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1 10. Community Resilience Metrics

2 10.1. Background

3 Community resilience metrics or indicators come in a wide variety of types. They can be descriptive or 

4 quantitative; they can be based on interviews, expert opinion, engineering analysis, or pre-existing

5 datasets. They can also be presented as an overall score or as a set of separately reported scores across a 

6 broad spectrum of physical, economic, and social dimensions. Regardless of the methodologies used to 

7 develop and summarize the results, effective community resilience metrics must address two questions 

8 (National Academies 2012a):

9 1. How can community leaders know how resilient their community is?

10 2. And how can they know if their decisions and investments to improve resilience are making a 

significant difference?111

12 In 2012, the National Academies Committee on Increasing National Resilience to Hazards and Disasters

13 and the Committee on Science, Engineering, and Public Policy evaluated 17 approaches to measuring

14 various aspects of resilience. The authors concluded that none of the 17 existing methodologies

15 satisfactorily addressed the two basic questions posed above. As a result, one of the six main 

16 recommendations coming out of the report was the development of a ―national resilience scorecard, from 

17 which communities can then develop their own, tailored scorecards‖ (National Academies 2012b).

18 Similar recommendations can be found in other recent reviews of disaster risk reduction and disaster 

19 resilience (Government Office for Science 2012; UNISDR 2012). The need for a tailorable or locally 

20 relevant scorecard recognizes that a single prescriptive scorecard is unlikely to be appropriate for 

21 communities of all sizes and types (e.g., from small tourism- or agriculture-centric communities to large 

22 financial- or industrial-centric cities) and for all planning scenarios (e.g., from preliminary scoping studies 

23 to comprehensive planning with ongoing follow-up assessments).

24 10.2. Desirable Characteristics for Community Resilience Metrics

25 From the community perspective, effective community resilience metrics should be accurate, reliable, 

26 comprehensive, scalable, affordable, and actionable indicators of the community‘s capacity to respond to 

27 and recover from a specified disaster scenario. Cutter (2014) suggests that communities seek a resilience 

28 measurement tool that meets the following criteria:

29  Open and transparent

30  Aligns with the community‘s goals and vision

31  Measurements...

32  are simple, well documented

33  can be replicated

34  address multiple hazards

35  represent community‘s areal extent, physical (manmade and environmental) characteristics, 

36 and composition/diversity of community members

37  are adaptable and scalable to different community sizes, compositions, changing 

38 circumstances

39 For purposes of this framework, we are specifically interested in community resilience metrics or tools 

40 that will reliably predict the physical, economic, and social implications (either positive or negative) of 

41 community decisions (either active or passive) made with respect to planning, siting, design, construction, 

42 operation, protection, maintenance, repair, and restoration of the built environment.

 

1

As stated in (National Academies 2012b), “measuring resilience is challenging but essential if communities want to 

track their progress toward resilience and prioritize their actions accordingly.”
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43 10.3. Types of Metrics

44 As defined in PPD-21 (White House 2013) and emphasized throughout this framework, the concept of 

45 disaster resilience extends well beyond the magnitude of direct physical damage sustained by the various

46 components of the built environment under a specified disaster scenario. The centrality of community 

47 impacts and community recovery to the concept of community resilience demands that community 

48 resilience be evaluated and measured in much broader terms than, for example, critical infrastructure

49 vulnerability.

50 Looking beyond direct physical damage and direct repair costs for the built environment, at least three 

51 broad categories of metrics should be considered by communities: (1) recovery times, (2) economic 

52 vitality metrics, and (3) social well-being metrics. A community can use these end result metrics to 

53 measure improvements through proactive planning and implementation. Resilience planning and 

54 implementation of plans will produce a faster and more robust recovery that avoids or minimizes the 

55 expected negative economic and social impacts of hazard scenarios. However, predicting how these end 

56 result metrics will be impacted by specific community planning and implementation decisions is a 

57 challenging and ongoing area of research.

58 Many indicators of community resilience may have a direct and quantifiable cause-and-effect influence 

59 on resilience; whereas others may either have some postulated influence on resilience or simply be 

60 correlated with resilience. Examples of indicators that may influence or correlate with recovery times, 

61 economic vitality, and social well-being are provided below. 

62 10.3.1. Recovery Times

63 Recovery times for the built environment are easy to grasp as resilience goals, but difficult to predict with

64 precision or confidence. Predicting recovery times under different planning scenarios should consider:

65  Designated performance level or restoration level for each building cluster and infrastructure 

66 system

67  Original criteria used in the design of the various components of the built environment and their 

68 condition immediately prior to the specified disaster scenario

69  Loading conditions applied to the built environment during and after the specified hazard 

70 scenario

71  Spatial and logical distribution of physical damage to the built environment

72  Availability of resources and leadership to strengthen (pre-event) or repair (post-event) the built 

73 environment

74  Critical interdependencies among the built environment and social structures within a community 

75 (See Chapter 2)

76 Recovery times have a direct bearing on many economic and social functions in a community. As such, 

77 explicit estimates (or at least a general sense) of system recovery times become a prerequisite for most, if 

78 not all, other measures of community resilience. Due to the large volume of data required and the inherent 

79 complexity of ―system-of-systems‖ modeling, recovery times are likely to be estimated based on some 

80 combination of simplified modeling, past experience, and/or expert opinion.

81 Examples of community-level recovery time goals by building cluster and infrastructure system are 

82 provided in Table 3-10 through Table 3-12 in Chapter 3. These community-level recovery times are built83 up from the buildings and sector-level recovery time examples discussed in Chapters 5 through 9. Each 

84 community should define its own set of building clusters, infrastructure systems, and designated 

85 performance levels that reflect its makeup and priorities.

