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"(We) look at trends in the New
Orleans area across 3 decades to get the entire
view of the health and vitality of the city as a
measure of the city's resilience..."
Allison Plyer, Greater New Orleans
Community Data Center, January 20, 2011
4
Measuring Progress Toward Resilience
THE NEED FOR METRICS AND INDICATORS
The committee recognized early on in its discussions that the study's
focus on improving resilience necessitates measurement, a position also
indicated in the study's Statement of Task (see Chapter 1). Measurement is
essential for several reasons. First, it would be impossible to identify the
priority needs for improvement without some numerical means of assessment.
Second, a system of measurement is essential if progress is to be monitored.
Third, any effort to compare the benefits of increasing resilience with the
associated costs requires a basis of measurement. Establishing a baseline or
reference point from which changes in resilience can be measured, combined
with a regular system of monitoring to track changes through time, is also
necessary. However, the measurement of a hard-to-define concept is necessarily
difficult, requiring not only an agreed-upon metric, but also the data and
algorithms needed to compute it. Resilience also includes human (social) and
physical (infrastructure, natural environment) components that add complexity
and challenges in finding metrics that cover this range of factors. This chapter
discusses some of the more important principles and issues connected with
measuring resilience. It examines the available methods, data, and tools, and
makes recommendations designed to implement one type of measuring system
for resilience.
One national-scale metric of resilience could be the dollar amount (per
capita) of federal assistance spent annually for disasters, with the measure for
resilience being whether this dollar amount flattens or declines (potentially
indicating increasing resilience) or continues its steady growth (potentially
indicating that resilience is not increasing, or is not increasing at a significant
rate nationally). While imperfect, such an indicator provides a valuable
91
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92 DISASTER RESILIENCE: A NATIONAL IMPERATIVE
synoptic, national picture, but other metrics would be required to measure the
progress of individual communities.
Metrics are an important tool of administration. They allow targets to
be established and set clear goals for improvement. The very act of defining a
metric, and the discussions that ensue about its structure, help a community to
clarify and formalize what it means by an abstract concept, thereby raising the
quality of debate. The general concept of resilience is one with which most
people are familiar, but resilience is not something that communities have much
experience in measuring. Resilience is also clearly influenced by multiple
factors, making precise measurement very difficult. This immediately suggests a
strategy of combining various factors, using appropriate weights, into a
composite index. The set of factors, how they are measured, the weights given to
each factor, and the operations used to combine them into a composite index all
present issues that can be the subject of lengthy debates and contention. At the
same time, the translation of an abstract concept into a rigorous procedure for
measurement--the formalization of the concept--allows for monitoring, the
comparison of progress in different communities, and the prioritization of
actions and investments, all of which can be extremely helpful. The effects of
actions and policy changes can then be monitored through time to produce more
desirable outcomes in the future by comparing improvements in resilience that
result from those actions to what was promised or predicted, iteratively
modifying actions and policies, and perhaps recalibrating metrics.
To be useful in this context, a resilience metric needs to be open and
transparent, so that all members of a community understand how it was
constructed and computed. It needs to be replicable, providing sufficient detail
of the method of determination of a community's resilience so that it can be
checked by anyone using the same data. It must also be well documented and
simple enough to be used by a wide range of stakeholders.
Metrics may be quantitative, but metrics with no more than ordinal
properties still allow resilience to be ranked and progress to be monitored. For
example, a metric might set the qualitative levels "unsatisfactory," "marginal,"
and "satisfactory" resilience, without specifying quantitative measures or ranges
for each level, as long as the procedure for arriving at a rating was open,
transparent, and replicable. A scale similar to those used in academic report
cards with designations of A, B, C, D, or F could also be used to indicate
progress. In recent years, much of this process of defining a metric has been the
subject of extensive research, often under the rubric of multicriteria decision
making (MCDM). Many of these methods have been devised for problems
embedded in geographic space, such as the selection of a site for a new public
facility, or of a route for a new highway. The geospatial nature of such problems
raises additional issues such as estimating environmental, social, and economic
impacts of site selection for the new development and the way in which the
necessary data to gauge these impacts can be incorporated into a collective
planning process, as several texts make clear (see, e.g., Massam, 1993;
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Measuring Progress Toward Resilience 93
Malczewski, 2010). The methods deal effectively with the disparate views of
stakeholders, allowing consensus to emerge and measuring the degree to which
consensus exists. For example, the analytical hierarchy process (Saaty, 1988) is
a much-applied method for reconciling divergent views in the creation of a
consensus metric.
