflection and problem solving skills, flexibility and creativity, collective efficacy, empowerment, and political partnerships (Norris et al., 2008). This dimension reflects the reality that community resilience is not simply a passive “bouncing back” to pre-disaster conditions (Brown and Kulig, 1996/97) but rather a concerted and active effort that relies on peoples’ ability to creatively imagine a new future and then take the requisite steps to achieve that desired future. It captures both the raw abilities of the community (e.g., ability to develop multifaceted solutions to complex problems, ability to engage in meaningful political networks) and the community’s perceptions of its ability to effect positive change. Communities that collectively believe that they can rebuild, restructure, and revive themselves are more likely to be persistent in the face of environmental, governmental, and other obstacles. Quality of life surveys often reveal whether members of a given community are committed to that community and willing to engage in the activities necessary to sustain the community, regardless of whether a disaster strikes. Less soft general indicators of community competence may include measures of migration, measures of citizen involvement in politics, and others. Disaster-specific indicators may include the comprehensiveness of community warning plans and procedures, and the extensiveness of citizen and organizational disaster training programs (Tierney, 2009). 1.11 Economic development Economic development dimension includes both the static assessment of a community’s current economy (economic activity) and the dynamic assessment of a community’s ability to continuously sustain economic growth (economic development). As described in the RICSA Poverty Project (2010), economic activity takes into account the supply of labor for the production of economic goods and services, which includes: “All production and processing of primary products whether for market, for barter or for own consumption, the production of all other goods for the market and, in the case of households which produce such goods and services for the market, the corresponding production for own consumption.” Economic development addresses the future and growth. It addresses a community’s efforts to increase its: “productive capacities ..., in terms of technologies (more efficient tools and machines), technical cultures (knowledge of nature, research and capacity to develop improved technologies), and the physical, technical and organizational capacities and skills of those engaged in production.” Resilient communities are characterized by their involvement in a diverse array of products and services that are both produced in and available to the community. Diversity in production and employment is linked to a community’s ability to substitute goods and services and shift employment patterns as the situation demands. The PEOPLES Resilience Framework incorporates three illustrative subcategories within this dimension: Industry – Production, Industry – Employment Distribution, and Financial Services. Primary indicators of this dimension include the proportion of the population that is employed within the various industries, and the variability that might characterize a community’s industrial employment distribution. This dimension is closely interconnected with the Population and Demographics dimension. For example, key indicators of economic development beyond employment and industry distribution include literacy rates, life expectancy, and poverty rates. Disaster-specific indicators related to economic development include extent of evacuation plans and drills for high-occupancy structures, adequacy of plans for inspecting damaged buildings following disasters, and adequacy of plans for postdisaster commercial reconstruction (Tierney, 2009). 1.12 Social/cultural capital Social/cultural capital dimension incorporates several subcategories, including education services, child and elderly services, cultural and heritage services, and community participation. Measuring social/cultural capital requires acquisition of tallies, such as the number of members belonging to various civil and community organizations. It also requires surveys of community leaders and their perceptions (e.g., quality of life surveys). For example, social support underlies many of the services associated with social/cultural capital. It includes both the “helping behaviors within family and friendship networks” and the “relationships between individuals and their larger neighborhoods and communities” (Norris et al., 2008, p. 139). People choose to provide social and cultural services that manifest and extend their sense of community, defined as an attitude of bonding with other members of one’s group Norris et al., 2008). They may feel an emotional