86 10.3.2. Economic Vitality

87 Economic health and development are major concerns for communities. Economic development concerns 

88 include attracting and retaining businesses and jobs, building the tax base, addressing poverty and 
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89 inequality, enhancing local amenities, and economic sustainability. These factors are discussed below. 

90 Further background on economic modeling approaches and issues appears later in Section 10.5.

91 10.3.2.1. Attracting and Retaining Businesses and Jobs

92 Attracting and retaining businesses and jobs is a major concern of most communities. A community that 

93 cannot attract and retain businesses and jobs is in decline. Communities also prefer businesses that 

94 produce high-paying jobs. Metrics for this would include the employment rate, per capita income or, per 

95 capital Gross Domestic or Regional Product, and education attainment rate. 

96 Metrics indicative of a community‘s ability to continue attracting and retaining businesses and jobs 

97 through and after a hazard event would include the resiliency of infrastructure systems.

98 10.3.2.2. Tax Base

99 For most cities, local revenue sources consist of property tax and/or sales tax. Sales tax revenue is 

100 increased by attracting commercial businesses and jobs, and property tax revenue is increased by 

101 increasing property values.

102 Tax base indicators include real-estate prices, rents, and amount of tourism (for hotel tax revenues). 

103 Metrics indicative of how a community‘s tax base would be affected by a hazard event include the extent

104 of property insurance coverage across the community, percent of property in areas susceptible to hazards 

105 (like flood plains), adopted building codes, and the number of buildings that fail to meet current codes.

106 10.3.2.3. Poverty and Income Distribution

107 Poverty and income distribution are a major concern of local communities. Many projects communities 

108 pursue aim to decrease poverty in their neighborhoods, and a significant amount of external funding 

109 available to communities aim to alleviate poverty. This concern intersects with community resilience 

110 because the disadvantaged are often the most vulnerable to disasters. Metrics of poverty and income 

distribution include the poverty rate and the Gini coefficient, a measure of income dispersion.2111

112 Metrics that indicate or influence how a hazard event might affect poverty and income distribution 

113 include the poverty rate itself because poor people tend to fare worse in disasters.

114 10.3.2.4. Local Services and Amenities

115 Local services and amenities include the infrastructure systems discussed in Chapters 6-9, but also 

116 include a variety of other characteristics and services associated with communities, such as public 

117 transportation, parks, museums, restaurants, theaters, etc. Local services and amenities improve the 

118 quality of life for local residents. In addition, there is an expectation that improving local amenities will 

119 indirectly help attract and retain businesses and jobs. Amenities are provided by multiple sources. Some 

120 are provided by local governments, some are privately provided, and some are environmental. Metrics for 

121 infrastructure systems are discussed in Chapters 6-9 and in Section 10.3.5 of this chapter. Metrics for 

122 amenities will depend on the community.

123 10.3.2.5. Sustainability

124 Local communities are interested in ensuring that their community is sustainable. Sustainability includes 

125 two distinct ideas: 1) protecting and improving the environment (i.e., being ―green‖ and maintaining a 

126 small footprint); and 2) producing a vibrant and thriving economy. It is desirable that a community 

127 remain sustainable, even amid disasters. Metrics of economic sustainability include population growth 

128 rates and growth rates of Gross Domestic or Regional Product.

 

2

http://data.worldbank.org/indicator/SI.POV.GINI
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129 Factors that might affect a community‘s sustainability in the presence of hazard events include the degree 

130 to which the local economy depends on a single industry. Metrics could include percent of jobs in the 

131 service industry or percent of jobs in agriculture and mining.

132 10.3.2.6. Other Economic Indicators

133 There are a number of economic indicators that are associated with or affect non-economic aspects of 

134 community resilience. For example, debt ratios generally impact a community‘s ability to deal with 

135 disasters. Poverty impacts the probability that people will rebound from a disaster, as do ownership of a 

136 car or phone. Similarly, job continuity and economic sustainability will strongly influence the continuity 

137 of social networks.

138 10.3.3. Social Well-being

139 Reflecting the hierarchy of human needs presented in Section 2.3, social metrics should address:

140  Survival – preservation of life and availability of water, food, clothing and shelter

141  Safety and security – personal safety, financial (economic) security, and health/well-being 

142  Sense of belonging – belonging and acceptance among family, friends, neighborhoods, and 

143 organizations

144  Growth and achievement – opportunities for recognition and fulfillment

145 The resilience of a community following a hazard event depends on how well these needs are met.

146 Examples of indicators or metrics for each of these needs are provided below. An example of a resilience 

147 plan that includes several of these indicators is the Canterbury Wellbeing Index (CERA 2014).

148 10.3.3.1. Survival

149 Survival depends on the ability of a community‘s residents, employees and visitors to possess physical 

150 requirements, including water, food, shelter, and clothing. Access to these requirements depends on the 

151 functionality of the supporting physical infrastructure, availability of distribution systems, and personnel. 

152 These tasks may be performed by the governmental organizations, non-governmental aid organizations, 

153 or the private sector. Metrics for survivability could include housing availability and affordability, 

154 poverty rates, homeless rates, etc.