Many of these principles are illustrated by the well-known LEED
(Leadership in Energy and Environmental Design; Box 4.1) process, released by
the U.S. Green Building Council in March 2000. By providing an open forum
for the measurement of environmental sustainability of buildings, LEED has
provided an important tool for promoting and achieving energy efficiency.
LEED was a bottom-up initiative without any initial endorsement from
government agencies. It has gained popularity in engineering and architectural
design as an added value to building occupants and to the environment in
general. It has also become a trademark of socially conscious organizations in
the private sector. The committee was struck by the impact LEED has had and
seeks to emulate its success by envisioning a similar strategy for the
measurement of resilience, laid out in the final section of this chapter.
BOX 4.1
Leadership in Energy and Environmental Design
LEED, or Leadership in Energy and Environmental Design, is an
internationally recognized green-building certification system. Developed by
the U.S. Green Building Council (USGBC) in March 2000, LEED is a
framework for building owners and operators that allows identification and
implementation of green building design, construction, operations, and
maintenance.
LEED promotes sustainable building and development practices through a
set of rating systems that recognize building projects that have adopted
strategies for better environmental and health performance. The LEED rating
systems are developed through an open, consensus-based process led by LEED
committees comprising groups of volunteers from across the building and
construction industry. Key elements of the process of developing LEED rating
systems include a balanced, transparent committee structure, technical advisory
groups for scientific consistency and rigor, opportunities for stakeholder
comment, member ballot of new rating systems, and fair and open appeals.
LEED can apply to all building types, whether commercial or residential.
LEED works throughout the building life cycle from design and construction
through to tenant fitout and retrofit. LEED for Neighborhood Development is
designed to allow the benefits of LEED to extend beyond a single building and
into the neighborhood it serves.
SOURCE: http://www.usgbc.org/DisplayPage.aspx?CMSPageID=1988.
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94 DISASTER RESILIENCE: A NATIONAL IMPERATIVE
While LEED focuses primarily on buildings, the thrust of this chapter's
discussion is on the resilience of communities and their complexities. For
example, a metric of the overall resilience of an entire city may mask substantial
variations within the city. Carried to an extreme, we might conceive of resilience
as varying continuously over the Earth's surface, similar to the way elevation
varies, and scale-dependent in both space and time. Moreover, resilience is a
function of many factors, not all of which may be the same for all people, even
when those people occupy the same location.
Problems such as these are familiar to geographers and others who
work with geospatial data, and are commonly termed the Modifiable Areal Unit
Problem (see, e.g., Longley et al., 2011). Such problems arise when the results
of an analysis, such as the measurement of resilience, depend on the areas used
for the analysis. We might find, for example, that neighborhoods in some areas
of New Orleans are substantially more resilient than other neighborhoods and
that New Orleans as a whole has a resilience in the middle of the range, when
compared with other places. By selectively lumping neighborhoods together, in
other words, by modifying the areal units in a process similar to gerrymandering
electoral districts, one could produce a map that sharply and misleadingly
contrasts highly resilient areas and much less resilient areas.
The committee recognized the need to address this problem in any
recommended system of measurement. The key is the concept of community,
and its requirements of self-identification and mutual affinity, allowing a
community, its members, and its boundary to be treated as an existing, well-
defined area. In this sense a neighborhood, a town, or an entire city might all
qualify as communities; and a community need not be formally recognized as an
administrative unit, or precisely defined by a boundary on the Earth's surface.
Any individual might belong to more than one community, each with its own
measurement of resilience; a New Orleans resident might live in a highly
resilient neighborhood, but in a city of relatively low resilience. With this
principle as its foundation, and no possibility of arbitrary or selective
gerrymandering, the process of measurement of community resilience becomes
much more straightforward. Essentially, and recalling a long-recognized duality
in geography and related disciplines (e.g., Tuan, 2007), resilience needs to be
addressed by reference to place and not space.
