CHAPTER 6

HAZARD, VULNERABILITY, AND RISK ANALYSIS

This chapter describes how preimpact conditions act together with
event-specific conditions to produce a disaster's physical and social
impacts. These disaster impacts can be reduced by emergency management
interventions. In addition, this chapter discusses how emergency
managers can assess the preimpact conditions that produce disaster
vulnerability within their communities. The chapter concludes with a
discussion of vulnerability dynamics and methods for disseminating
hazard/vulnerability data.

**Introduction**

A disaster occurs when an extreme event exceeds a community's ability to
cope with that event. Understanding the process by which natural
disasters produce community impacts is important for four reasons.
First, information from this process is needed to identify the preimpact
conditions that make communities vulnerable to disaster impacts. Second,
information about the disaster impact process can be used to identify
specific segments of each community that will be affected
disproportionately (e.g., low income households, ethnic minorities, or
specific types of businesses). Third, information about the disaster
impact process can be used to identify the event-specific conditions
that determine the level of disaster impact. Fourth, an understanding of
disaster impact process allows planners to identify suitable emergency
management interventions. The process by which disasters produce
community impacts can be explained in terms of models proposed by Cutter
(1996) and Lindell and Prater (2003). Specifically, Figure 6-1 indicates
the effects of a disaster are determined by three preimpact
conditions---hazard exposure, physical vulnerability, and social
vulnerability. There also are three event-specific conditions, hazard
event characteristics, improvised disaster responses, and improvised
disaster recovery. Two of the event-specific conditions, hazard event
characteristics and improvised disaster responses, combine with the
preimpact conditions to produce a disaster's physical impacts. The
physical impacts, in turn, combine with improvised disaster recovery to
produce the disaster's social impacts. Communities can engage in three
types of emergency management interventions to ameliorate disaster
impacts. Physical impacts can be reduced by hazard mitigation practices
and emergency preparedness practices, whereas social impacts can be
reduced by recovery preparedness practices.

The following sections describe the components of the model in greater
detail. Specifically, the next section will describe the three preimpact
conditions---hazard exposure, physical vulnerability, and social
vulnerability. This section will be followed by sections discussing
hazard event characteristics and improvised disaster responses. The
fourth section will discuss disasters' physical impacts, social impacts,
and improvised disaster recovery. The last section will discuss three
types of strategic interventions, hazard mitigation practices, emergency
preparedness practices, and recovery preparedness practices.

#### **Preimpact Conditions**

*Hazard Exposure*

Hazard exposure arises from people's occupancy of geographical areas
where they could be affected by specific types of events that threaten
their lives or property. For natural hazards, this exposure is caused by
living in geographical areas as specific as floodplains that sometimes
extend only a few feet beyond the floodway or as broad as the Great
Plains of the Midwest where tornadoes can strike anywhere over an area
of hundreds of thousands of square miles. For technological hazards,
exposure can arise if people move into areas where they could be exposed
to explosions or hazardous materials releases. In principle, hazard
exposure can be measured by the probability of occurrence of a given
event magnitude, but these exceedance probabilities can be difficult to
obtain for hazards about which the historical data are insufficient to
reliably estimate the probability of very unusual events. For example,
many areas of the US have meteorological and hydrological data that are
limited to the past 100 years, so the estimation of extreme floods
requires extrapolation from a limited data series. Moreover,
urbanization of the watersheds causes the boundaries of the 100-year
floodplains to change in ways that may be difficult for local emergency
managers to anticipate. Even more difficult to estimate are the
probabilities of events, such as chemical and nuclear reactor accidents,
for which data are limited because each facility is essentially unique.
In such cases, techniques of probabilistic safety analysis are used to
model these systems, attach probabilities to the failure of system
components, and synthesize probabilities of overall system failure by
mathematically combining the probabilities of individual component
failure.

[**Figure 6-1.** Disaster Impact Model.]{.underline}

Source: Adapted from Lindell and Prater (2003)

The greatest difficulties are encountered in attempting to estimate the
probabilities of social hazards such as terrorist attacks because the
occurrence of these events is defined by social system dynamics that
cannot presently be modeled in the same way as physical systems. That
is, the elements of social systems are difficult to define and measure.
Moreover, the interactions of the system elements have multiple
determinants and involve complex lag and feedback effects that are not
well understood, let alone precisely measured. Indeed, there are
significant social and political constraints that limit the collection
of data on individuals and groups. These constraints further inhibit the
ability of scientists to make specific predictions of social system
behavior.

*Physical Vulnerability*

*Human vulnerability.* Humans are vulnerable to environmental extremes
of temperature, pressure, and chemical exposures that can cause death,
injury, and illness. For any hazard agent---water, wind, ionizing
radiation, toxic chemicals, infectious agents---there often is
variability in the physiological response of the affected population.
That is, given the same level of exposure, some people will die, others
will be severely injured, still others slightly injured, and the rest
will survive unscathed. Typically, the most susceptible to any
environmental stressor will be the very young, the very old, and those
with weakened immune systems.

*Agricultural vulnerability*. Like humans, agricultural plants and
animals are also vulnerable to environmental extremes of temperature,
pressure, chemicals, radiation, and infectious agents. Like humans,
there are differences among individuals within each plant and animal
population. However, agricultural vulnerability is more complex than
human vulnerability because there is a greater number of species to be
assessed, each of which has its own characteristic response to each
environmental stressor.

*Structural vulnerability*. Structural vulnerability arises when
buildings are constructed using designs and materials that are incapable
of resisting extreme stresses (e.g., high wind, hydraulic pressures of
water, seismic shaking) or that allow hazardous materials to infiltrate
into the building. The construction of most buildings is governed by
building codes intended to protect the life safety of building occupants
from structural collapse---primarily from the dead load of the building
material themselves and the live load of the occupants and
furnishings--- but do not necessarily provide protection from extreme
wind, seismic, or hydraulic loads. Nor do they provide an impermeable
barrier to the infiltration of toxic air pollutants.

*Social Vulnerability*

The social vulnerability perspective (e.g., Cannon, Twigg & Rowell,
2003; Cutter, Boruff & Shirley, 2003) represents an important extension
of previous theories of hazard vulnerability (Burton, et al., 1978). As
a concept, social vulnerability has been defined in terms of people's
"capacity to anticipate, cope with, resist and recover from the impacts
of a natural hazard" (Wisner, Blakie, Canon & Davis, 2004, p. 11).
Whereas people's physical vulnerability refers to their susceptibility
to biological changes (i.e., impacts on anatomical structures and
physiological functioning), their social vulnerability refers to their
susceptibility to behavioral changes. As will be discussed in greater
detail below, these consist of psychological, demographic, economic, and
political impacts.

The central point of the social vulnerability perspective is that, just
as people's occupancy of hazard prone areas and the physical
vulnerability of the structures in which they live and work are not
randomly distributed, neither is social vulnerability randomly
distributed---either geographically or demographically. Thus, just as
variations in structural vulnerability can increase or decrease the
effect of hazard exposure on physical impacts (property damage and
casualties), so too can variations in social vulnerability. Social
vulnerability varies across communities and also across households
within communities. It is the variability in vulnerability that is
likely to be of greatest concern to local emergency managers because it
requires that they identify the areas within their communities having
population segments with the highest levels of social vulnerability.

**Event-Specific Conditions**

*Hazard Event Characteristics*

Hazard impacts often are difficult to characterize because a given
hazard agent may initiate a number of different threats. For example,
tropical cyclones (also known as hurricanes or typhoons) can cause
casualties and damage through wind, rain, storm surge, and inland
flooding (Bryant, 1991). Volcanoes can impact human settlements through
ash fall, explosive eruptions, lava flows, mudflows and floods, and
forest fires (Perry & Lindell, 1990; Saarinen & Sell, 1985; Warrick, et
al., 1981). However, once these distinct threats have been distinguished
from each other, each can be characterized in terms of six significant
characteristics. These are the speed of onset, availability of
perceptual cues (such as wind, rain, or ground movement), the intensity,
scope, and duration of impact, and the probability of occurrence. The
speed of onset and availability of perceptual cues affect the amount of
forewarning that affected populations will have to complete emergency
response actions (Gruntfest, et al., 1978; Lindell, 1994c). In turn,
these attributes determine the extent of casualties among the population
and the degree of damage to structures in the affected area.

A hazard's impact intensity can generally be defined in terms of the
physical materials involved and the energy these materials impart. The
physical materials involved in disasters differ in terms of their
physical state---gas (or vapor), liquid, or solid (or particulate). In
most cases, the hazard from a gas arises from its temperature or
pressure. Examples include hurricane or tornado wind (recall that the
atmosphere is a mixture of gases), which is hazardous because of
overpressures that can inflict traumatic injuries directly on people.
High wind also is hazardous because it can destroy structures and
accelerate debris that can itself cause traumatic injuries.
Alternatively, the hazard from a gas might arise from its toxicity, as
is the case in some volcanic eruptions. Liquids also can be hazardous
because of their toxicity, but the most common liquid hazard is water.
It is hazardous to structures because of the pressure it can exert and
is hazardous to living things when it fills the lungs and prevents
respiration. Lava is solid rock that has been liquefied by extreme heat
and therefore is hazardous to people and structures because of its
thermal energy. Solids also can be hazardous if they take the form of
particulates such as airborne volcanic ash or floodborne mud. These are
particularly significant because they can leave deposits that have
impacts of long duration.