connection to their neighborhood or city, which may or may not relate to the people who inhabit those places (Manzo and Perkins, 2006). For example, after Hurricane Katrina, many displaced residents of New Orleans expressed a strong desire to return home, irrespective of the people they knew or the jobs they once had. It seems likely that people with a strong “place attachment” would be more willing to act in order to help their community bounce back after a disaster, assuming that other essential factors such as employment and housing were available. Citizen participation takes into account the “engagement of community members in formal organizations, including religious congregations, school and resident associations, neighborhood watches, and self-help groups” (Norris et al., 2008, p. 139). Participation in community organizations is a means of demonstrating one’s care for one’s community. Pragmatically, participation in community organizations is a means for meeting and understanding one’s fellow citizens. It increases individuals’ circle of influence and perception of control. Measuring social/cultural capital requires acquisition of tallies, such as the number of members belonging to various civil and community organizations. It also requires surveys of community leaders and their perceptions (e.g., quality of life surveys). Disaster-specific indicators include existence of community plans targeting transportation-disadvantaged populations, adequacy of post-disaster sheltering plans, adequacy of plans for incorporating volunteers and others into official response activities, adequacy of donations management plans, and the community’s plans to coordinate across diverse community networks (Tierney, 2009). 1.13 General framework at community level The general framework at the community level is described by the equations below, where for each dimension a performance indicator and /or functionality is defined by combining different functionality dimensions:     , , , , , , TOT TOT P Env O Ph L Eco S Q t Q Q Q Q Q Q Q Q (4) where QTOT=global functionality; and Qx=functionality of each of the seven dimensions defined above. Within each dimension, functionality is defined as a combination of functionalities of their respective subsystems. For example, the functionality of the physical infrastructure Qph is defined as follows:     , , , ,... Ph Ph Hosp Ele Road Water Q t Q Q Q Q Q (5) where Qhosp=functionality of health care facilities; QEle=functionality of the electric network; QRoad=functionality of the road network; QWater=functionality of the water network; etc. Once the geographic scale is defined, it is possible to plot the global functionality QTOT over the region of interest in a contour plot at a given instant of time t, so time-dependent functionality maps of the region can be obtained. When also the temporal scale is defined through the control time TLC, then the resilience contour map of the region of interest can be plotted (e.g Figure -6). The Resilience contour map is obtained by integrating functionality maps over time using Equation (2), therefore they will be time independent, but they will vary in space from point to point in the selected region. Finally, the community resilience index Rcom is given by the double integral over time and space as follows:       OE LC C C OE tT com C TOT C LC A A t R R r A dr Q t A T dtdr      (6) where Ac is the total area of the selected region. For each dimension, a contour plot can be determined and combined using a layered approach as shown in Figure -3. Then a radar graph can be plotted and the area will determine the final value of the resilience score for the region of interest. This will identify gaps as well as priority actions, which will enter in the decision process. In summary a schematic step-by-step procedure of the MCEER methodology described in Figure -4 is the following: (1) Define extreme event scenarios (e.g. PSHA and ground motion selection); (2) Define the system model; (3) Evaluate the response of the model; (4) Compute performance measures (e.g. losses, recovery time, functionality, resilience); (5) Identify remedial mitigation actions (e.g. advanced technologies) and/or resilience actions (e.g. resourcefulness, redundancy, etc.); This design approach has analogies with the feedback loop taken from control theory. The same framework can be used for a region as well as a single structure (e.g. hospital). In this case, functionality reduces the functionality of a single hospital Qhosp which can be evaluated for example with the procedure described in Cimellaro et al. (2011), where the waiting time of a patient before receiving assistance is the main parameter of response to measure resilience. The hospital performance is described using a double exponential function, called metamodel which is able to estimate the hospital capacity and the dynamic response in real time incorporating the influence of damage of structural and non-structural components. 