MEASURES OF U.S. NATIONAL RESILIENCE
Many organizations have tackled the problem of measuring resilience,
or its close relative vulnerability, for the United States. This section reviews
many of these efforts, choosing specific representative examples for detailed
discussion.
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Measuring Progress Toward Resilience 95
Coastal Resilience Index
The Coastal Resilience Index, cosponsored by the Louisiana Sea Grant,
Mississippi-Alabama Sea Grant Consortium, and the National Oceanic and
Atmospheric Administration Gulf Coast Services Center (Emmer et al., 2008),
provides an example of a community-based approach to developing an index of
resilience to storm events through self-assessment. It adapts the principles
outlined by FEMA (2001) to the specific needs of coastal hazards and
operationalizes them into an ordinal metric.
The community is first asked to identify two scenarios from memory: a
"bad storm" and a "worst storm." Critical infrastructure and facilities are then
evaluated: Were they impacted in either or both of the scenarios, and were they
functioning afterward? Critical infrastructure includes the wastewater treatment
system, the power grid, the water purification system, and
transportation/evacuation routes. Critical facilities include government
buildings, law enforcement buildings, fire stations, communication offices, the
emergency operations center, evacuation shelters, hospitals, and critical record
storage. The community is encouraged to expand these lists as appropriate. The
numbers of critical infrastructure elements and critical facilities that continued to
function after the scenarios are then totaled.
In the next step, the community is asked to assess whether various
elements of its transportation system will be restored within 1 week after a "bad
storm," and again to total the number of such elements. The third step asks for
information on the participation of the community in various plans and
agreements, and whether it has key personnel in place with responsibility for
disaster-related matters. The number of positive responses is counted. Step 4
yields a total for ongoing mitigation measures, Step 5 addresses business plans
for the recovery of retail stores, and Step 6 asks about social networks and civic
organizations.
The totals in each step are next transformed to Low, Medium, and High
categories based on specified ranges--for example, to gain a High rating on
critical infrastructure the community must have agreed that 100 percent of its
elements would be functioning after a disaster. No weights are applied to each
element; rather, the community is asked simply to count. The result is a total of
seven metrics (two from Step 1 and one from each of the subsequent steps). The
community is advised to treat these as separate indicators and not to attempt to
combine them into a single metric.
The Low, Medium, and High resilience ratings are then converted into
an overall state-of-the-community resilience for a specific category, along with
some estimate of the time it would take for reoccupation of the community after
the disaster: more than 18 months for a Low rating; less than 2 months for a
Medium rating; and minimal impact for a High rating.
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96 DISASTER RESILIENCE: A NATIONAL IMPERATIVE
Argonne National Laboratory Resilience Index
A very different approach to measuring the resilience of critical
infrastructure is described by Fisher et al. (2010), the result of a project
conducted by Argonne National Laboratory in collaboration with the U.S.
Department of Homeland Security's Protective Security Coordination Division.
Data are gathered at critical infrastructure facilities by trained interviewers
known as Protective Security Advisors (PSAs). The interviews use an
Infrastructure Survey Tool covering roughly 1,500 variables that cover six major
physical and human components (physical security, security management,
security force, information sharing, protective measures assessment, and
dependencies) that are themselves broken down into 42 components. The
approach is used for one or several types of critical infrastructure or key
resource sector (banking and finance, dams, energy, etc.). Data are subjected to
an elaborate, six-step process of quality control involving review by experts in
critical infrastructure protection.
A five-stage aggregation process is then used to combine the items into
a single Resilience Index (called the Protective Measure Index PMI) that ranges
from 0 (lowest resilience) to 100 (highest resilience) for a given critical
infrastructure or key resource sector and for a given threat. Each of the stages
takes a subset of items at that stage and combines them using weights to obtain a
single index for the next stage. From roughly 1,500 items at Level 5, this
process results in 47 composite scores at Level 2, three at Level 1, and finally a
single score. At Level 2, 18 of the 47 measures contribute to Robustness at
Level 1, five to Recovery at Level 1, and 24 to Resourcefulness at Level 1. At
each stage, every contributing measure is multiplied by a weight, and the
products are summed to obtain the PMI composite index. Weights are obtained
by analyzing the opinions of experts, using the MCDM methods of Keeney and
Raiffa (1976). PMI ratings by sector (e.g., commercial facilities, energy,
transportation, water) may help in identifying the infrastructure facility that is
weakest in relation to one or several threats.