155 Metrics affecting a community member‘s chance of survival during or after a hazard event include: 

156  Building code adoption and enforcement history

157  Existence and effectiveness of warning systems

158  Existence of comprehensive emergency management plans (mutual aid pacts, emergency 

159 response resources (e.g., urban search and rescue teams), public shelters)

160  Number of community service organizations that assist in distributing water, food, or clothing or 

161 providing shelter in the wake of a disaster

162  Level of household disaster preparation

163  Percentage of homes that are owner occupied (i.e., renters may be more vulnerable in disasters)

164  Percentage of insured homes and businesses

165  Availability of short- and medium-term accommodation

166  Distance to family/friends unaffected by the disaster

167 10.3.3.2. Safety and Security

168 Safety and security includes all aspects of personal and financial (economic) security, and health and 

169 well-being. People require safety and security in their personal lives from situations of violence, physical 

170 or verbal abuse, war, etc., as well as knowing that the safety of their family and friend networks are

171 secure. Individuals also require financial safety, which can include job security, a consistent income, 
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172 savings accounts, insurance policies, and other safety nets. Finally, people require safety from negative 

173 health conditions, so that they can enjoy life and consistent well-being.

174 Examples of metrics for personal safety evaluated before and after a hazard event could include 

175 community statistics on assaults, property offenses, re-offending rates, and reports on child abuse or 

176 neglect. 

177 Examples of metrics for financial (economic) security include employment rates (also covered in Section 

178 10.3.2.1 under economic metrics). Additionally, metrics that would be indicative of how a community 

179 member‘s employment would be affected by a hazard event include occupation type (e.g., some 

occupations, more than others, can be severely affected by a hazard event)3180 , education levels, percentage 

181 of residents that commute other communities for work, and gender (i.e., women may have a more difficult 

182 time than men due to employment type, lower wages, and/or family care responsibilities).

183 Examples of metrics for health and well-being of community members include acute medical admissions, 

184 immunization rates, cancer admissions, substance abuse rates, and blood donor rates. Additionally, 

185 metrics that would be indicative of how a community member‘s health/well-being would be affected by a 

186 hazard event include percentage of the population with health insurance, access to health services (e.g., 

187 health system demand and capacity indicators: emergency room, in-patient beds, out-patient clinics, 

188 community health centers, mental health services, etc.), and community demographics (e.g., age 

189 distribution, number of individuals with disabilities or access and functional needs, etc.).

190 10.3.3.3. Sense of Belonging

191 Social metrics can also address the belonging need, which can represent belonging and acceptance among 

192 various groups of people (e.g., family, friends, school groups, sports teams, work colleagues, religious 

193 congregation) or belonging to a place or location. Examples of metrics or indicators related to sense of 

194 belonging include: 

Civic participation4195 :

196  Voter registration or voter participation rates

197  Involvement in local action groups

198  Perception of being well-informed of local affairs

199 Social networks:

200  Frequency of contact with friends, family, neighbors, etc.

201  Number of close friends/family (geographically)

202 Social participation:

203  Membership in (and frequency of involvement in ) community-wide social, cultural, and leisure

204 clubs/groups including sports clubs 

205  Membership in (and frequency of involvement in) religious organizations and other belief 

206 systems

207  Volunteering

208 Trust

209  Confidence in leadership (at various levels)

210  Trust in others (similar or dissimilar to member)

 

3

Reference to University of South Carolina – Social Vulnerability Index

4

Foxton, F. and R. Jones. 2011. Social Capital Indicators Review. Office for National Statistics

http://www.ons.gov.uk/ons/dcp171766_233738.pdf
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211 10.3.3.4. Growth and Achievement

212 Humans need to feel a sense of achievement and respect in society, accompanied by the need for 

213 continual growth and exploration. Examples of metrics or indicators related to growth and achievement 

214 include: 

215  Education

216  System capacity (sufficient numbers of teachers, classrooms, books, etc.)

217  Graduation rates

218  Memberships to public libraries

219  Education levels

220  Participation rates in arts and recreation

221 10.3.4. Hybrids

222 Some metrics combine several indicators into an overall score. Often, additional types of metrics, beyond 

223 the three broad categories discussed above, are included. These other types of metrics, such as system224 specific or ecological/environmental metrics, are discussed below in Section 10.3.5.

225 Due to the sparsity of data, the unique aspects of each hazard event, and the lack of generally applicable 

226 community resilience models, the scaling and weighting schemes used to aggregate disparate metrics into 

227 an overall score of community resilience are largely based on reasoning and judgment. A related 

228 technique is to attempt to monetize all of the dimensions (e.g., the statistical value of lost lives, lost jobs, 

229 lost business revenue, increased healthcare costs, etc.), but this approach cannot adequately address the 

230 social dimensions of community resilience.

231 10.3.5. Other Metrics

232 Examples of system-specific metrics include indicators such as:

233  Temporary shelter demand in the housing sector

234  Water pressure level or water quality level in water supply systems

235  Vehicles per hour or shipping tonnage capacities in transportation systems

236  Percentage of dropped calls or undelivered messages in communications systems

237  Percentage of customers without service in electrical power systems

238 In the context of this framework, these system-level indicators can be thought of as performance levels to 

239 gauge recovery time for the built environment.

240 Ecological or environmental metrics include indicators such as debris and hazardous waste volumes (by 

241 which landfill and waste management requirements can be assessed), indicators of water and soil quality 

242 (e.g., salinity), and many more. While very important due their impact to public health, wildlife 

243 management, etc., these metrics address impacts and planning issues that are, for the most part, outside 

244 the scope of this framework. 