The scope of impact defines the number of affected social units (e.g.,
individuals, households, and businesses). The probability of occurrence
(per unit of time) is another important characteristic that affects
disaster impacts indirectly because more probable hazards are likely to
mobilize communities to engage in hazard mitigation and emergency
preparedness measures to reduce their vulnerability (Prater & Lindell,
2000).

*Improvised Disaster Response*

Disaster myths commonly portray disaster victims as dazed, panicked, or
disorganized but, as will be discussed at greater length in Chapter 8,
people actually respond in a generally adaptive manner when disasters
strike. Adaptive response is often delayed because *normalcy bias*
delays people's realization that an improbable event is, in fact,
occurring to them. Further delays occur because people have limited
information about the situation and, therefore, seek confirmation of any
initial indications of an emergency before initiating protective action.
In addition, the vast majority of people respond in terms of their
customary social units---especially their households and
neighborhoods---which usually consumes time in developing social
organizations that can cope with the disaster's demands. Contrary to
stereotypes of individual selfishness, disaster victims often devote
considerable effort to protecting others' persons and property.
Accordingly, there is considerable convergence on the disaster impact
area, as those in areas nearby move in to offer assistance. When
existing organizations seem incapable of meeting the needs of the
emergency response, they expand to take on new members, extend to take
on new tasks, or new organizations emerge (Dynes, 1970).

*Improvised Disaster Recovery*

Once the situation has stabilized to the point that the imminent threat
to life and property has abated, disaster-stricken communities must
begin the long process of disaster recovery. Immediate tasks in this
process include damage assessment, debris clearance, reconstruction of
infrastructure (electric power, fuel, water, wastewater,
telecommunications, and transportation networks), and reconstruction of
buildings in the residential, commercial, and industrial sectors.
Improvised disaster assistance is derived primarily from resources
provided by individuals and organizations within the community. The
victims themselves might have financial (e.g., savings and insurance) as
well as tangible assets (e.g., property) that are undamaged by hazard
impact. As one might expect, low-income victims tend to have lower
levels of savings, but they also are more likely to be victims of
insurance redlining and, thus, have been forced into contracts with
insurance companies that go bankrupt after the disaster. Thus, even
those who plan ahead for disaster recovery can find themselves without
the financial resources they need (Peacock & Girard, 1997).
Alternatively, victims can promote their recovery by bringing in
additional funds through overtime employment or by freeing up the needed
funds by reducing their consumption below preimpact levels. Friends,
relatives, neighbors, and coworkers can assist recovery through
financial and in-kind contributions, as can community based
organizations (CBOs) and local government. In addition, the latter also
can provide assistance by means of tax deductions or deferrals.

**Disaster Impacts**

As noted earlier, disaster impacts comprise physical and social impact.
The physical impacts of disasters include casualties (deaths and
injuries) and property damage, and both vary substantially across hazard
agents. The physical impacts of a disaster are usually the most obvious,
easily measured, and first reported by the news media. Social impacts,
which include psychosocial, demographic, economic, and political
impacts, can develop over a long period of time and can be difficult to
assess when they occur. Despite the difficulty in measuring these social
impacts, it is nonetheless important to monitor them, and even to
predict them if possible, because they can cause significant problems
for the long-term functioning of specific types of households and
businesses in an affected community. A better understanding of
disasters' social impacts can provide a basis for preimpact prediction
and the development of contingency plans to prevent adverse consequences
from occurring.

*Physical Impacts*

*Casualties*. According to Noji (1997b), hurricanes produced 16 of the
65 greatest disasters of the 20^th^ Century (in terms of deaths) and the
greatest number of deaths from 1947-1980 (499,000). Earthquakes produced
28 of the greatest disasters and 450,000 deaths, whereas floods produced
four of the greatest disasters and 194,000 deaths. Other significant
natural disasters include volcanic eruptions with nine of the greatest
disasters and 9,000 deaths, landslides with four of the greatest
disasters and 5,000 deaths, and tsunamis with three of the greatest
disasters and 5,000 deaths. There is significant variation by country,
with developing countries in Asia, Africa, and South America accounting
for the top 20 positions in terms of number of deaths from 1966-1990.
Low-income countries suffer approximately 3,000 deaths per disaster,
whereas the corresponding figure for high-income countries is
approximately 500 deaths per disaster. Moreover, these disparities
appear to be increasing because the average annual death toll in
developed countries declined by at least 75% between 1960 and 1990, but
the same time period saw increases of over 400% in developing countries
(Berke, 1995).

There often are difficulties in determining how many of the deaths and
injuries are "caused by" a disaster. In some cases it is impossible to
determine how many persons are missing and, if so, whether this is due
to death or unrecorded relocation. The size of the error in estimates of
disaster death tolls can be seen in the fact that for many of the most
catastrophic events the number of deaths is rounded to the nearest
thousand and some even are rounded to the nearest ten thousand (Noji,
1997b). Estimates of injuries are similarly problematic (see Langness,
1994; Peek-Asa, et al., 1998; Shoaf, et al., 1998, regarding conflicting
estimates of deaths and injuries attributable to the Northridge
earthquake). Even when bodies can be counted, there are problems because
disaster impact may be only a contributing factor to casualties with
pre-existing health conditions. Moreover, some casualties are indirect
consequences of the hazard agent as, for example, with casualties caused
by structural fires following earthquakes (e.g., burns) and destruction
of infrastructure (e.g., illnesses from contaminated water supplies).

*Damage*. Losses of structures, animals, and crops also are important
measures of physical impacts, and these are rising exponentially in the
United States (Mileti, 1999). However, the rate of increase is even
greater in developing countries such as India and Kenya (Berke, 1995).
Such losses usually result from physical damage or destruction of
property, but they also can be caused by losses of land use to chemical
or radiological contamination or loss of the land itself to subsidence
or erosion. Damage to the built environment can be classified broadly as
affecting residential, commercial, industrial, infrastructure, or
community services sectors. Moreover, damage within each of these
sectors can be divided into damage to structures and damage to contents.
It usually is the case that damage to contents results from collapsing
structures (e.g., hurricane winds failing the building envelope and
allowing rain to destroy the furniture inside the building). Because
collapsing buildings are a major cause of casualties as well, this
suggests that strengthening the structure will protect the contents and
occupants. However, some hazard agents can damage building contents
without affecting the structure itself (e.g., earthquakes striking
seismically-resistant buildings whose contents are not securely
fastened). Thus, risk area residents may need to adopt additional hazard
adjustments to protect contents and occupants even if they already have
structural protection.

Perhaps the most significant structural impact of a disaster on a
stricken community is the destruction of households' dwellings. Such an
event initiates what can be a very long process of disaster recovery for
some population segments. According to Quarantelli (1982a), people
typically pass through four stages of housing recovery following a
disaster. The first stage is *emergency shelter*, which consists of
unplanned and spontaneously sought locations that are intended only to
provide protection from the elements, typically open yards and cars
after earthquakes (Bolin & Stanford, 1991, 1998). The next step is
*temporary shelter*, which includes food preparation and sleeping
facilities that usually are sought from friends and relatives or are
found in commercial lodging, although "mass care" facilities in school
gymnasiums or church auditoriums are acceptable as a last resort. The
third step is *temporary housing*, which allows victims to re-establish
household routines in nonpreferred locations or structures. The last
step is *permanent housing*, which re-establishes household routines in
preferred locations and structures.

Households vary in the progression and duration of each type of housing
and the transition from one stage to another can be delayed
unpredictably, as when it took nine days for shelter occupancy to peak
after the Whittier Narrows earthquake (Bolin, 1993). Particularly
significant are the problems faced by lower income households, which
tend to be headed disproportionately by females and racial/ethnic
minorities. Such households are more likely to experience destruction of
their homes because of preimpact locational vulnerability. This is
especially true in developing countries such as Guatemala (Peacock,
Killian & Bates, 1987), but also has been reported in the US (Peacock &
Girard, 1997). The homes of these households also are more likely to be
destroyed because the structures were built according to older, less
stringent building codes, used lower quality construction materials and
methods, and were less well maintained (Bolin & Bolton, 1986). Because
lower income households have fewer resources on which to draw for
recovery, they also take longer to transition through the stages of
housing, sometimes remaining for extended periods of time in severely
damaged homes (Girard & Peacock, 1997). In other cases, they are forced
to accept as permanent what originally was intended as temporary housing
(Peacock, et al., 1987). Consequently, there may still be low-income
households in temporary sheltering and temporary housing even after
high-income households all have relocated to permanent housing (Berke,
et al., 1993; Rubin, Sapperstein & Barbee, 1985).

As is the case with estimates of casualties, estimates of losses to the
built environment are prone to error. Damage estimates are most accurate
when trained damage assessors enter each building to assess the percent
of damage to each of the major structural systems (e.g., roof, walls,
floors) and the percentage reduction in market valuation due to the
damage. Early approximate estimates are obtained by conducting
"windshield surveys" in which trained damage assessors drive through the
impact area and estimate the extent of damage that is visible from the
street, or by conducting computer analyses using HAZUS (National
Institute of Building Sciences, 1998). These early approximate estimates
are especially important in major disasters because detailed assessments
are not needed in the early stages of disaster recovery and the time
required to conduct them on a large number of damaged structures using a
limited number of qualified inspectors would unnecessarily delay the
community recovery process.