In contrast to the bottom-up elements of the Coastal Resilience Index,
this approach is almost entirely top down, reflecting the need of a national
program to be uniform and universal in its approach. There is no possibility of
adaptation to local needs, by modifying either the set of data items or the
weights, both of which are prescribed. The index is entirely concerned with
critical infrastructure, such a narrow focus being more conducive to a rigorous,
quantitative approach. Nevertheless, justifying universal weights resolved to
three decimal places is difficult given the inherent vagueness of the concept of
resilience and its essential components, and uncertainties over the exact nature
of threats.
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Measuring Progress Toward Resilience 97
Social Vulnerability Index (SoVIŽ)
Social vulnerability is the susceptibility of a population to harm from a
natural hazard and examines those characteristics of the population that
influence their resilience. Vulnerable populations may be less resilient to
hazards and disasters than other parts of the population, may need special
assistance in preparing for, responding to, and recovering from disasters, and
may be more susceptible to economic or other impacts from an event. Social
vulnerability is place-based and context-specific, and helps explain why some
portions of the country or community experience a hazard differently, despite
having the same exposure. Income is but one variable that is often associated
with vulnerable populations, and income levels clearly vary by race and
ethnicity (Figure 4.1). Other vulnerable populations may include special-needs
populations such as residents with physical or mental impairments, the elderly,
the young, and those with limited access to transportation (see also Chapter 5).
FIGURE 4.1 Trends in median household income in the United States. Data show income level
variations by race and ethnicity. Source: U.S. Census Bureau.
Social vulnerability helps us to understand the inequalities in disaster
impacts and is a multiattribute concept that includes socioeconomic status, race
and ethnicity, gender, age, housing tenure, and so forth and how these factors
influence a community's resilience (Mileti, 1999; Heinz Center, 2002; NRC,
2006). Social vulnerability can change over time and across space (Cutter and
Finch, 2008) and can be measured both qualitatively and quantitatively
(Birkmann, 2006; Phillips et al., 2010).
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98 DISASTER RESILIENCE: A NATIONAL IMPERATIVE
Social vulnerability metrics are increasing in sophistication and usage
in both research and practice. Among the best known is the Social Vulnerability
Index (SoVIŽ), a metric that permits comparisons of places (block groups,
census tracts, metropolitan areas, counties) (Cutter et al., 2003; Box 4.2).
Mapping SoVIŽ scores illustrates the extremes of social vulnerability--those
places with very high values (the most vulnerable), and those with relatively low
values (the least vulnerable) (Figure 4.2). SoVIŽ captures the multidimensional
nature of social vulnerability--vulnerability that exists prior to any hazard or
disaster event. In addition to describing the relative level of social vulnerability,
the metric also enables the examination of those underlying dimensions that are
contributing to the overall score such as age disparities, socioeconomic status,
employment, and special-needs populations.
FIGURE 4.2 Social Vulnerability Index, 2006-2010. Areas in red denote higher levels of social
vulnerability relative to other counties, whereas counties in blue show lower levels of social
vulnerability. Mapping by standard deviations (represented here as top and bottom 20 percent)
shows the extremes of the distribution, which is of greatest interest. HVRI = Hazard and
Vulnerability Research Institute.
Source: S. Cutter/HVRI.
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Measuring Progress Toward Resilience 99
BOX 4.2
The Social Vulnerability Index (SoVIŽ)
SoVIŽ is a statistically derived comparative metric to
illustrate the variability in capacity for preparedness, response,
and recovery at county and subcounty levels of geography.
Using census data, SoVIŽ synthesizes 32 different variables,
using a principal components analysis and expert judgment,
into a single composite value, which is then mapped to
illustrate differences between places. Several factors
consistently appear in the results of these analyses, including
socioeconomic status, elderly, and gender; however, the
relative importance of these factors is observed to be place
specific. Since its inception, SoVIŽ has been used by
emergency planners as part of their state hazard mitigation
planning (South Carolina, California, and Colorado) and has
been incorporated into a number of digital products including
the National Oceanic and Atmospheric Administration's
Coastal Services Digital Coast.