245 10.4. Examples of Existing Community Resilience Assessment Methodologies

246 As discussed in Section 10.1, a variety of community-wide resilience assessment methodologies was

247 presented in the research literature. In this section, we present brief overviews of nine existing 

248 methodologies and evaluate their applicability as tools for assessing both current resilience and plans for

249 improved resilience within the context of planning decisions regarding the built environment. Not all of 

250 these methodologies were developed to address community resilience, but they are considered as relevant 

251 and potentially applicable in whole or part. This list is not meant to be complete and is expected to evolve 

252 along with this framework, as additional research and pilot studies are completed.
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253 10.4.1. SPUR Methodology

254 The SPUR methodology provides ―a framework for improving San Francisco‘s resilience through seismic 

255 mitigation policies.‖ The stated goals of the SPUR report (2009) are:

256 1. Define the concept of “resilience” in the context of disaster planning,

257 2. Establish performance goals for the “expected” earthquake that supports our definition of 

258 resilience,

259 3. Define transparent performance measures that help us reach our performance goals; and

260 4. Suggest next steps for San Francisco’s new buildings, existing buildings and lifelines.

261 The SPUR methodology focuses on establishing performance goals for several clusters of buildings (i.e., 

262 groups of buildings that provide a community service, such as critical response facilities, emergency 

263 housing, or neighborhood services) and establishing target recovery times for a specified earthquake 

264 scenario in the San Francisco area. While economic and social metrics are not direct outputs of the SPUR 

265 methodology, the building clusters selected and recovery time goals provided are clearly intended to 

266 improve both the economic and social resilience of San Francisco. Similarly, although SPUR focuses on 

267 earthquakes as the primary hazard, the underlying methodology is applicable to other perils.

268 10.4.2. Oregon Resilience Plan

269 In 2011, the Oregon Seismic Safety Policy Advisory Commission (OSSPAC) was directed by House 

270 Resolution 3 ―to lead and coordinate preparation of an Oregon Resilience Plan that reviews policy 

271 options, summarizes relevant reports and studies by state agencies, and makes recommendations on 

272 policy direction to protect lives and keep commerce flowing during and after a Cascadia earthquake and 

273 tsunami.‖ The OSSPAC assembled eight task groups (earthquake and tsunami scenario, business and 

274 work force continuity, coastal communities, critical buildings, transportation, energy, information and 

275 communications, water and wastewater) and assigned the following tasks to each group:

276 1. Determine the likely impacts of a magnitude 9.0 Cascadia earthquake and tsunami on its 

277 assigned sector, and estimate the time required to restore functions in that sector if the 

278 earthquake were to strike under present conditions;

279 2. Define acceptable timeframes to restore functions after a future Cascadia earthquake to fulfill 

280 expected resilient performance; and 

281 3. Recommend changes in practice and policies that, if implemented during the next 50 years, will 

282 allow Oregon to reach the desired resilience targets.

283 The Oregon Resilience Plan (2013) builds on the SPUR methodology and the Resilient Washington State 

284 initiative to produce a statewide projection of the impacts of a single earthquake and tsunami scenario. 

285 Immediate impacts include lives lost, buildings destroyed or damaged, and households displaced.

286 Moreover, a particular statewide vulnerability identified in the study is Oregon‘s liquid fuel supply and 

287 the resulting cascade of impacts induced by a long-term disruption of the liquid fuel supply. The study 

288 includes recommended actions to reduce the impacts of the selected hazard scenario and shorten the 

289 state‘s recovery time.

290 10.4.3. UNISDR Disaster Resilience Scorecard for Cities

291 The United Nations International Strategy for Disaster Risk Reduction (UNISDR) Disaster Resilience 

292 Scorecard for Cities ―provides a set of assessments that will allow cities to understand how resilient they 

293 are to natural disasters.‖ The Scorecard is ―intended to enable cities to establish a baseline measurement 

294 of their current level of disaster resilience, to identify priorities for investment and action, and to track 

295 their progress in improving their disaster resilience over time.‖ There are 85 disaster resilience evaluation 

296 criteria grouped into the following areas:

297  Research, including evidence-based compilation and communication of threats and needed 

298 responses
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299  Organization, including policy, planning, coordination and financing

300  Infrastructure, including critical and social infrastructure and systems and appropriate 

301 development

302  Response capability, including information provision and enhancing capacity

303  Environment, including maintaining and enhancing ecosystem services

304  Recovery, including triage, support services and scenario planning.

305 Each evaluation criterion is broken down into the aspect of disaster resilience being measured, an 

306 indicative measurement, and the measurement scale (from 0 to 5, where 5 is best practice).

307 The formal checklist is organized around ―10 Essentials for Making Cities Resilient,‖ which were 

308 developed to align with the five priorities of the Hyogo Framework (UNISDR 2005). The overall score is 

309 the percentage of possible points from each of the 85 measures. It is suggested that cities plan on 2 to 3 

310 people working for a minimum of 1 week to complete an assessment, ranging up to 2 months for a more 

311 detailed and comprehensive assessment.

312 10.4.4. CARRI Community Resilience System

313 The Community and Regional Resilience Institute‘s Community Resilience System (CARRI CRS 2013)

314 ―is an action-oriented, web-enabled process that helps communities to assess, measure, and improve their 

315 resilience to ... threats and disruptions of all kinds, and ultimately be rewarded for their efforts. The CRS 

316 brings together people, process and technology to improve resilience in individual communities. The 

317 system includes not only a knowledge base to help inform communities on their resilience path but also a 

318 process guide that provides a systematic approach to moving from interest and analysis to visioning and 

319 action planning. It also provides a collaborative mechanism for other interested stakeholders to support 

320 community efforts.‖

321 The CRS is a DHS/FEMA funded initiative. It began in 2010, convening three working groups: 

322 researchers (the Subject Matter Group), community leaders (the Community Leaders Group), and 

323 government/private sector representatives (the Resilience Benefits Group). The findings of these working 

324 groups culminated in the development of the CRS web-based tool along with pilot implementations in 

325 eight communities commencing in the summer of 2011.