1.14 Recovery models In general, the performance measure of a community and a system during transient analysis is a function of time t and other parameters that depend on the type of a community considered. Therefore at time t after the crisis, functionality is given by     1 , , , n Q t f t x x (7) where 1,, n xx are the parameters involved in describing the recovery model. Several models have been presented in Cimellaro et al. (2010a) to describe the recovery function which can be either empirical or analytical depending on the source of data and the type of analysis. Empirical recovery functions are based on test or field data interpretation and engineering judgment. They can be built using the maximum likelihood method based on data reported from past extreme events as well as Monte Carlo simulations of specified community models. Since the complexity of the problem changes case by case, no specific model is presented in this part. Analytical recovery functions are developed from community response data obtained through analysis of the system using numerical simulations. For example, for the case of earthquake events, they can be obtained from nonlinear time history analysis, response spectral analysis, etc. Since the recovery process is characterized by uncertainties, the parameters considered in the model are modeled as random variables in order to quantify the uncertainties in the system. These uncertainties can be divided in aleatoric and epistemic uncertainties (Ang and Tang, 2007). Several models can be fitted to the observed data, and subsequently, model selection can be carried out using as goodness of fit measure, such as the r2 value. The essential requirement of the analytical recovery models is the simplicity, therefore the model should be selected so that it is easy to fit to real or numerical observation data and the number of parameters involved should be as low as possible. Below are reported five different recovery models which are grouped according to the two control periods (short term vs. long term). Long term recovery models are used when the reconstruction phase needs to be modeled, while short term recovery models are used when the emergency phase after the extreme event needs to be focused upon. Several long term recovery models are proposed in Cimellaro et al. (2010). They can be grouped according to the number of parameters (one, two, or three parameters). Complex recovery models with more parameters can be proposed, but simpler mathematical models have benefits over more complex ones. They have fewer unknown parameters, and thus it is easier to fit to data (fewer experiments are needed). There is also less chance of “overfitting”. With more free parameters, a model can be made to fit any data; however, at best the exercise is little more than curve fitting (with little meaningful understanding gained), while at worst the model may give an overconfidence in its predictive ability. The simplest recovery model is the uniform cumulative distribution (cdf) recovery function (also known as the linear model). This model is usually adopted when there is no information regarding the preparedness, resources available, societal response, etc.       0 0 0 0 0 ,RE R Q t Q F t t t T Q Q L        (8) where Q0 is the initial functionality after the drop; L0 is the initial total loss of functionality after the drop; QR is the residual functionality after the recovery process ends; and   00 ,RE F t t t T is the uniform cumulative distribution function which is given by       0 0 0 0 0 ,, RE RE RE tt F t t t T I t t T T     (9) where I(t0, t0+TRE) is the interval step function. The model is characterized by only one parameter (Figure -5a) which defines the slope of the curve and it represents rapidity (Cimellaro et al., 2010). The model can also be generalized by dividing the recovery process in several time intervals using a multilinear model that is given by          1 1 i i i i i iii tt Q t Q H t t Q Q tt          (10) where Qi is the residual functionality at the step i and Qi+1 is the residual functionality at the step i+1; H( ) is the Heaviside step function. Alternatively, lognormal cumulative distribution (cdf) recovery function, can be adopted, having three parameters (L0, θ, β), and it is given by       0 0 0 ,R Q t Q F t Q Q L         (11) where       2 2 log 2 1 ,2 x te F t dx x         (12) This model combines both the exponential recovery model proposed by Kafali and Grigoriu (2005) and the trigonometric recovery model proposed by Chang and Shinozuka (2004). The parameter L0 in Equation (11) can be used to define the initial total loss of functionality after the drop (Figure -5d). The parameter θ can be used to define the time frame (Figure -5e) when the societal response and recovery are driven by lack or limited organization and/or resources. The parameter β defines the rapidity of the recovery process (Figure -5f). The second group of recovery models is called short term recovery models and instead of using cdf shape models such as in the long term recovery models, they use the probability density functions (pdf) shape models. The simplest recovery model after the linear model proposed in Equation (8) is the Rayleigh probability density function recovery model, and it is defined as         0 1max f t b Q t L f t b  (13) where   2 2 2 2 t b t f t b e b       (14) The model is calibrated using two parameters: L0 is related to the robustness dimension (Figure -5b), while b is related to the rapidity and the delay in the recovery process (Figure -5c). 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Table -1 Literature review about resilience definitions Author Definition Holling (1973) Ecological systems resilience is a measure of the persistence of systems and of their ability to absorb change and disturbance and still maintain the same relationships between populations or state variables. Wildavsky (1991) Resilience is the capacity to cope with unanticipated dangers after they have become manifest, learning to bounce back. Horne and Orr (1998) Resilience is the ability of a system to withstand stresses of ‘environmental loading’... [it is] a fundamental quality found in individuals, groups, organizations, and systems as a whole. Haimes et al. (1998) Resilience is the ability of system to return to its optimal condition in a short period of time. Considering resilience one of four strategies for hardening a system, together with security, redundancy and robustness. Mileti (1999) Local resiliency with regard to disasters means that a locale is able to withstand an extreme natural event without suffering devastating losses, damage, diminished productivity, or quality of life and without a large amount of assistance from outside the community. Comfort (1999) Resilience is the capacity to adapt existing resources and skills to new situations and operating conditions. Adger (2000) Social resilience is the ability of groups or communities to cope with external stresses and disturbances as a result of social, political, and environmental change. Gunderson et al. (2002) Engineering resilience […] is the speed of return to the steady state following a perturbation […] ecological resilience […] is measured by the magnitude of disturbance that can be absorbed before the system is restructured…. Fiksel (2003) Resilience is the essence of sustainability […] the ability to resist disorder. Bruneau et al. (2003) Resilience is defined in terms of three stages: the ability of a system to reduce the probability of an adverse event, to absorb the shock if the adverse event occurs, and to quickly re-establish normal operating conditions. So resilience thus encompasses the four characteristics of robustness, redundancy, resourcefulness, and rapidity. Are considered four types of resilience: technical; organizational; economic; and social. Allenby and Fink (2005) Resiliency is defined as the capability of a system to maintain its functions and structure in the face of internal and external change and to degrade gracefully when it must. Rose and Liao (2005) Regional economic resilience is the inherent ability and adaptive response that enables firms and regions to avoid maximum potential losses. Hollnagel (2006) Resilience is defined as the intrinsic ability of an organization (system) to maintain or regain a dynamically stable state, which allows it to continue operations after a major mishap and/or in the presence of a continuous stress. Manyena (2006) Evaluating all the possible definitions provided from the 90’s to nowadays, resilience could be viewed as the intrinsic capacity of a system, community or society predisposed to a shock or stress to adapt and survive by changing its non essential attributes and rebuilding itself. Woods (2006) Evaluating all the possible definitions provided from the 90’s to nowadays, resilience could be viewed as the intrinsic capacity of a system, community or society predisposed to a shock or stress to adapt and survive by changing its non essential attributes and rebuilding itself. Holmgren (2007) Resilience is the ability of the system to return to a stable condition after a disruption. Distinguishing robustness and resilience, using robustness to imply that the system will remain (nearly) unchanged even in the face of disruption. Tierney and Bruneau (2007) Resilience is both the inherent strength and ability to be flexible and adaptable after environmental shocks and disruptive events. DHS (2008) Resilience is the ability of systems, infrastructures, government, business, and citizenry to resist, absorb, recover