(http://www.csc.noaa.gov/digitalcoast/tools/slrviewer/index.html). See
http://sovius.org for more details and applications.
Baseline Resilience Indicator for Communities
A new composite indicator called the Baseline Resilience Indicator for
Communities (BRIC) was introduced to measure community resiliency (Cutter
et al., 2010). BRIC acknowledges that resilience is a multifaceted concept with
social, economic, institutional, infrastructural, ecological, and community
components. The composite indicator is calculated as the arithmetic mean of
five subindexes related to social, economic, institutional, infrastructural, and
community resilience; ecological resilience is not included in the 2010
formulation. Each subindex is normalized so that the final indicator varies
between 0 and 1.
Cutter et al. (2010) proposed several applications of the proposed
method to communities at different scales. An interesting case study relates to
the spatial distribution of disaster resilience over 736 counties within FEMA
Region IV (Figure 4.3). A second example deals with determining the resilience
score of three metropolitan areas: Gulfport-Biloxi, Charleston, and Memphis.
Both case studies show a clear ability to identify least-resilient areas at different
geographic scales using an empirically based descriptive approach.
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100 DISASTER RESILIENCE: A NATIONAL IMPERATIVE
FIGURE 4.3 Spatial resolution of disaster resilience for FEMA Region IV. Source: S.
Cutter/HRVI.
SPUR Model
The San Francisco Planning and Urban Research Association (SPUR)
developed a set of metrics for measuring the resilience of the Bay Area with
respect to earthquakes (SPUR, 2008). The process begins with the definition of
an "expected earthquake," defined as one "that can reasonably be expected to
occur once during the useful life of a structure or system," and in operation is
one with a 10 percent probability of occurrence in a 50-year period. In the SPUR
methodology, specific recovery objectives are defined in distinct time frames
(Table 4.1): hours (3 to 72), days (30 to 60), and months (4 to 36). These target
states of recovery and their time frames include those for hospitals, police and
fire, the emergency operations center, transportation systems and utilities,
airports, and neighborhood retail businesses, offices, and workplaces. Five
categories of performance are defined for buildings ranging from A (safe and
operational) to E (unsafe). Significantly, the goal for San Francisco was to have
95 percent of residents sheltering in place with 24 hours, requiring Category B
performance for buildings. Although not all utilities might be functioning
within 24 hours, the goal was to keep citizens in their homes and in their
neighborhoods. The table provides the target states of recovery for San
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Measuring Progress Toward Resilience 101
Francisco's buildings and infrastructure together with an assessment of the
current status for each of 31 distinct criteria. The gap between desired
performance and current status highlights which areas need most work. No
attempt is made in the model to collapse the criteria into a single metric. This
approach provides a useful template that could be applied to an entire city, or to
any neighborhood or community for use in defining their critical criteria for
recovery, creating a timeline using performance objectives to achieve it, all in
support of longer-term resilience goals.
TABLE 4.1 SPUR Model of Measuring Recovery from Earthquakes
Note: The table provides a useful template for identifying critical areas for recovery, which could
provide the basis for establishing resilience goals. Source: C. Poland/SPUR.
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Table 4.3 Tasks Defined by HFA to Make Risk Reduction a National and Local Priority--HFA Priority 1
Making Risk Reduction a National and Local/City Priority with a
Strong Institutional Basis for Implementation
HFA Tasks Local National HFA Guiding Questions Tools
Indicators Monitor
Indicators
Task 1 ˇ A local/city A. National ˇ Are different stakeholders engages in a ˇ Multistakeholder
Engage in multisectoral multisectoral continuing dialogue for disaster risk dialogues;
multistakehold platform for platform for reduction? management
er dialogue to disaster risk disaster risk ˇ Is there political consensus on importance of information system
establish reduction is reduction is DRR?
foundations for functioning operational ˇ What is the degree of participation of civil
disaster risk ˇ Political society in DRR?
reduction commitment ˇ Is local/city government supportive to a
(DRR) community vision for DRR?
Task 2 ˇ Community B. Community ˇ Are community participation and ˇ Stakeholder
Create or participation participation and decentralization ensured through the engagement
strengthen and decentralization delegation of authority and resources to the mechanisms; local
mechanisms decentralized are ensured local/city level? platform for DRR
for systematic functions are through the ˇ Is there official policy and strategy to
coordination ensured delegation of support community-based disaster risk
for DRR throughout authority and management in the city?
the local resources to local ˇ Are communities empowered to participate
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authority levels in disaster risk reduction?