326 The CRS addresses 18 distinct Community Service Areas (CSAs) and is designed specifically for use by 

327 community leaders. The web process is a checklist driven approach, with questions tailored for each of 

328 the CSAs. The answer to a question may trigger additional questions. For many of the questions, 

329 comment fields are provided so that communities may answer the questions as specifically as possible. 

330 The CARRI team notes that a facilitated approach (i.e., an outside group coming in, such as CARRI), is 

331 most effective. ―The CRS process works more productively as a ―partially facilitated‖ model where some 

332 supportive expertise assists communities in applying aspects of resilience to and embedding them within 

333 their community circumstances and processes.‖

334 10.4.5. Communities Advancing Resilience Toolkit (CART)

335 The Communities Advancing Resilience Toolkit (CART 2012) was developed by the Terrorism and 

336 Disaster Center at the University of Oklahoma Health Sciences Center. It was funded by the Substance 

337 Abuse and Mental Health Services Administration, U.S. Department of Health and Human Services, and 

338 the National Consortium for the Study of Terrorism and Responses to Terrorism, U.S. Department of 

339 Homeland Security, and by the Centers for Disease Control and Prevention.

340 CART is designed to enhance community resilience through planning and action. It engages community 

341 organizations in collecting and using assessment data to develop and implement strategies for building 

342 community resilience for disaster prevention, preparedness, response, and recovery. The CART process

343 uses a combination of qualitative and quantitative approaches, and it involves the following steps:

344 1. Generating a community profile (CART Team and Partners)
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345 2. Refine the community profile (Community Work Groups)

346 3. Develop a strategic plan (Community Planning Groups)

347 4. Implement the plan (Community Leaders and Groups)

348 The CART approach is not hazard specific, and it is applicable across communities of varying size and 

349 type. It is innovative, providing a complete set of tools and guidelines for communities to assess their 

350 resilience across a number of domains. The toolkit includes the CART assessment survey, key informant 

351 interviews, data collection framework, community conversations, neighborhood infrastructure maps, 

352 community ecological maps, stakeholder analysis, SWOT analysis, and capacity and vulnerability 

353 assessment. The focus of the approach is to provide a process that engages communities in thinking about 

354 resilience and provide a foundation to move forward into sophisticated activities.

355 10.4.6. Baseline Resilience Indicators for Communities (BRIC)

356 The Baseline Resilience Indicators for Communities (BRIC, Cutter et al. 2014) process builds on prior 

357 work by Cutter et al., and is based on empirical research with solid conceptual and theoretical 

358 underpinnings. BRIC measures overall pre-existing community resilience. The approach provides an 

359 empirically based resilience metric for use in a policy context. Using data from 30 public and freely 

360 available sources, BRIC comprises 49 indicators associated with six domains: 

361  Social (10 indicators)

362  Economic (8 indicators)

363  Housing and infrastructure (9 indicators)

364  Institutional (10 indicators)

365  Community Capital (7 indicators)

366  Environmental (5 indicators)

367 BRIC is not hazard specific, and it has been implemented at the county level. The 49 indicators were

368 selected through conceptual, theoretical, and/or empirical justification as capturing qualities associated

369 with community resilience. Indicators in the aforementioned domains determine areas that policy makers

370 should invest for intervention strategies to improve resilience scores.

371 10.4.7. Rockefeller Foundation City Resilience Framework

372 The City Resilience Framework (CRF 2014) is a framework ―for articulating city resilience‖ developed 

373 by Arup with support from the Rockefeller Foundation 100 Resilient Cities initiative. One merit of this 

374 framework is that it is based on a very extensive literature review involving cities with different 

375 characteristics and a substantial amount fieldwork to collect data and develop case studies. The 

376 framework organizes 12 so-called ―key indicators‖ into 4 categories:

377  Leadership and strategy

378  Health and wellbeing

379  Infrastructure and environment

380  Economy and social

381 This organization integrates social and physical aspects, and it considers human-driven processes as 

382 inherent components of the system-of-systems, making the community fabric of a city. 

383 Economic/financial constraints are also considered in an integral way, providing a realistic setting for its 

384 application for planning purposes. In turn, the 12 key indicators span 7 qualities of what is considered a 

385 resilient city: being reflective, resourceful, robust, inclusive, redundant, integrated, and/or flexible.

386 The CRF will serve as the basis for developing a City Resilience Index in 2015. The CRF report states 

387 that the CRI will further refine the 4 categories and 12 indicators of the framework into 48 to 54 sub388 indicators and 130 to 150 variables or metrics.
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389 10.4.8. NOAA Coastal Resilience Index

390 The National Oceanic and Atmospheric Administration‘s Coastal Resilience Index (NOAA CRI 2010)

391 was developed to provide a simple and inexpensive self-assessment tool to give community leaders a 

392 method of predicting if their community will reach and maintain an acceptable level of functioning after a 

393 disaster. The tool is completed by experienced local planners, engineers, floodplain managers and 

394 administrators in less than three hours using readily available, existing sources of information, in a yes/no 

395 question format. 