Other important physical impacts include damage or contamination to
cropland, rangeland, and woodlands. Such impacts may be well understood
for some hazard agents but not others. For example, ashfall from the
1980 Mt. St. Helens eruption was initially expected to devastate crops
and livestock in downwind areas, but no significant losses materialized
(Warrick, et al., 1981). There also is concern about damage or
contamination to the natural environment (wild lands) because these
areas serve valuable functions such as damping the extremes of river
discharge and providing habitat for wildlife. In part, concern arises
from the potential for indirect consequences such as increased runoff
and silting of downstream river beds, but many people also are concerned
about the natural environment simply because they value it for its own
sake.

*Social Impacts*

For many years, research on the social impacts of disasters consisted of
an accumulation of case studies, but two research teams conducted
comprehensive statistical analyses of extensive databases to assess the
long-term effects of disasters on stricken communities (Friesma, et al.,
1979; Wright, et al., 1979). The more comprehensive Wright, et al.
(1979) study used census data from the 1960 (preimpact) and 1970
(post-impact) censuses to assess the effects of all recorded disasters
in the United States. The authors concurred with earlier findings by
Friesma, et al. (1979) in concluding no long-term social impact of
disasters could be detected at the community level. In discussing their
findings, the authors acknowledged their results were dominated by the
types of disasters occurring most frequently in the United
States---tornadoes, floods, and hurricanes. Moreover, most of the
disasters they studied had a relatively small scope of impact and thus
caused only minimal disruption to their communities even in the short
term. Finally, they noted their findings did not preclude the
possibility of significant long-term impacts upon lower levels such as
the neighborhood, business, and household.

Nonetheless, their findings called attention to the importance of the
*impact ratio*---the amount of damage divided by the amount of community
resources---in understanding disaster impacts. They hypothesized
long-term social impacts tend to be minimal in the US because most
hazard agents have a relatively small scope of impact and tend to strike
undeveloped areas more frequently than intensely developed areas simply
because there are more of the former than the latter. Thus, the
numerator of the impact ratio tends to be low and local resources are
sufficient to prevent long-term effects from occurring. Even when a
disaster has a large scope of impact and strikes a large developed area
(causing a large impact ratio in the short term), state and federal
agencies and NGOs (e.g., American Red Cross) direct recovery resources
to the affected area, thus preventing long-term impacts from occurring.
For example, Hurricane Andrew inflicted \$26.5 billion in losses to the
Miami area, but this was only 0.4% of the US GDP (Charvériat, 2000).
Recovery problems described in the studies reported in Peacock, Morrow
and Gladwin (1997) were determined more by organizational impediments
than by the lack of resources.

*Psychosocial impacts*. Research reviews conducted over a period of 25
years have concluded that disasters can cause a wide range of negative
psychological responses (Bolin 1985; Gerrity & Flynn, 1997; Houts,
Cleary & Hu, 1988; Perry & Lindell, 1978). These include
psychophysiological effects such as fatigue, gastrointestinal upset, and
tics, as well as cognitive signs such as confusion, impaired
concentration, and attention deficits. Psychological impacts include
emotional signs such as anxiety, depression, and grief. They also
include behavioral effects such as sleep and appetite changes,
ritualistic behavior, and substance abuse. In most cases, the observed
effects are mild and transitory---the result of "normal people,
responding normally, to a very abnormal situation" (Gerrity & Flynn
1997, p. 108). Few disaster victims require psychiatric diagnosis and
most benefit more from a *crisis counseling* orientation than from a
*mental health treatment* orientation, especially if their normal social
support networks of friends, relatives, neighbors, and coworkers remain
largely intact. However, there are population segments requiring special
attention and active outreach. These include children, frail elderly,
people with pre-existing mental illness, racial and ethnic minorities,
and families of those who have died in the disaster. Emergency workers
also need attention because they often work long hours without rest,
have witnessed horrific sights, and are members of organizations in
which discussion of emotional issues may be regarded as a sign of
weakness (Rubin, 1991). However, as Chapter 11 will indicate, there is
little evidence of emergency workers needing directive therapies either.

The negative psychological impacts described above, which Lazarus and
Folkman (1984) call *emotion focused coping*, generally disrupt the
social functioning of only a very small portion of the victim
population. Instead, the majority of disaster victims engage in adaptive
*problem focused coping* activities to save their own lives and those of
their closest associates. Further, there is an increased incidence in
prosocial behaviors such as donating material aid and a decreased
incidence of antisocial behaviors such as crime (Drabek, 1986; Mileti,
et al., 1975; Siegel, et al., 1999). In some cases, people even engage
in altruistic behaviors that risk their own lives to save the lives of
others (Tierney, et al., 2001).

There also are psychological impacts with long-term adaptive
consequences, such as changes in risk perception (beliefs in the
likelihood of the occurrence a disaster and its personal consequences
for the individual) and increased hazard intrusiveness (frequency of
thought and discussion about a hazard). In turn, these beliefs can
affect risk area residents' adoption of household hazard adjustments
that reduce their vulnerability to future disasters. However, these
cognitive impacts of disaster experience do not appear to be large in
aggregate, resulting in modest effects on household hazard adjustment
(see Lindell & Perry, 2000 for a review of the literature on seismic
hazard adjustment, and Lindell & Prater 2000; Lindell & Whitney, 2000;
and Whitney, Lindell & Nguyen, 2004 for more recent empirical research).

*Demographic impacts*. The demographic impact of a disaster can be
assessed by adapting the *demographic balancing equation*, *P~a\ ~*--
*P~b~* = *B* -- *D* + *IM* -- *OM*, where *P~a~* is the population size
after the disaster, *P~b~* is the population size before the disaster,
*B* is the number of births, *D* is the number of deaths, *IM* is the
number of immigrants, and *OM* is the number of emigrants (Smith, Tayman
& Swanson, 2001). The magnitude of the disaster impact, *P~a\ ~*--
*P~b~*, is computed for the population of a specific geographical area
and two specific points in time. Ideally, the geographical area would
correspond to the disaster impact area, *P~b~* would be immediately
before disaster impact, and *P~a~* would be immediately after disaster
impact. In practice, population data are available for census divisions
(census block, block group, tract, or larger area), so a Geographical
Information System (GIS) must be used to estimate the impact on the
impact area. Moreover, population data are likely to be most readily
available from the decennial censuses, so the overall population change
and its individual demographic components---births, deaths, immigration,
and emigration---are likely to be estimated from that source (e.g.,
Wright, et al., 1979). On rare occasions, special surveys have been
conducted in the aftermath of disaster (e.g., Peacock, Morrow & Gladwin,
1997). The limited research available on demographic impacts (Friesma,
et al., 1979; Wright, et al., 1979) suggests disasters have negligible
demographic impacts on American *communities,* but the highly aggregated
level of analysis in these studies does not preclude the possibility of
significant impacts at lower levels of aggregation (census tracts, block
groups, or blocks). Although it is logically possible that disasters
could affect the number of births, it does not seem likely that the
effect would be large. Moreover, as noted in the previous section on
physical impacts, the number of deaths from disasters in the United
States has been small relative to historical levels (e.g., the 6000
deaths in the 1900 Galveston hurricane were approximately 17% of the
city's population) or to the levels reported in developing countries.
The major demographic impacts of disasters are likely to be the
(temporary) immigration of construction workers after major disasters
and the emigration of population segments that have lost housing. In
many cases, the housing-related emigration is also temporary, but there
are documented cases in which housing reconstruction has been delayed
indefinitely---leading to "ghost towns" (Comerio, 1998). Other potential
causes of emigration are psychological impacts (belief that the
likelihood of disaster recurrence is unacceptably high), economic
impacts (loss of jobs or community services), or political impacts
(increased neighborhood or community conflict).

*Economic impacts*. The property damage caused by disaster impact
creates losses in asset values that can be measured by the cost of
repair or replacement (Committee on Assessing the Costs of Natural
Disasters, 1999). Disaster losses in United States are initially borne
by the affected households, businesses, and local government agencies
whose property is damaged or destroyed. However, some of these losses
are redistributed during the disaster recovery process. There have been
many attempts to estimate the magnitude of direct losses from individual
disasters and the annual average losses from particular types of hazards
(e.g., Mileti, 1999). Unfortunately, these losses are difficult to
determine precisely because there is no organization that tracks all of
the relevant data and some data are not recorded at all (Charvériat,
2000; Committee on Assessing the Costs of Natural Disasters, 1999). For
insured property, the insurers record the amount of the deductible and
the reimbursed loss, but uninsured losses are not recorded so they must
be estimated---often with questionable accuracy.

The ultimate economic impact of a disaster depends upon the disposition
of the damaged assets. Some of these assets are not replaced, so their
loss causes a reduction in consumption (and, thus, a decrease in the
quality of life) or a reduction in investment (and, thus, a decrease in
economic productivity). Other assets are replaced---either through
in-kind donations (e.g., food and clothing) or commercial purchases. In
the latter case, the cost of replacement must come from some source of
recovery funding, which generally can be characterized as either
intertemporal transfers (to the present time from past savings or future
loan payments) or interpersonal transfers (from one group to another at
a given time). Some of the specific mechanisms for financing recovery
include obtaining tax deductions or deferrals, unemployment benefits,
loans (paying back the principal at low- or no-interest), grants
(requiring no return of principal), insurance payoffs, or additional
employment. Other sources include depleting cash financial assets (e.g.,
savings accounts), selling tangible assets, or migrating to an area with
available housing, employment, or less risk (in some cases this is done
by the principal wage earner only).