ˇ Are city offices aware of their respective
roles in reduction?
ˇ Are there committed and effective
community outreach activities (DRR and
related services, e.g. healthcare?)
Task 3 ˇ Policy C. A legal ˇ Is responsibility for DRR planning and ˇ Development plan;
Assess and instruments framework for implementation devolved to city land use plan; physical
develop the and tools to disaster risk government and communities? plan
institutional support reduction exists ˇ Are city government and communities ˇ Budget allocation for
basis for national with explicit equipped with human, financial, and DRR
disaster risk institutional responsibilities organizational capacities/resources? ˇ Disaster management
reduction and legal defined for all ˇ Are city government DRR policies, ordinance; building
frameworks levels of strategies, and implementation plans in code; fire code; zoning
ˇ Legal and government. place? ordinance
regulatory ˇ Are there relevant and enabling legislation ˇ Specific ordinances
system D. A national (ordinance), land use regulations, building
policy framework codes, etc. addressing and supporting DRR
for disaster risk at the local level?
reduction exists ˇ Are thre mechanisms for compliance and
that requires plans enforcement of laws, regulations, building
and activities at codes, etc., and penalties for non-
all administrative compliance defined by laws and
levels, from regulations?
national to local ˇ Is DRR integrated into planning at the
levels local/city level in key sectors such as
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agriculture, climate change, education,
environment, health, housing, poverty
alleviation, and social welfare?
ˇ Are the roles and responsibilities for disaster
risk reduction clearly designated?
ˇ Is the legal and regulatory system
underpinned by guarantees of relevant rights
to safety, to equitable assistance, to be
listened to and consulted?
Task 4 ˇ Dedicated E. Dedicated and ˇ Are there institutional capacities for DRR at ˇ Disaster risk
Prioritize DRR and adequate adequate the local/city level? management office;
and allocate resources are resources are ˇ Is budget allocated to local/city covernment disaster coordinating
appropriate available to available to and other local institutions adequate to council
resources implement implement DRR enable DRR to be integrated into planning
DRR plans at all and actual activities?
activities administrative ˇ Are financial resources available to build
within the levels partnerships with civil society for DRR?
local ˇ Are there logistical, and other such
authority resources allocated for DRR?
ˇ Does the government provide training in
DRR to local/city officials and community
leaders?
ˇ Is a system of accountability in place,
including transparency in the conduct of
DRR and use of funds?
Source: R. Shaw and Y. Matasuoko, UNISDR
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Measuring Progress Toward Resilience 109
Other International Resilience Metrics and Indicators
Other international metrics and indicators for vulnerability, risk, and
resilience have also been developed. Table 4.4 provides a brief summary of
some of these.
Table 4.4 Selected Summary of International Metrics and Indicators for
Vulnerability, Risk, and Resilience
United Nations The DRI, introduced in 2004, measures the average risk of
Development death per country in three types of disasters (earthquakes,
Programme tropical cyclones, and floods). It is a measure of
(UNDP) Disaster vulnerability to a specific hazard that also accounts for the
Risk Index (DRI) role of sociotechnical-humanistic and environmental issues
that could be correlated with death and may point toward
causal processes of disaster risks. The key steps in
determining the DRI for a specific hazard include
calculation of physical exposure in terms of number of
people exposed to a hazard event in a given year;
calculation of relative vulnerability in terms of number of
people killed to number of people exposed; and calculation
of vulnerability indicators using 26 variables. Based on the
value of the DRI, and for a given specific hazard, countries
are ranked according to their degree of physical exposure,
relative vulnerability, and degree of risk (UNDP, 2004;
Peduzzi et al., 2009).
Inter-American The DDI, introduced in 2005, is an indicator of a country's
Development economic vulnerability to disaster. It is limited to Latin
Bank Disaster America and the Caribbean. DDI is a measure of the likely
Deficit Index economic loss related to a disaster in a given time period
(DDI) and for the economic coping capacity of the country (IDB,
2007).