396 The CRI is targeted primarily at coastal storms, particularly hurricanes and other surge or rain induced 

397 flooding events with immediate and short-term recovery. More specifically, it focuses on the restoration 

398 of basic services and how long a community will take to reach and maintain functioning systems after a 

399 disaster. The eight page assessment form addresses six broad areas: 

400 1. Critical facilities and infrastructure

401 2. Transportation issues

402 3. Community plans and agreements

403 4. Mitigation measures

404 5. Business plans

405 6. Social systems

406 The resulting assessment is meant to identify problems (vulnerabilities) that should be addressed before 

407 the next disaster – areas in which a community should become more resilient and where resources should 

408 be allocated. It also estimates the adaptability of a community to a disaster, but is not meant to replace a 

409 detailed study. The authors note that ―The Resilience Index and methodology does not replace a detailed 

410 study.... But, the Resilience Index resulting from this Community Self-Assessment may encourage your 

411 community to seek further consultation.‖

412 The authors also state that the tool should not be used to compare one community to another. Rather, they 

413 recommend using it as an approach to internal evaluation to identify areas in which a given community 

414 might increase its resilience. As part of its development process the NOAA Community Resilience Index 

415 (CRI) was pilot tested in 17 communities in five states (Alabama, Florida, Louisiana, Mississippi, and 

416 Texas). In addition to developing their community indices, these pilot tests were also used to further 

417 refine and improve the assessment methodology.

418 10.4.9. FEMA Hazus Methodology

419 The Federal Emergency Management Agency‘s Hazus tool (FEMA 2014) ―is a nationally applicable 

420 standardized methodology that contains models for estimating potential losses from earthquakes, floods 

421 and hurricanes. Hazus uses Geographic Information Systems (GIS) technology to estimate physical, 

422 economic and social impacts of disasters. It graphically illustrates the limits of identified high-risk 

423 locations due to earthquake, hurricane and floods. Users can visualize the spatial relationships between 

424 populations and other fixed geographic assets or resources for the specific hazard being modeled – a 

425 crucial function in the pre-disaster planning process.‖

426 The Hazus methodology and data sets cover the entire United States, and the study region (i.e., 

427 community) can be defined as any combination of US Census tracts. The specific hazard models included 

428 are earthquake (including fire following), flood (riverine or coastal) and hurricane (wind and storm 

429 surge). The focus of the model is on immediate physical, economic and (to a lesser degree) social 

430 impacts. But, the model does produce outputs on expected loss of use for buildings, loss of use for 

431 infrastructure (earthquake and flood only), shelter requirements, casualties (earthquake only), building 

432 contents and inventory losses, lost wages and income and indirect economic losses (earthquake and flood 

433 only). Estimated repair times are explicitly considered in economic loss estimates produced by the model, 

434 but the economic outputs are not tabulated or viewable as a function of time. While Hazus can be used to 
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435 assess losses avoided through some mitigation measures, it does not estimate mitigation costs and 

436 therefore does not output estimates of return on investment.

437 There are gaps between the results produced by Hazus and the information required for a community438 level resilience assessment methodology, particularly in the areas of interdependencies, social impacts 

439 and recovery times. However, many of the Hazus methodologies and the types of results they produce 

440 could become portions of a larger framework.

441 10.4.10. Comparison Matrix

442 A summary comparison of the nine example methodologies discussed in the preceding sections is 

443 provided in Figure 10-1. As noted earlier, not all of these methodologies address community resilience, 

444 but were evaluated to identify relevant and potentially applicable methods, indicators, or processes.

445 Each methodology was assessed on five broad dimensions: (1) comprehensiveness, (2) utility, (3) impacts

446 assessed, (4) techniques used, and (5) overall merit with respect to the maturity, innovativeness, 

447 objectivity, and scientific merit of the methodology. Assessments were made in the context of community 

448 resilience planning and assessment, specifically as it pertains to the built environment.

449 Consistent with the findings of previously published assessments, none of the nine methods reviewed is 

450 strong in all five dimensions. However, it may be possible to combine the strongest features of existing 

451 and emerging methodologies to produce a new community resilience assessment methodology that 

452 addresses the needs identified in this chapter.

453
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454

Group Category Sub-Category SPUR Oregon Res. Plan (ORP) UNISDR Scorecard CARRI CRS CART BRIC Rockefeller CRF & CRI NOAA CRI FEMA HazusGroup Symbol Description

1 Comprehensiveness Community size ● ● + + + + + ● + 1 + Addresses a broad range

Hazards ● ● + + + + + - - ● Focused subset, but not inherently limited 

Recovery time scales + + ? ? ? ? + ● - - Limitation

Systems + + ? + - - + ● ● ? Additional information required

Interdependencies ● ● ? +? - - +? - -

2 Utility User friendliness ● ● + + + + ● + ● 2 + High

Utility without hired or volunteer SMEs - - + ●? ●? ●? ● ●? ●? ● Moderate

Value of outputs for resilience planning + + ● ? ? ? +? ● ●? - Low

Consistency with PPD-21 + + ● + + ● ● ● - ? Additional information required

3 Impacts assessed Physical impacts and recovery times + + ● ● ● ● ● ● ● 3 + Explicitly assessed

Economic impacts and recovery times ● +? ● ● ● ● +? - ● ● Partially or indirectly assessed

Social impacts and recovery times ● ● ● ● ● ● +? ● ● - Not assessed

? Additional information required

4 Techniques used Checklists - - + + + - + + ● 4 + Yes

Interviews, Surveys - - - ● + - + ● ● ● Optional

Ratings + + + ● + - + ● + - N o

Existing national data sets - - - - - + - - + ? Additional information required

Physical inspections ● ● ● ● - - - ● ●

Engineering analysis or expert opinion + + ● ● - - - ● +

Statistical inference ● ● - ● - - - - +

Simulations ● ● - ● - - - - +

5 Critical Assessment Maturity + + ● + - + ● ? + 5 + Strength

Unique/innovative + ● ● + + + ● - + ● Neither a strength nor a weakness

Objective/repeatable ● ● ● ● ● + +? - + - Weakness

Scientific merit +? +? - ? ? ? +? ? + ? Additional information required

Existing Assessment Methodologies

455

456 Figure 10-1. Preliminary Summary Assessment of Nine Existing Community Resilience Methodologies
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457 10.5. Economic Evaluation of Community Resilience Investment Portfolio 

458 This section presents a brief overview of existing economic concepts related to the evaluation of 

459 investments to improve community resilience. The focus is on the development of a portfolio of 

460 investments that maximize the social net benefits to the community, recognizing constraints, uncertainty, 

461 and interdependencies that affect the mix of investments.