In addition to direct economic losses, there are indirect losses that
arise from the interdependence of community subunits. Research on the
economic impacts of disasters (Alesch, et al., 1993; Dacy & Kunreuther,
1969; Dalhamer & D'Sousa, 1997; Durkin, 1984; Gordon, et al., 1995;
Kroll, et al., 1991; Lindell & Perry, 1998; Nigg, 1995; Tierney, 1997a)
suggests the relationships among the social units within a community can
be described as a state of dynamic equilibrium involving a steady flow
of resources, especially money. Specifically, a household's linkages
with the community are defined by the money it must pay for products,
services, and infrastructure support. This money is obtained from the
wages that employers pay for the household's labor. Similarly, the
linkages that a business has with the community are defined by the money
it provides to its employees, suppliers, and infrastructure in exchange
for inputs such as labor, materials and services, and electric power,
fuel, water/wastewater, telecommunications, and transportation.
Conversely, it provides products or services to customers in exchange
for the money it uses to pay for its inputs.

It also is important to recognize the financial impacts of recovery (in
addition to the financial impacts of emergency response) on local
government. Costs must be incurred for tasks such as damage assessment,
emergency demolition, debris removal, infrastructure restoration, and
re-planning stricken areas. In addition to these costs, there are
decreased revenues due to loss or deferral of sales taxes, business
taxes, property taxes, personal income taxes, and user fees.

*Political impacts*. There is substantial evidence that disaster impacts
can cause social activism resulting in political disruption, especially
during the seemingly interminable period of disaster recovery. The
disaster recovery period is a source of many victim grievances and this
creates many opportunities for community conflict, both in the US (Bolin
1982, 1993) and abroad (Bates & Peacock 1988). Victims usually attempt
to recreate preimpact housing patterns, but it can be problematic for
their neighbors if victims attempt to site mobile homes on their own
lots while awaiting the reconstruction of permanent housing. Conflicts
arise because such housing usually is considered to be a blight on the
neighborhood and neighbors are afraid the "temporary" housing will
become permanent. Neighbors also are pitted against each other when
developers attempt to buy up damaged or destroyed properties and build
multifamily units on lots previously zoned for single family dwellings.
Such rezoning attempts are a major threat to the market value of
owner-occupied homes but tend to have less impact on renters because
they have less incentive to remain in the neighborhood. There are
exceptions to this generalization because some ethnic groups have very
close ties to their neighborhoods, even if they rent rather than own.

Attempts to change prevailing patterns of civil governance can arise
when individuals sharing a grievance about the handling of the recovery
process seek to redress that grievance through collective action.
Consistent with Dynes's (1970) typology of organizations, existing
community groups with an explicit political agenda can *expand* their
membership to increase their strength, whereas community groups without
an explicit political agenda can *extend* their domains to include
disaster-related grievances. Alternatively, new groups can *emerge* to
influence local, state, or federal government agencies and legislators
to take actions that they support and to terminate actions that they
disapprove. Indeed, such was the case for Latinos in Watsonville,
California following the Loma Prieta earthquake (Tierney, et al., 2001).
Usually, community action groups pressure government to provide
additional resources for recovering from disaster impact, but may oppose
candidates' re-elections or even seek to recall some politicians from
office (Olson & Drury, 1997; Prater & Lindell, 2000; Shefner, 1999). The
point here is not that disasters produce political behavior that is
different from that encountered in normal life. Rather, disaster impacts
might only produce a different set of victims and grievances and,
therefore, a minor variation on the prevailing political agenda (Morrow
& Peacock, 1997).

**Emergency Management Interventions**

As Figure 6-1 indicates, there are three types of preimpact
interventions that can effect reductions in disaster impacts. Hazard
mitigation and emergency preparedness practices directly reduce a
disaster's physical impacts (casualties and damage) and indirectly
reduce its social impacts, whereas recovery preparedness practices
directly reduce a disaster's social impacts. Improvised disaster
response actions also directly affect disasters' physical impacts but,
by their very nature, are likely to be much less effective than planned
interventions. Similarly, improvised recovery assistance directly
affects disasters' social impacts but is likely to be less effective
than systematic recovery preparedness practices.

Figure 6-1 includes the four "phases" of emergency
management---mitigation, preparedness, response, and recovery---but
makes it clear there is a complex relationship between them. In reality,
these "phases" might better be called functions, since they are neither
discrete nor temporally sequential. Later chapters will address hazard
mitigation (Chapter 7), emergency preparedness (Chapter 9), emergency
response (Chapter 10), and disaster recovery (Chapter 11) in greater
detail. However, this section will provide a brief description of each
of these functions and their interrelationships.

*Hazard Mitigation Practices*

One way to reduce the physical impacts of disasters is to adopt hazard
mitigation practices. These can be defined as preimpact actions that
protect passively against casualties and damage at the time of hazard
impact (as opposed to an active emergency response). Hazard mitigation
includes hazard source control, community protection works, land use
practices, building construction practices, and building contents
protection. Hazard source control acts directly on the hazard agent to
reduce its magnitude or duration. For example, patching a hole in a
leaking tank truck prevents a gas from being released. Community
protection works, which limit the impact of a hazard agent on an entire
community, include dams and levees that protect against floodwater and
sea walls that protect against storm surge. Land use practices reduce
hazard vulnerability by avoiding construction in areas that are
susceptible to hazard impact. The use of the term land use *practices*
instead of land use *regulations* is deliberate. Landowners can adopt
sustainable practices whether or not they are required to do so. Thus,
government agencies can encourage the adoption of appropriate land use
practices by providing incentives to encourage development in safe
locations, establishing sanctions to prevent development in hazardous
locations, or engaging in risk communication to inform landowners about
the risks and benefits of development in locations throughout the
community.

Hazard mitigation can also be achieved through building construction
practices that make individual structures less vulnerable to natural
hazards. Here too, the use of the term building construction *practices*
rather than building *codes* is deliberate because building owners can
adopt hazard resistant designs and construction materials in the absence
of government intervention. Disaster resistant construction practices
include elevating structures out of flood plains, designing structures
to respond more effectively to lateral stresses, and providing window
shutters to protect against wind pressure and debris impacts. Government
agencies can encourage the adoption of appropriate building construction
practices by providing incentives to encourage appropriate designs and
materials, establishing code requirements for hazard resistant building
designs and materials, or informing building owners about the risks and
benefits of different building designs and materials. Finally, hazard
mitigation can be achieved by contents protection strategies such as
elevating appliances above the base flood elevation or bolting them to
walls to resist seismic forces.

##### Emergency Preparedness Practices

# Another way to reduce a disaster's physical impacts is to adopt emergency preparedness practices, which are preimpact actions that provide the human and material resources needed to support active responses at the time of hazard impact (Lindell & Perry, 2000). The first step in emergency preparedness is to use community hazard/vulnerability analysis (HVA) to identify the emergency response demands that must be met by performing four basic emergency response functions---emergency assessment, hazard operations, population protection, and incident management (Lindell & Perry, 1992, 1996b). Emergency assessment consists of actions that define the potential scope of the disaster impacts (e.g., projecting hurricane wind speed) whereas hazard operations consists of short-term actions to protect property through expedient hazard mitigation actions initiated during an emergency (e.g., sandbagging around structures). Population protection actions protect people from impact (e.g., warning and evacuation) and incident management actions activate and coordinate the emergency response (e.g., communication among responding agencies). The next step is to determine which community organization will be responsible for accomplishing each function (Federal Emergency Management Agency, 1996b). Once functional responsibilities have been assigned, each organization must develop procedures for accomplishing those functions. Finally, the organizations must acquire response resources (personnel, facilities, and equipment) to implement their plans and they need to maintain preparedness for emergency response through continued planning, training, drills, and exercises (Daines, 1991).

##### Recovery Preparedness Practices

Just as emergency preparedness practices are preimpact actions intended
to develop the human and material resources needed to support an active
emergency response, recovery preparedness practices are preimpact
actions that are intended to develop the financial and material
resources needed to support a prompt and effective disaster recovery.
First of all, households and businesses need to prepare for disaster
recovery by purchasing hazard insurance because this will provide the
money they need to rebuild damaged structures and replace destroyed
contents. However, hazard insurance varies significantly in its
availability and cost---flood, hurricane, and earthquake insurance being
particularly problematic (Kunreuther & Roth, 1998). Moreover, some
ethnic groups cannot afford the rates of high quality insurance
companies or are denied coverage altogether (Peacock & Girard, 1997).

In addition, the governments of hazard prone communities need to prepare
for actions including impact assessment, debris management,
infrastructure restoration, housing recovery, economic recovery, and
linkage to hazard mitigation. It seems to be commonly thought that the
development of disaster recovery operations plans (ROPs) can be delayed
until after disaster strikes, but practitioners and researchers agree
that community disaster recovery is faster and more effective when it is
based on a plan that has been developed prior to disaster impact (Geis,
1996; Olson, Olson & Gawronski, 1998; Schwab, et al., 1998; Wilson,
1991; Wu & Lindell, 2004).

There are six important features of a preimpact ROP. First, it should
define a disaster recovery organization. Second, it should identify the
location of temporary housing because resolving this issue can cause
conflicts that delay consideration of longer-term issues of permanent
housing and distract policymakers altogether from hazard mitigation
(Bolin & Trainer, 1978; Bolin, 1982). Third, the plan should indicate
how to accomplish essential tasks such as damage assessment,
condemnation, debris removal and disposal, rezoning, infrastructure
restoration, temporary repair permits, development moratoria, and permit
processing because all of these tasks must be addressed before the
reconstruction of permanent housing can begin (Schwab, et al., 1998).