from, or adapt to an adverse occurrence that may cause harm, destruction, or loss of national significance. Haimes (2009) Resilience is defined as the ability of the system to withstand a major disruption within acceptable degradation parameters and to recover within an acceptable time and composite costs and risk. Vugrin et al. (2010) Given the occurrence of a particular disruptive event (or set of events), the resilience of a system to that event (or events) is the ability to efficiently reduce both the magnitude and duration of the deviation from targeted system performance levels. Table -2 Complete list of components and subcomponents of PEOPLES framework a) Distribution/Density b) Composition c) Socio-Economic Status i) Urban i) Age i) Educational Attainment iv) Home Ownership ii) Suburban ii) Gender ii) Income v) Housing Vacancies iii) Rural iii) Immigrant Status iii) Poverty vi) Occupation iv) Wildland iv) Race/Ethnicity a) Water Quality/Quantity b) Air Quality c) Soil Quality d) Biodiversity e) Biomass (Vegetation) f) Other Natural Resources a) Executive/Administrative b) Judicial c) Legal/Security i) Emergency Response and Rescue ii) Health and Hygiene a) Facilities b) Lifelines i) Residential i) Communications (1) Housing Units (2) Shelters ii) Health Care ii) Commercial (1) Distribution Facilities (3) Manufacturing Facilities (2) Hotels - Accommodations (4) Office Buildings iii) Food Supply iii) Cultural iv) Utilities (1) Entertainment Venues (4) Schools (2) Museums (5) Sports/Recreation Venues v) Transportation (3) Religious Institutions b) Collective Efficacy and c) Quality of Life Empowerment a) Financial Services b) Industry – Employment - Services c) Industry – Production i) Asset Base of Financial Institutions i) Agriculture x) Number of Corporate Headquarters i) Food Supply ii) Checking Account Balances (Personal and Commercial) ii) Construction xi) Other Business Services ii) Manufacturing iii) Consumer Price Index iii) Education and Health Services xii) Professional and Business Services iv) Insurance iv) Finance, Insurance and Real Estate (1) Employment Services v) Number and Average Amount of Loans v) Fortune 1000 (a) Flexibilities vi) Number of Bank and Credit Union Members vi) Fortune 500 (b) Opportunities vii) Number of Banks and Credit Unions vii) Information, Professional Business, Other (c) Placement viii) Savings Account Balances (Personal and Commercial) viii) Leisure and Hospitality (2) Transport and Utilities ix) Stock Market ix) Manufacturing (3) Wholesale and Retail a) Child and Elderly Services b) Commercial Centers c) Community Participation d) Cultural and Heritage Services e) Education Services f) Non-Profit Organizations g) Place Attachment 7) SOCIAL/CULTURAL CAPITAL 1) POPULATION AND DEMOGRAPHICS (1) Internet (2) Phones (3) TV (4) Radio (5) Postal (1) Acute Care (2) Long-Term Acute Care (4) Psychiatric (3) Primary Care (5) Specialty (1) Electrical (2) Fuel/Gas/Energy (3) Waste (4) Water (1) Aviation (2) Bridges (3) Highways (4) Railways (5) Transit (6) Vehicles (7) Waterways i) Conflict Resolution ii) Self-Organization a) Collective Action and Decision Making 2) ENVIRONMENTAL/ECOSYSTEM 3) ORGANIZED GOVERNMENTAL SERVICES 4) PHYSICAL INFRASTRUCTURE 5) LIFESTYLE AND COMMUNITY COMPETENCE 6) ECONOMIC DEVELOPMENT NAME TYPE OF INFRASTRUCTURE N° Name 1 Building 1 Facilities Residential Housing Units 2 Building 2 3 Building 3 4 Building 4 Commercial Office Buildings 5 Building 5 6 Building 6 7 Building 7 Residential Housing Units 8 Building 8 9 Building 9 Commercial Hotels – Accommodations 10 Building 10 Residential Housing Units Table -3 Type of infrastructure for each building GLOBAL RESILIENCE OUTPUT DATA Case: I II III IV Community Resilience (Ta; Tb) [%]: 98,3 95,0 74,8 58,2 Community Functionality (Tb) [%]: 100,0 100,0 88,6 58,2 Table -4 Global resilience output data for the four scenarios RESILIENCE OUTPUT DATA FOR EACH BUILDING Case: I II III IV I II III IV Building N° AT [days] RES [%] 1 0 133 152 inf. 95,6 82,1 80,2 45,2 2 0 91 336 inf. 96,0 87,2 63,8 48,1 3 0 0 0 inf. 99,6 99,6 99,6 84,9 4 0 0 51 inf. 99,4 99,4 97,5 82,5 5 0 34 843 inf. 98,7 96,9 61,3 74,3 6 0 51 744 inf. 98,7 96,0 61,3 74,3 7 0 34 643 inf. 98,6 96,6 61,7 74,1 8 0 0 942 inf. 98,9 98,9 64,3 77,8 9 0 66 511 inf. 97,7 93,0 61,4 67,4 10 0 0 118 inf. 99,8 99,8 97,2 89,2 Table -5 Output data of Resilience features for each building and each case. Table Legend: AT: Administrative time [days]; RES: Resilience over the control period [%]. RESILIENCE INDEX Case: I II III IV Community Resilience index [%]: 92,0 87,6 80,6 58,2 Time of completion of work, TCW [days]: 183,9 316,8 1033,0 inf. Table -6 Output data of Global Resilience for each case. Figure -1 Resilience (Cimellaro et al., 2010a) Figure -2 Spatial and temporal dimension of Resilience-Based design (RBD) using PEOPLES approach