462 10.5.1. Portfolio Considerations

463 10.5.1.1. Economic Efficiency 

464 Economic efficiency refers to obtaining the maximum benefit from the resources available. Equivalently, 

465 it means not wasting resources.

466 10.5.1.1.1. Maximization of Net Benefits 

467 Improved community resilience will also increase the level of service economically. Several alternatives 

468 may maximize the net benefits to the citizens of the local community.

469 This assessment takes into account the fact that improved levels of service are typically more costly. This 

470 type of analysis will identify the level of service where the net benefits (that is, the increased value of the 

471 improved level of service minus the cost of obtaining that level of service) are maximized.

472 10.5.1.1.2. Minimization of Cost + Loss 

473 From an economic perspective, this is an equivalent formulation to maximizing net benefits. Since the 

474 ―Level of Service‖ is defined in terms of minimizing costs and losses, it may be a more convenient format 

475 for analysis. Expressing the results of this analysis in terms of net benefits is straightforward.

476 10.5.1.1.3. First-Cost vs. Life-Cycle Cost

477 Any effort to identify the alternatives that produce a maximization of net benefits depends on accurate 

478 estimates of benefits and costs. With regard to the costs of attaining a desired level of service, all costs, 

479 covering the entire life-cycle of any mitigation measures, need to be accounted for. It is not sufficient to 

480 include first costs only. Operation costs, maintenance costs, replacement costs and end-of-life costs 

481 (among others) need to be included.

482 10.5.1.2. Multiple Objectives

483 There are several complementary (and overlapping) objectives that are likely to be considered, accounting 

484 for the types of losses that a community wishes to avoid. In any analysis of avoided losses, care needs to 

485 be taken to ensure that savings are not double-counted.

486 10.5.1.2.1. Minimize Economic Losses 

487 The simplest consideration is that of minimizing economic losses. Treated in isolation, that simply means 

488 making sure that the difference between economic gain (in terms of losses avoided) and costs of the 

489 desired level of service are maximized. It is simpler than the other considerations because costs and 

490 benefits are both in dollar terms.

491 10.5.1.2.2. Minimize Loss of Life 

492 The remaining objectives all relate to economic losses of one sort or another. The most important 

493 consideration is avoiding loss of life and other casualties.

494 10.5.1.2.3. Minimize Other Losses 

495 Other losses a jurisdiction might wish to avoid include disruption of key government services, disruption 

496 of social networks, and damage to the environment. Including non-economic factors such as these in the 

497 optimization is difficult, as benefits and costs are measured in different terms. If loss of life is included in 
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498 the optimization, the benefits are measured in terms of lives saved (or deaths avoided), while the costs are 

499 typically measured in dollars. The normal economic way of handling this issue is by assigning a value to 

500 the benefits. For lives saved, Value of a Statistical Life is a standard approach. For other benefits, a 

501 number of techniques are available to determine the value a community places on those benefits.

502 However, there is a strong reluctance to put a price on a life (which is nominally what Value of a 

503 Statistical Life does) and other non-economic amenities. As an alternative, some form of Lexicographic 

504 Preferences could be used. Here each objective is strictly ranked, and then optimized in order. For 

505 example, an assessment could optimize for loss of life and then for economic losses. This ranking 

506 approach would ensure the selection of an alternative that minimizes loss of life (irrespective of costs).

507 Next, the minimum cost alternative that maintained the minimum loss of life would be found.

508 Why not choose zero loss of life? As a practical matter, tradeoffs between safety and costs cannot be 

509 avoided.

510 10.5.1.3. Constraints 

511 To the extent a local community has a limited budget, that budget must be factored into the optimization.

512 Other constraints can also be factored in, largely by screening out potential plans that do not meet the 

513 constraints.

514 10.5.1.4. Economic Interdependencies 

515 The economy in general is affected by the resilience of the built environment. The reverse also holds – the 

516 resilience of the community depends on the health and resilience of the economy.

517 10.5.2. Economic Decision-Making Involving Risk and Uncertainty 

518 10.5.2.1. Expected Utility Theory 

519 Economists often approach decision-making with expected utility theory. The basic idea is that people 

520 will choose the alternative that has the best  ̳utility‘ or value for them, as indicated by the highest 

521 probability-weighted average value. The value is adjusted to account for both time preference and risk 

522 preference.

523 10.5.2.1.1. Time Preference 

524 Most people prefer consumption now over consumption later. The typical way to address that is to

525 discount future consumption.

526 10.5.2.1.2. Risk Preferences 

527 Most people would prefer to avoid risk – that is, they are risk averse. For people who are risk averse, a 

528 large potential loss weighs more heavily than a large number of small losses, which together, add up to 

529 the same value as the big event. Someone who is risk neutral would weigh the two equally.

530 Risk aversion is handled in economic theory by weighting the large losses more heavily (or equivalently, 

531 by weighting large gains less heavily). The simplest approach, and the one used most often in net benefit 

532 analyses, is to assume that the community is risk neutral. Then you simply compute the present expected 

533 value. However, when it comes to disasters it seems unlikely that communities will be risk neutral.

534 To account for risk preferences, it will be necessary to measure those risk preferences. A number of 

535 widely-accepted methods for measuring risk preferences exist.