Inter-Agency Established in 1994, the IASC was created to be the
Standing primary mechanism for interagency coordination of
Committee humanitarian assistance at the international level. It is
(IASC) In- composed of representatives of all 14 leading UN
Country Team agencies, non-UN humanitarian agencies, and three
Self-Assessment consortia of nongovernmental organizations. The In-
Tool for Natural Country Team Self-Assessment Tool for Natural Disaster
Disaster Response Response Preparedness consists of a support chart and a
Preparedness checklist of issues and questions to self-assess the level of
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110 Disaster Resilience: A National Imperative
international standards. It also provides resources to
address key concerns and propriety areas for disaster
preparedness and response. See
http://www.humanitarianinfo.org/iasc/.
United Nations The World Risk Index, introduced in 2011 (UNU, 2011),
University indicates the probability that a country or region will be
Institute for affected by an extreme natural event (earthquakes, storms,
Environment and floods, droughts, and sea-level rise). It also focuses on (i)
Human Security, the vulnerability of the population (levels of poverty,
World Risk Index education, food security, infrastructure, economic
framework) to natural hazards, (ii) its capacity to cope with
severe and immediate disasters as a function of
governance, disaster preparedness, early warning systems,
medical services, and social and economic security, and
(iii) its adaptive precautionary measures against anticipated
future natural disasters. The World Risk Index is also
combined with local and project risk indexes.
THE COMMITTEE'S PERSPECTIVE
The preceding two sections have presented representative approaches
to the measurement of resilience. They vary on many dimensions: top-down
prescriptions versus community-based consensus; universal or adaptable, based
on available data or requiring extensive data gathering; place-based or spatial,
and focused on specific hazards and vulnerabilities or extensible depending on
the context. This section introduces the committee's perspective, comments on
each of these dimensions as they might apply to the committee's charge, and
then moves to a discussion of the implementation of metrics.
First, the committee visited three different areas--New Orleans and the
Mississippi Gulf Coast, Iowa, and Southern California--and recognized the
degree to which community concerns vary. New Orleans was recovering from a
major storm event and Iowa from a major flood event, whereas Southern
California has a history of disastrous wildfires and landslides and must prepare
for a future major earthquake event. In the committee's view, therefore, any
approach to measuring resilience has to address multiple hazards, and has to be
adaptable to the needs of specific communities and the hazards they face. By
contrast, the SPUR model (see earlier section) concerns only earthquake hazard,
though it could perhaps be generalized to other hazards.
Second, the committee met with communities of many sizes, from
those in the greater metropolitan areas of Southern California to the small towns
of the Mississippi Gulf Coast. It is clear that any approach the committee
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Measuring Progress Toward Resilience 111
recommends must be place-based rather than spatial, in the meaning of those
terms defined at the start of the chapter, and capable of dealing with a range of
community sizes. Moreover some communities, such as the Lower Ninth Ward
of New Orleans, will be very different in structure, spatial extent, and level of
social organization than others. Again, the emphasis in the committee's
approach to measuring resilience is on adaptability. This concern for
community, place, and adaptability argues against any universal solution, such
as that represented by the Argonne National Laboratory Resilience Index.
Third, the committee recognizes that many dimensions must contribute
to an index, from the physical resilience of the built and natural environment and
critical infrastructure to aspects of human/social resilience such as the existence
of strong social networks, a strong economic base, or good governance. The
examples that yield a single index--SoVIŽ, BRIC, and the Argonne National
Laboratory Resilience Index--all focus on a single dimension, social
vulnerability in the first case, community resilience in the second, and critical
infrastructure in the third. SoVIŽ's reliance on available Census data suggests
that it would be difficult to extend its approach to other dimensions, while the
Argonne approach requires substantial investment in data gathering, compared
with the community-based data gathering of the Coastal Resilience Index, for
example.
KNOWLEDGE AND DATA NEEDS
As mentioned in Chapter 3, the issues of data availability are critical
not only for hazard and disaster informatics, but resilience metrics as well.
However, it is not just data that constrain our ability to measure resilience.
Better understanding on how to implement such a measurement system is also
needed. What should be measured over what time frame and geographic scale?