Fourth, preimpact recovery plans also should address the licensing and
monitoring of contractors and retail price controls to ensure victims
are not exploited and also should address the jurisdiction's
administrative powers and resources, especially the level of staffing
that is available. It is almost inevitable that local government will
have insufficient staff to perform critical recovery tasks such as
damage assessment and building permit processing, so arrangements should
be made to borrow staff from other jurisdictions (via pre-existing
Memoranda of Agreement) and to use trained volunteers such as local
engineers, architects, and planners. Fifth, these plans also need to
address the ways in which recovery tasks will be implemented at
historical sites (Spennemann & Look, 1998). Finally, preimpact recovery
plans should recognize the recovery period as a unique time to enact
policies for hazard mitigation and make provision for incorporating this
objective into the recovery planning process.

##### **Conducting Community Hazard/Vulnerability Analyses**

The model described in the previous pages provides a framework for
understanding how hazard agent characteristics produce physical and
social impacts that can be ameliorated by emergency management
interventions. The next section will describe how local emergency
managers can use this framework to guide assessments of their
communities' exposure to specific hazards and vulnerability to physical
and social impacts. Although this section focuses on *how* to conduct
hazard/vulnerability analyses (HVAs), it is important to remember *who*
should be involved in the process. The HVAs provides critical
information for the community's hazard mitigation, emergency
preparedness, and recovery preparedness practices so it should involve a
wide range of the community's emergency management stakeholders (Pearce,
2003). This is an important reason for the LEMC to conduct the community
HVA through the LEMC's Hazard/Vulnerability Analysis subcommittee.

*Mapping Natural Hazard Exposure*

States and local jurisdictions across the country vary in their exposure
to the hazards described in Chapter 5. Consequently, an important
objective for a local emergency manager is to identify the hazards that
his or her community should set as priorities for its emergency
management program. There are many useful sources of information about
the regional incidence of these hazards, one of which is the set of maps
contained in the Federal Emergency Management Agency's (1997) *Multi
Hazard Identification and Risk Assessment*. This source has an extensive
set of maps describing exposure to natural hazards and also addresses
some technological hazards. The maps of natural hazard exposures
contained in *Multi Hazard Identification and Risk Assessment* can be
supplemented by visiting the Web sites of FEMA (www.fema.gov), the US
Geological Survey (www.usgs.gov), and the National Weather Service
(www.nws.noaa.gov). These maps provide a good start toward assessing the
potential impacts of disasters, but they have three limitations. First,
many of these large scale maps are designed to compare the relative risk
of broad geographical areas (i.e., regions of the country). This
information enables local emergency managers to identify the hazards
that could strike their jurisdictions, but it does not provide enough
resolution to tell them which areas *within* their jurisdictions are
most likely to be struck by a disaster. For example, a coastal county
might be exposed to hurricanes but, as Chapter 5 indicated, only part of
the jurisdiction is likely to experience significant damage.
Consequently, smaller scale maps are needed to assess exposure of
different areas to storm surge, inland flooding, and high wind.

Second, these maps vary from one hazard to another in terms of whether
they define risk areas in terms of event magnitudes or in terms of
recurrence intervals. For example, hurricane risk area maps identify
areas that are expected to be affected by Category 1-5 hurricanes.
However, these maps provide no information about the probability that
each of these different hurricane intensities would occur. By contrast,
US Geological Survey earthquake hazard maps plot the peak ground
acceleration (PGA) with a 2% probability of exceedance in 50 years.
Thus, these maps provide useful information about the areas in which
buildings are most likely to collapse and some indication likelihood of
a disaster. However, as will be explained in the section on physical
vulnerability, trained engineers would be needed to use this
quantitative information to assess the probabilities of building
failure.

Third, these maps are insufficient for local HVAs because local
emergency managers also need to assess the relative risk of different
hazards for a given geographical area. That is, emergency managers need
to know whether their jurisdictions are at risk for certain hazards, but
they also need to know what is the likelihood of a flood *in comparison
to* a tornado, an earthquake, or a toxic chemical release.

As a result of these limitations, local emergency managers must often
settle for qualitative comparisons of the relative risk of different
hazards. That is, they must typically categorize the probability of
disaster impact as high, medium, or low (Federal Emergency Management
Agency, 1996b). Such categorization provides only a "rough screen" for
determining which hazards require the most attention, but this
limitation is often more apparent than real because many hazards impose
similar demands on the community, especially during the emergency
response phase. Specifically, the equipment and methods used for
emergency assessment and hazard operations might differ among the
meteorological, hydrological, geophysical, and hazardous materials
hazards, but the equipment and methods used for population protection
and incident management will be quite similar. Consequently, differences
in hazard probability are often unimportant because preparedness for one
provides preparedness for many, if not all, other hazards. Nonetheless,
many mitigation measures are hazard-specific, so an understanding of the
relative risk from different hazards can provide valuable guidance in
determining which investments are most likely to reduce a community's
vulnerability.

*Mapping Hazmat Exposures*

Incidents involving fires, explosions, or chemical releases can be
initiated by internal (accident or sabotage) or external (geophysical,
meteorological, or hydrological events, or terrorist attacks) causes.
The types of hazards that can occur at a chemical facility, their
initiating events, their consequences, and their likelihoods of
occurrence can be assessed using hazard analysis (Huebner, et al.,
2000). This process begins by identifying dangerous chemicals (i.e.,
those that are threats because of their flammability, reactivity, or
toxicity), their locations, and the quantities stored at those
locations. Once the chemical inventory has been developed, this
information can be used to assess the threats these chemicals pose to
the facility, its workers, its neighbors, and the environment. In the
case of Extremely Hazardous Substances (EHSs) defined under SARA Title
III, *Vulnerable Zones* (VZs) can be computed using data on the
chemical's toxicity, its quantity available for release, the type of
spill (liquid or gaseous), the postulated release duration (e.g., 10
minutes), assumed meteorological conditions (wind speed and atmospheric
stability), and terrain (urban or rural). Available methods include
manual computations (US Environmental Protection Agency, 1987), *ALOHA*
(Federal Emergency Management Agency, no date, a, see information about
*CAMEO* at www.epa.gov/ceppo/cameo), or *RMP\*Comp* at
yosemite.epa.gov/oswer/ceppoweb.nsf/content/ rmp-comp.htm). Once the
radii of the VZs for the different chemicals have been computed, these
can be overlaid onto a map with the release point in the center of the
circle and the radius drawn around it (see Figure 6-2).

Emergency managers also should work with their LEPCs to identify the
highway, rail, water, and air routes though which hazardous materials
are transported. Once these routes have been identified, the number of
tank trucks, railroad tankcars, and barges carrying each type of
hazardous material can be counted in a *commodity flow study*.
Information about hazardous materials transportation can be found on the
US DOT Web site (hazmat.dot.gov) and specific guidance for commodity
flow studies is found at hazmat.dot.gov/hmep/guide_flow_surveys.pdf.
Once the chemicals being transported have been identified, analysts can
use the same procedures that were used for fixed site facilities. As
Figure 6-2 indicates, this will lead to the construction of rectangular
VZs surrounding the transportation routes. The facility and
transportation route VZs can then be examined to identify areas of
residential, commercial, and industrial land use (see Lindell, 1995, for
an analysis of hazardous waste transportation to an incinerator).

[**Figure 6-2.** Vulnerable Zones around fixed-site facility and
transportation route.]{.underline}

Source: Adapted from Lindell (2006)

If VZs for the transportation routes have not been prepared before an
incident occurs, the *North American Emergency Response Guidebook*
(hazmat.dot.gov/ohmform.htm#erg) can be used to approximate them. This
document contains a table listing the principal chemicals commonly found
in transportation, each chemical's identification number, and the
emergency response guide that should be used to provide technical
assistance in responding to a spill. In addition, the table identifies
the most dangerous chemicals and directs emergency responders to a Table
of Initial Isolation and Protective Action Distances that should be used
to determine where protective action (either evacuation or shelter
in-place) should be implemented. Emergency managers should understand
that the *Emergency Response Guidebook* classifies releases only as
small or large, so use of the procedures in the *Technical Guidance for
Hazards Analysis* or more advanced methods identified in the *Handbook
of Chemical Hazard Analysis Procedures* is preferred to the table in the
*Emergency Response Guidebook*.

A special case of hazmat exposures arises in connection with nuclear
power plants. The NRC conducted extensive analyses to define the
emergency planning zones (EPZs) that should be designated around these
facilities. The NRC's analyses led to the establishment of a 10 mile
radius *plume inhalation EPZ* in which state and local authorities
should be prepared for people to evacuate or shelter in-place to avoid
inhalation exposure and direct radiation from a radioactive plume (US
Nuclear Regulatory Commission, 1978). In addition, there is a 50 mile
radius *ingestion pathway EPZ* in which authorities should prepare to
monitor water, milk, and food (especially leafy green vegetables) for
contamination.

*Mapping Exposure to Secondary Hazards*

Emergency managers should recall that some disaster impacts can initiate
others. As noted in Chapter 5, earthquakes can cause surface faulting,
ground failure, landslides, fires, dam failures, and hazardous materials
releases in addition to the expected structural failures caused by
ground shaking. One method of identifying areas exposed to multiple
hazards is to use a GIS to overlay the areas subject to these different
hazards. This is accomplished by entering all of the data on primary and
secondary hazard exposures and special facilities into a GIS that
creates separate layers for fault lines; areas prone to the highest
levels of ground shaking, subsidence, and landsliding; hazardous
facility Vulnerable Zones; and locations of sensitive facilities. Next,
these layers are intersected to produce composite maps displaying the
areas subject to multiple hazards. Finally, the layers identifying the
locations of residential, commercial, and industrial areas, and
sensitive facilities are overlaid to produce the final maps. The most
common secondary hazards of the events described in Chapter 5 are listed
in Table 6-1.