536 10.5.2.2. Behavioral Economics and Cognitive Bias

537 People are not Expected Utility maximizers; there is a very large body of literature regarding departures 

538 from Expected Utility maximization. Expected utility maximization is a difficult problem, and typically,

539 there are not enough resources available to solve it. There are several approaches to thinking about these 

540 departures from economic theory, but the most widely accepted is the Heuristics and Biases school. They 
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541 argue that people use standard shortcuts—heuristics—that work well most of the time. However, there 

542 will be cases where they do not work well, and in those situations they will be biased. The biases are 

543 generally used to try and identify the heuristics used.

544 There are a number of identified biases, some of which are relevant here. These include Uncertainty v. 

545 risk, overconfidence, and small probability events, among others.

546 10.5.2.3. Uncertainties

547 Uncertainties regarding estimates of expected damages and recovery times from disasters fall into two 

548 categories. First, there are factors that cannot be known with certainty in advance, such as the timing and 

549 magnitude of future hazard events. Second, there are things that are in principal knowable, but are not 

550 currently known with certainty. For example, while in principal the cost of a particular project can be 

551 estimated, the level of uncertainty associated with the estimate can vary and will likely increase with the 

552 scope of the project.

553 Mitigation costs, recovery costs, and losses will have uncertainties in their estimates. As community 

554 resilience plans are developed and refined, the level of uncertainty may reduce.

555 A particularly high level of uncertainty exists regarding business interruption losses. In cases where they 

556 have been estimated, such losses are often as large or larger than direct economic losses. However, they 

557 are difficult to estimate, due to the lack of data from past events to support estimates.

558 10.6. References

559 CERA (2014). Canterbury Wellbeing Index June 2014, Canterbury Earthquake Recovery Authority, 

560 Christchurch, New Zealand.

561 Cutter, S.L. (2014). The Landscape of Resilience Measures. Presentation at the Resilient America 

562 Roundtable Workshop on Measures of Community Resilience, September 5, 2014. Viewed 

563 January 25, 2015.

564 <http://sites.nationalacademies.org/cs/groups/pgasite/documents/webpage/pga_152239.pdf>

565 Government Office for Science (2012). Foresight – Reducing Risks of Future Disasters: Priorities for 

566 Decision Makers, Final Project Report, London. Viewed January 25, 2015. 

567 <https://www.gov.uk/government/publications/reducing-risk-of-future-disasters-priorities-for568 decision-makers>

569 National Academies (2012a). Disaster Resilience: A National Imperative – Summary, Committee on 

570 Increasing National Resilience to Hazards and Disasters and Committee on Science, Engineering, 

571 and Public Policy. Viewed January 25, 2015.

572 <www.nap.edu/html/13457/13457_summary.pdf>

573 National Academies (2012b). Disaster Resilience: A National Imperative, Committee on Increasing 

574 National Resilience to Hazards and Disasters and Committee on Science, Engineering, and Public 

575 Policy. Viewed January 25, 2015.

576 < http://www.nap.edu/catalog.php?record_id=13457>

577 UNISDR (2005). Hyogo Framework for Action 2005-2015: Building the Resilience of Nations and 

578 Communities to Disasters, World Conference on Disaster Reduction 18-22 January 2005, Kobe, 

579 Hyogo, Japan. Viewed January 25, 2015.

580 <http://www.unisdr.org/2005/wcdr/intergover/official-doc/L-docs/Hyogo-framework-for-action581 english.pdf>

582 UNISDR (2012). Making Cities Resilient Report 2012: My City is Getting Ready! A Global Snapshot of 

583 How Local Governments Reduce Disaster Risk, Second Edition, October 2012. Viewed January 

584 25, 2015. 

585 <http://www.unisdr.org/we/inform/publications/28240>
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586 White House (2013). Presidential Policy Directive/PPD-21: Critical Infrastructure Security and 

587 Resilience, Office of the Press Secretary, February 12, 2013. Viewed January 26, 2015.

588 <http://www.whitehouse.gov/the-press-office/2013/02/12/presidential-policy-directive-critical589 infrastructure-security-and-resil> 

590 References for methodologies and metrics discussed in Sections 10.4

591 SPUR: 

592 <http://www.spur.org/sites/default/files/publications_pdfs/SPUR_Seismic_Mitigation_Policies.pd

593 f>

594 Oregon: 

595 <http://www.oregon.gov/OMD/OEM/osspac/docs/Oregon_Resilience_Plan_Final.pdf>

596 UNISDR Scorecard: 

597 <http://www.unisdr.org/2014/campaign-cities/Resilience%20Scorecard%20V1.5.pdf>

598 CARRI CRS: 

599 <http://www.resilientus.org/wp-content/uploads/2013/05/CRS-Final-Report.pdf>

600

601 CART:

602 <http://www.oumedicine.com/docs/ad-psychiatry-workfiles/cart_online603 final_042012.pdf?sfvrsn=2>

604 BRIC: S Cutter, K Ash, and C Emrich (2014). See ―The geographies of community disaster resilience.‖

605 Viewed December 9, 2019. 

606 <http://www.sciencedirect.com/science/article/pii/S0959378014001459>

607 Rockefeller Foundation CRF: 

608 <http://www.rockefellerfoundation.org/uploads/files/0bb537c0-d872-467f-9470-

609 b20f57c32488.pdf>

610 NOAA Coastal Resilience Index: 

611 <http://masgc.org/assets/uploads/publications/662/coastalcommunity

612 _resilience_index.pdf>

613 FEMA Hazus Methodology: 

614 <https://msc.fema.gov/portal/resources/hazus>