Should resilience be reassessed on a regular schedule, or should certain factors
trigger a reassessment? Should scales be prescribed and uniform, or should they
be adapted to meet specific circumstances? How should these indicators be
measured (e.g., qualitatively, quantitatively)? Should these data be included into
a single composite index or some other structure, and if a single index, how
should the various components be weighted? By what means can it be
determined that the right elements for the resilience index have been captured?
How is the sensitivity of the index assessed? Addressing these issues through an
integrated research program would assist the nation in providing the scientific
backing for the development of a national resilience scorecard.
Moreover, such a research program could provide useful insights by
making a systematic comparison of the different metrics proposed in the
literature. Besides addressing the questions raised earlier in this paragraph, it
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112 Disaster Resilience: A National Imperative
would be very useful to compare metrics on the basis of cost, and the time and
effort needed to implement and evaluate them.
SUMMARY AND RECOMMENDATION: IMPLEMENTING A
MEASUREMENT SYSTEM
With this background, we now turn to the committee's conclusions and
specific recommendations regarding metrics and indicators. Related topics have
been discussed at several points in the report, including Chapter 3, where we
discuss the lack of consistent, reliable data on the impacts of hazards and
disasters that might feed into the measurement of resilience.
This chapter has focused on the importance of metrics and indicators
that can be used to evaluate resilience, to provide baselines for comparison and
the foundation for a system of tracking improvements. In essence, the committee
concludes from the evidence gathered that without some numerical basis for
assessing resilience it would be impossible to monitor changes or show that
community resilience has improved. At present, no consistent basis for
such measurement exists. We recommend therefore that a National
Resilience Scorecard be established.
Until a community experiences a disaster and has to respond to and
recover from it, demonstrating the complexity, volume of issues, conflicts, and
lack of ownership are difficult. A national resilience scorecard, from which
communities can then develop their own, tailored scorecards, will make it easier
for communities to see the issues they will face without being subjected to the
event and can support necessary work in anticipation of an appropriate
resilience-building strategy. A scorecard will also allow communities to ask the
right questions in advance.
In the preceding sections the committee's vision of such a scorecard
was outlined. It should be readily adaptable to the needs of communities and
levels of government, focusing specifically on the hazards that threaten each
community. It should align with community goals and vision. It should not
attempt unreasonable precision, either in the ways in which individual factors
are measured, or in the ways they are combined into composite indicators.
Rather, the scorecard should follow the examples presented earlier where
qualitative and quantitative measures are mingled, and reduced where
appropriate to ordinal (rankings) rather than interval or ratio scales.
The various indicators reviewed in this chapter vary greatly depending
on the dimensions they assess, the sources of data they employ, and the ways in
which they combine data to obtain indicators. However, certain commonalities
emerge and provide useful guidance in the development of a Scorecard. While
maintaining its commitment to local solutions and not wishing to be overly
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Measuring Progress Toward Resilience 113
prescriptive, the committee emphasizes that it is imperative to include certain
dimensions in the Scorecard:
ˇ Indicators of the ability of critical infrastructure to recover rapidly from
impacts (see, e.g., Section 4.2.1);
ˇ Social factors that enhance or limit a community's ability to recover,
including social capital, language, and socioeconomic status, and the
availability of a workforce with skills relevant to recovery (see, e.g., Section
4.2.3);
ˇ Indicators of the ability of buildings and other structures to withstand the
physical and ecological impacts of disasters (e.g., ground shaking, severe
wind and precipitation, inundation, fires (see, e.g., Section 4.2.5); and
ˇ Factors that capture the special needs of individuals and groups, related to
minority status, mobility, or health status (see, e.g., the T*H*R*I*V*E model
in Section 4.2.6).
Although such a scorecard would be used as a self-assessment tool
employed by individual communities, some central coordination and direction
for the development of the scorecard is appropriate from the federal level. The
committee concludes that responsibility for coordinating the development of a
scorecard should rest with a single federal agency but be compiled through a
national effort that engages with individuals and communities at all levels. The
Department of Homeland Security appears to be the most appropriate agency for
coordinating this collective endeavor. In summary, the committee concludes its
work in the area of metrics and indicators with this recommendation:
Recommendation. The Department of Homeland Security in conjunction
with other federal agencies, state and local partners, and professional
groups should develop a National Resilience Scorecard.
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114 Disaster Resilience: A National Imperative
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