**Table 6-1**. Secondary Hazards.

  ---------------------- ------------------------------------------------
  Primary Hazard         Secondary Hazards

  Severe storms          Floods, tornadoes, landslides

  Extreme summer weather Wildfires

  Tornadoes              Toxic chemical or radiological materials
                         releases

  Hurricane wind         Toxic chemical or radiological materials
                         releases

  Wildfires              Landslides (on hillsides in later rains)

  Floods                 Toxic chemical or radiological materials
                         releases

  Storm surge            Toxic chemical or radiological materials
                         releases

  Tsunamis               Toxic chemical or radiological materials
                         releases

  Volcanic eruptions     Floods, wildfires, tsunamis

  Earthquakes            Fires, floods (dam failures), tsunami,
                         landslides, toxic chemical or radiological
                         materials releases

  Landslides             Tsunami
  ---------------------- ------------------------------------------------

**Assessing Physical Vulnerability**

Information on hazard exposure needs to be supplemented with information
on physical vulnerability of structures and people. This makes it
necessary to identify the types of structures and populations that are
located in the areas exposed to environmental hazards.

*Structural Vulnerability to Wind, Seismic, and Water Forces*

Structures can be vulnerable to environmental hazards because of
inadequate designs, inadequate construction materials, or both. Older
homes have been constructed under earlier building codes and most
neighborhoods are relatively homogeneous with respect to age, so
identifying older neighborhoods in hazard-prone areas will help set
priorities of emergency management interventions. For example, many
areas of the country have homes, built during the early part of the
20^th^ Century using unreinforced masonry (brick walls constructed
without steel reinforcing rods), that are especially vulnerable to
earthquakes. Similarly, older homes are usually less weathertight, so
they are much more prone to infiltration of hazardous materials.

There are three major issues in assessing structural vulnerability.
First is the question of whether the structure has the strength or
resilience to withstand environmental forces such as wind, seismicity,
or water. In this case, the concern is about the impact on the structure
itself and, consequently, the loss of function and the time and cost of
rebuilding. The second issue concerns the ability of the structure to
protect the contents. This issue is distinct from the first one because
in earthquakes, for example, buildings that survive ground shaking
without damage can transmit the motion to light fixtures, cabinets, and
furniture---possibly damaging these items. The third issue concerns the
ability of the structure to protect the occupants. This is especially
important in connection with hazardous materials because they can
infiltrate into a structure and kill the occupants without damaging the
building.

Once the areas at risk from environmental hazards have been identified,
emergency managers should identify the types of residential, commercial,
and industrial land uses located within them. It is particularly
important to determine if there are any facilities within each VZ that
have highly vulnerable populations. A list of such facilities is listed
in Table 6-2 and important characteristics of the facility users are
listed in Table 6-3. These characteristics make it much more problematic
to evacuate facility occupants than households (Vogt, 1991). In
addition, it is important to identify the location of critical
infrastructure facilities (see Table 6-4).

**[Table 6-2.]{.underline}** Facilities With Highly Vulnerable
Populations.

+-----------------------------------+-----------------------------------+
| HEALTH RELATED                    | RELIGIOUS                         |
|                                   |                                   |
| Hospitals                         | Churches/synagogues               |
|                                   |                                   |
| Nursing homes                     | Evangelical group centers         |
|                                   |                                   |
| Halfway houses (drug, alcohol,    | HIGH DENSITY RESIDENTIAL          |
| mental retardation)               |                                   |
|                                   | Hotels/motels                     |
| Mental institutions               |                                   |
|                                   | Apartment/condominium complexes   |
| PENAL                             |                                   |
|                                   | Mobile home parks                 |
| Jails                             |                                   |
|                                   | Dormitories (college, military)   |
| Prisons                           |                                   |
|                                   | Convents/monasteries              |
| Detention camps                   |                                   |
|                                   | TRANSPORTATION                    |
| Reformatories                     |                                   |
|                                   | Rivers/lakes                      |
| ASSEMBLY & ATHLETIC               |                                   |
|                                   | Dam locks/toll booths             |
| Auditoriums                       |                                   |
|                                   | Ferry/railroad/bus terminals      |
| Theaters                          |                                   |
|                                   | COMMERCIAL                        |
| Exhibition halls                  |                                   |
|                                   | Shopping centers                  |
| Gymnasiums                        |                                   |
|                                   | Central business districts        |
| Athletic stadiums or fields       |                                   |
|                                   | Commercial/industrial parks       |
| AMUSEMENT & RECREATION            |                                   |
|                                   | EDUCATIONAL                       |
| Beaches                           |                                   |
|                                   | Day care centers                  |
| Camp/conference centers           |                                   |
|                                   | Preschools/kindergartens          |
| Amusement parks/fairgrounds/race  |                                   |
| courses                           | Elementary/secondary schools      |
|                                   |                                   |
| Campgrounds/Recreational Vehicle  | Vocational/business/specialty     |
| parks                             | schools                           |
|                                   |                                   |
| Parks/lakes/rivers                | Colleges/universities             |
|                                   |                                   |
| Golf courses                      |                                   |
|                                   |                                   |
| Ski resorts                       |                                   |
|                                   |                                   |
| Community recreation centers      |                                   |
+-----------------------------------+-----------------------------------+

Source: Lindell and Perry (1992).

Of course, the assessment of structural vulnerability usually involves
all three issues. For riverine flooding and hurricane storm surge,
structures---especially concrete structures with well-anchored
foundations---resist battering waves to protect the structure and
provide the height to escape the rising water that could threaten
contents and occupants. In other cases, it is the strength of
construction in resisting wind loads (tornadoes and hurricanes), blast
forces (explosions and volcanic eruptions) and ground shaking
(earthquakes) that protects the structure, contents, and occupants. For
chemical, radiological, and volcanic ash threats, it is the tightness of
construction in preventing the infiltration of outside (contaminated)
air into the structure that is the important protective feature.
Finally, in the case of exposure to a cloud of radioactive material, the
construction material can provide shielding from penetrating radiation
and from surface contamination.

**Table 6-3.** Characteristics of Special Facility Users.

+-----------------+----------------------------------------------------+
| Characteristics | Special Considerations                             |
| Of Users        |                                                    |
+-----------------+----------------------------------------------------+
| Mobility of     | Ambulatory                                         |
| users           |                                                    |
|                 | Require close supervision                          |
|                 |                                                    |
|                 | Nonambulatory                                      |
|                 |                                                    |
|                 | Require life support                               |
+-----------------+----------------------------------------------------+
| Permanent       | Facility residents                                 |
| residence of    |                                                    |
| users           | Residents of the impact area, but *not* of the     |
|                 | facility (e.g., prison guards)                     |
|                 |                                                    |
|                 | Transients                                         |
+-----------------+----------------------------------------------------+
| Periods of use  | Days of week/hours of day                          |
|                 |                                                    |
|                 | Special events                                     |
+-----------------+----------------------------------------------------+
| User density    | Concentrated                                       |
|                 |                                                    |
|                 | Dispersed                                          |
+-----------------+----------------------------------------------------+
| Sheltering in   | Highly effective                                   |
| place           |                                                    |
|                 | Moderately effective                               |
|                 |                                                    |
|                 | Minimally or not effective                         |
+-----------------+----------------------------------------------------+
| Transportation  | Would use own vehicles                             |
| support         |                                                    |
|                 | Require buses or other high occupancy vehicles     |
|                 |                                                    |
|                 | Require ambulances                                 |
+-----------------+----------------------------------------------------+

Source: Lindell and Perry (1992).

In high wind (including tornadoes and hurricanes) and explosions
(usually technological in origin, but also including some volcanic
eruptions), a substantial increase in air pressure can cause structures
to collapse. Such structural failures are caused by deficiencies in
either design or materials, or both (American Institute of Architects,
1995; Institute for Business and Home Safety, 1997).

*Fortified* homes (see www.ibhs.org) provide for installation of
connections and braces to reinforce roofs and gable-end walls against
wind attack. In addition to positive pressure on upwind walls, high wind
creates negative pressure, or suction, as it is forced to flow up and
over the roof. This suction is greatest in flat roofs, intermediate in
gable-end roofs (which slope in two directions), and least in hipped
roofs (which slope in four directions). Suction tends to lift the roof
from the walls unless resisted by adequate connections to the
walls---which in turn, must have adequate connections to the foundation.
Adequate designs and materials also provide protection to building
openings such as windows and doors, thus preventing the wind from
pressurizing the interior and adding to the stress on roof and walls.
The need for window and sliding glass door shutters is widely recognized
because the shutters resist the direct pressure of the wind and the
impact of flying debris. However, door reinforcement is also
important---especially for double-wide (two-car) garage doors that are
highly susceptible to failure because their great width allows the wind
to deflect them inward and pull the rollers out of their tracks.

**Table 6-4.** Infrastructure Facilities.

+-----------------------------------+-----------------------------------+
| Facility Type                     | Examples                          |
+-----------------------------------+-----------------------------------+
| Water                             | Pumping stations and pipelines    |
+-----------------------------------+-----------------------------------+
| Sewer                             | Pumping stations and pipelines    |
+-----------------------------------+-----------------------------------+
| Electric power                    | Generating stations and power     |
|                                   | lines                             |
+-----------------------------------+-----------------------------------+
| Liquid (e.g., oil) and gas (e.g.  | Pumping stations and pipelines    |
| natural gas) fuels                |                                   |
+-----------------------------------+-----------------------------------+
| Telecommunications                | Broadcast studios, transmission   |
|                                   | towers                            |
|                                   |                                   |
|                                   | Cellular telephone towers         |
|                                   |                                   |
|                                   | Telephone switching centers and   |
|                                   | telephone lines                   |
+-----------------------------------+-----------------------------------+
| Transportation                    | Bus/truck terminals and roads     |
|                                   |                                   |
|                                   | Rail yards and rail lines         |
|                                   |                                   |
|                                   | Sea and inland marine ports       |
|                                   |                                   |
|                                   | Airports                          |
+-----------------------------------+-----------------------------------+
| Public safety and health          | Police and fire stations          |
|                                   |                                   |
|                                   | Ambulance garages                 |
|                                   |                                   |
|                                   | Hospitals                         |
+-----------------------------------+-----------------------------------+

Similar observations apply to earthquakes; building damage typically
results from the lateral pressures (ground shaking, surface faulting,
and soil failure) exerted against a structure that was designed
principally to resist the vertical loads resulting from the weight of
the occupants, furniture, upper stories, and roof (American Institute of
Architects, 1992). Rigid structures such as unreinforced masonry are
extremely vulnerable, whereas wood frame dwellings are much safer. In
the latter case, there might be rigid portions of a structure, such as
brick chimneys, that separate from the rest of the structure and
collapse into the living area. In addition, glass from broken windows,
falling pictures and mirrors, the toppling of unsecured furniture, and
other flying debris are safety hazards. For hurricanes, structures on
the open coast must be of sufficiently sturdy construction that they can
resist the direct impact of storm surf as well as the force of extremely
high winds. However, both riverine flooding and hurricane storm surge
also require the structure to have foundations anchored well enough to
resist scouring by water currents that can undermine building
foundations and cause structural collapse. Additional protection can be
provided by expedient floodproofing that uses waterproof construction
materials, sealing of cracks, provision of valves on sewer lines, steel
bulkheads for lower-level openings, and sump pumps to eject seepage (US
Office of Emergency Preparedness, 1972).

As with hurricane surge and riverine flooding, volcanic mudflows and
floods present the challenge of maintaining the integrity of buildings
and their foundation. However, flooding generated by volcanic eruption
commonly contains a substantial volume of rock and ash, resulting in
mudflows that have substantial carrying power (Hays, 1981). Moreover, as
was found during the eruption of Mt. St. Helens, silt buildup can
significantly raise the bed of the river channel (Perry & Lindell,
1990).

Tsunami impact poses an even greater threat than inland flooding, surge
from hurricanes and coastal storms, or volcanic mudflows. As noted in
Chapter 5, these seismic sea waves can threaten areas as much as 100
feet above sea level, so destruction is highly likely for most
structures located very near the shoreline. However, properly designed
steel-reinforced concrete structures located a short distance inland are
likely to survive even the largest tsunamis.

*Human Vulnerability to Inhalation Exposure*

In the case of radiological or toxic materials, the principal public
health hazard arises from inhalation of airborne materials that have an
adverse health effect (US Environmental Protection Agency, 1987).
Inhalation exposures can also result from the dispersion of airborne
debris such as ash and gas from volcanic eruptions. Ideally, an enclosed
space will provide a barrier if it can be closed tightly enough to keep
out the hazardous material and has enough oxygen to sustain those within
it until the danger has passed. Unfortunately, most structures are
leaky, allowing contaminated air to infiltrate even when the doors and
windows are closed. The rate of air exchange increases with the amount
of leakage area, the wind speed, and the temperature differential
between the indoor and outdoor air.

The rate at which indoor and outdoor air are exchanged is commonly
measured in air changes per hour (ACH). However, emergency managers will
find it more useful to think of air exchange in terms of *turnover
time*, which the reciprocal of the air exchange rate, or t~B~ = 1/ACH,
where ACH is the number of air changes per hour. As Wilson (1987)
emphasizes, an infiltration rate of 1.0 ACH does not imply that all the
clean air will be gone in one hour. Rather, the proportion of
contaminated air gradually rises until at the end of 1.0 t~B~ hours, 63%
of the original air has been replaced by contaminated air, while 95% of
the original air has been replaced by the end of 3.0 t~B~ hours. Thus,
for the case of 1.0 ACH, it will take over three hours (not just one
hour) for the indoor air to become almost completely contaminated. This
result is extremely important because it indicates that in-place
sheltering is more effective than most people might infer from the
apparent implication of the number of air changes per hour. The reason
for the difference between the apparent result and the correct result
can best be illustrated by examining the difference between the
apparent, but incorrect, mechanism of air exchange and the actual
mechanism. It would only take one hour to replace the clean air (the
incorrect result) if the contaminated air somehow "pushed out" the clean
air, but this is not what happens. Rather, the contaminated air that
infiltrates into the structure mixes with the clean air rather than
"pushing it out". Clearly, exfiltration of a mixture of clean air and
contaminated air will take longer to exhaust the clean air in a
structure than will exfiltration of ("pushing out") clean air alone.
Consequently, sheltering in-place is at least three times as effective
in reducing inhalation exposure as it first appears to be.

While this time lag effect is important, it is not the only mechanism by
which sheltering in-place can reduce adverse health effects. It is also
important to recognize the impact of a damping effect in reducing the
fluctuations in plume concentrations. As was discussed in the previous
section, these fluctuations arise from irregularities in meteorological
conditions and local terrain. One way of measuring peak concentration is
by estimating the value that is exceeded approximately 1% of the time.
Wilson (1987) reports that in the outdoor (contaminated) air, such 1%
peak concentrations are 400% as large as the mean concentration. For
indoor air, the equivalent peak concentration is only 50% larger than
the mean. As he notes, even when the indoor concentration (100 parts per
million \[ppm\], for example) has risen after six hours to match the
outdoor concentration, the indoor peaks would be expected to be
approximately 150 ppm when the outdoor peaks would be 400 ppm. This is,
of course, of considerable significance when peak concentrations are the
principal health threats.

As Wilson (1987, 1989) and others have observed, a major problem in
assessing the effectiveness of sheltering in-place is uncertainty about
whether indoor air concentrations will remain sufficiently low for a
sufficiently long period of time. This can be answered definitively only
if there is information about the hazardous material being released
(especially the identity of the material released, and the rate and
duration of the release), the meteorological data needed for a
computerized plume dispersion model (wind speed, wind direction, and
atmospheric stability), and the air exchange rates for the structures in
the hazard impact area. Data on the release and the meteorological
conditions will not be available until an incident occurs, but data on
the efficacy of sheltering in-place can be collected in advance. First,
Rogers, et al. (1990) report that energy conservation research has shown
air exchange in most US dwellings ranges from 0.5 to 1.5 ACH. Second,
Wilson (1989) reports that the most important factor affecting leakage
area is the presence of a vapor barrier in the walls and ceiling of a
structure, a feature that is most common in houses built in cold
climates after 1960. Thus, emergency managers could estimate the
effectiveness of sheltering in-place by obtaining access to local data
on the age of the housing stock within different neighborhoods within
their jurisdiction. Finally, the fact that so much of the research and
data on infiltration of hazardous materials has been developed from
studies of energy conservation suggests that emergency planners consult
with their local utilities to determine what information is available
regarding the air exchange rates of different types of structures (e.g.,
residences, schools, and commercial buildings) in their communities.
Special facilities, especially those such as hospitals that have low
mobility residents, should be examined individually to assess their air
exchange rates.

*Human Vulnerability to Radiological Materials*

Although both toxic and radiological materials present an inhalation
hazard, a plume of radioactive material released from a nuclear power
plant or during a transportation accident also can cause harm by means
of external gamma radiation from the cloud and from ground
contamination. Dense building materials such concrete, brick, and stone
provide shielding from external gamma radiation and, thus, can provide a
basis for in-place sheltering during radiological emergencies. The
effectiveness of structures made from different types of building
materials has been examined in studies by Burson and Profio (1977), Anno
and Dore (1978a; 1978b), Aldrich, Ericson and Johnson (1978), and
Aldrich, et al. (1982). These studies calculated the dose reduction
factors (the ratio of the dose received while sheltering to the
unprotected dose) for three exposure routes: external gamma radiation
from the cloud, external gamma radiation from ground contamination, and
inhalation of radioactive materials infiltrating into the structure.
Burson and Profio (1977) found that sheltering in a wood frame dwelling
provides little more protection from cloud and ground exposure than does
"sheltering" in a vehicle while evacuating. Sheltering on the ground
floor of a masonry home with no basement or in the basement of a wood
frame home gave considerably higher levels of protection: about 50% of
the unprotected exposure to the cloud and less than 20% of the
unprotected exposure to ground. As one might expect, the basement of a
masonry house was even more effective: 40% of the cloud exposure and 5%
of the ground exposure. A large office building was the most effective
shelter of all, reducing cloud exposure to about 20% and ground exposure
to 1%.

The importance of the construction materials is underscored by Burson
and Profio\'s (1977) work indicating it is the cloud exposure that
produces most of the whole body radiation dose received by those
sheltering in a home. Infiltration into the structure would account for
only about 5% of the gamma radiation dose. Anno and Dore (1978a)
calculated cloud dose reduction factors for single family dwellings and
large structures (e.g., office buildings, multistory apartment
complexes). They considered 0.125 to 3 ACH to define the range of
infiltration rates for single family dwellings and other structures that
could be used as temporary public shelters. For single family dwellings,
whole body dose reduction factors for low air exchange rates (0.125 ACH)
were calculated to be 0.40-0.33 compared to 0.43 for more representative
air change rates (3 ACH). For large structures, whole body dose
reduction factors for low air change rates were calculated to be 0.08
compared to 0.17-0.11 for the more representative air change rates.
These investigators also estimated thyroid (inhalation) dose reduction
factors to be about 0.05 to 0.01 for low air change rates and from 0.25
to 0.10 for more representative air change rates for either single
family dwellings or large structures.

More recent analyses by the NRC provide an indication of vulnerability
to radiological materials releases. The results of these analyses can be
seen in Figure 6-3, which displays the relative effectiveness of
continuing normal activity, sheltering in a home basement or large
building, or evacuating before or after plume arrival at three distances
from the plant. According to this figure, which is adapted from McKenna
(2000), even a late evacuation is better than home shelter, large
building shelter is better than late evacuation, and early evacuation is
best of all. Thus, those who are within five miles of a nuclear power
plant have high vulnerability if they remain in their homes during a
release. Only large building shelter provides as much safety as early
evacuation.

![](media/image3.wmf)**Figure 6-3.** Effectiveness of Protective Actions
in a Nuclear Power Plant Emergency.

1 Normal activity 4 Evacuation 1 hour before plume arrival

2 Home basement shelter 5 Evacuation after plume arrival

3 Large building shelter

Source: Adapted from McKenna (2000).

*Assessing Agricultural and Livestock Vulnerability*

Assessing the physical vulnerability of crops and livestock is a task
that is rarely considered to be the responsibility of emergency
managers. One reason for giving minimal emphasis to the agricultural
sector is that it accounts for a relatively small part of the total
vulnerability in many jurisdictions. In those cases where the
agricultural sector is a significant part of the local economy,
emergency managers should consult agricultural experts such as those
from the US Department of Agriculture because, as noted earlier, there
is substantial variation among animal and plant species in their
susceptibility to extreme environmental conditions. For example, fruit
orchards can be devastated by wind speeds that have no impact whatsoever
on rangeland. Moreover, the damage to many crops depends on the stage in
growth cycle---with some crops having minimal susceptibility to wind
damage until just before harvest.

**Assessing and Mapping Social Vulnerability**

In contrast to physical vulnerability, which arises from the potential
for environmental extremes to create adverse physiological changes,
social vulnerability arises from the potential for these extreme events
to cause changes in people's behavior. People can vary in their
potential for injury to themselves and their families. They also vary in
the potential for destruction of their homes and workplaces, as well as
the destruction of the transportation systems and locations for shopping
and recreation they use in their daily activities. The discussion below
emphasizes census data but it also is important to examine other
archival sources such as school records, immigration services, local
aging agency, special needs registries, property tax records, facilities
locations (Enarson, et al., 2003). In addition, consult local social
service providers (government and NGO) and churches to identify
vulnerable populations.

*Assessing Psychosocial Vulnerability*

One important component of psychological vulnerability is personal
fragility---that is, a lack of *emotion-focused* coping skills. Another
component of psychological vulnerability is rigidity---that is, a lack
of *problem-focused* coping skills defined by an inability to develop
adaptive strategies for responding to altered conditions. Ozer and
Weiss's (2004) summary of research on post-traumatic stress disorder
(PTSD) concluded the four categories of PTSD predictors were

-   A person's pre-existing characteristics (e.g., intelligence,
    previous psychological trauma),

-   The severity of the personal impact of the disaster,

-   Psychological processes immediately after the impact, and

-   Life stress and social support after the traumatic event.

Quite obviously, only the first of these categories can measure
psychological vulnerability that exists before a disaster strikes and
none of the variables in this category is routinely available through
secondary sources such as Census data. Because direct measures of the
incidence of PTSD predictors (e.g., through community surveys) are
prohibitively expensive, psychological vulnerability must be measured
indirectly, as discussed in a later section.

A major concern is social isolation. Thus, vulnerability is also
measured by the infrequency and superficiality of social contacts with
peers such as kin (extended family), neighbors, and coworkers. Routine
measures of social vulnerability are rarely available through surveys
conducted using representative samples of community members. However,
there are proxy variables that have statistically significant---although
admittedly small---correlations with social isolation. Suitable proxy
variables that are routinely available through Census files include age,
income, and ethnicity. Specifically, increasing age is associated with
reduced levels of community participation (involvement in voluntary
associations) and immersion in kin and friendship networks (Perry, 1985;
Perry, et al., 1981). By contrast, socioeconomic status is positively
associated with participation in community organizations (Alvirez &
Bean, 1976; Tomeh, 1973) and minority ethnicity is positively associated
with immersion in kin and friendship networks (Staples, 1976; Wilkinson,
1999). Accordingly, the use of age, socioeconomic status, and ethnicity
as proxy measures of psychosocial vulnerability will also be discussed
below.

*Assessing Demographic Vulnerability*

Vulnerability to demographic changes follows from the demographic
balancing equation discussed earlier. Until 2005, recent trends in
disaster casualties had indicated the number of deaths would be
relatively small for any North American disaster. It is unlikely that
Hurricane Katrina marks a reversal of that trend because New Orleans is
the only major coastal city with a significant portion of its land area
(and, thus, its population) below sea level. In most disasters, it is
the number of in-migrants and out-migrants cause significant changes in
its demographic composition. Once again, census data can be used to
provide indicators such as age, income, homeownership, and ethnicity.
Older, more affluent homeowners are likely to have high levels of
community bondedness (Turner, et al., 1986) and seek permanent housing
in the community even if their homes have been destroyed. Similarly,
ethnic minorities have tightly integrated kin networks that make them
stay. However, even otherwise stable communities are likely to
experience short term changes in their demographic composition if there
are few rental vacancies after a disaster. This is because a local
housing shortages would require residents to move farther away for
temporary housing. Similarly, a community's demographic composition is
likely to change if local businesses have high levels of physical
vulnerability to disaster impacts (making them likely to shut down for
extended periods). A declining local economy will make them financially
vulnerable and, thus, more likely to cease operations altogether.

*Assessing Economic Vulnerability*

It is obvious that wealth is a major component of economic
vulnerability, but the assets comprising wealth vary in their
vulnerability to disasters. Tangible assets such as buildings,
equipment, furniture, and vehicles that are located in the disaster
impact area are more vulnerable than financial assets such as bank
accounts, stocks, and bonds that are recorded electronically. Households
and businesses both have tangible and financial assets, so both are
vulnerable to the loss of their tangible assets and both have financial
assets that can be used to support disaster recovery. Of course, there
are substantial variations among households in their assets and the same
is true for businesses.

One noteworthy difference between households and businesses is that the
latter also have operational vulnerability arising from dependency upon
those who supply its inputs (suppliers and labor) as well as those who
purchase its outputs (distributors and customers). Evidence of
businesses' operational vulnerability to input disruptions can be seen
in data provided by Nigg (1995), who reported that business managers'
median estimate of the amount of time that they could continue to
operate without infrastructure was 0 hours for electric power, 4 hours
for telephones, 48 hours for water/sewer, and 120 hours for fuel. If
this infrastructure support is unavailable for time periods longer than
these, then businesses must suspend operations even if they have
suffered no damage to their structures or contents.

Measures of household wealth are not available, but data on household
income are available in census files. Available census data on
businesses are more limited in their relevance to economic
vulnerability. The Census Bureau's Web site (censtats.census.gov)
provides ZIP code-level data on the number of businesses in each
economic sector, broken down by number of employees. These data can be
overlaid onto risk areas for different hazards such as 500-year
floodplains, hurricane surge zones, or earthquake seismic zones to
develop estimates of the community's economic vulnerability to disaster
impact.

*Assessing Political Vulnerability*

As will be discussed later, political impacts of disasters often arise
from conflicts over the management of the emergency response and
disaster recovery. Accordingly, political vulnerability arises from
inadequate emergency management interventions---which create situations
that pit one group of stakeholders against another---and inadequate
mechanisms for managing this conflict when it does arise. The adequacy
of emergency management interventions to reduce hazard vulnerability
will be addressed in Chapters 9-12, but the adequacy of mechanisms for
managing conflict is a crucial part of civil governance. In particular,
government agencies that are believed to lack legitimacy, expertise, and
adequate information for making decisions about the allocation of public
resources will prove vulnerable in the aftermath of disaster. As is the
case with psychological, demographic, and economic vulnerability, there
currently are no direct measures of political vulnerability that are
readily available for use by emergency managers. Consequently, the use
of age, socioeconomic status, and ethnicity as proxy measures of
political vulnerability will also be discussed in the next section.

*Predicting Household Vulnerability*

As noted earlier in this chapter, it is important to recognize that
social vulnerability is not randomly distributed either demographically
or geographically. In particular, the social vulnerability arising from
a lack of psychological resilience, social network integration, economic
assets, and political power vary across demographic groups. Some of
these components of social vulnerability can be predicted by demographic
characteristics such as gender, age, education, income, and ethnicity.
Moreover, these demographic groups tend to be distributed relatively
systematically across the landscape of each community. Even though there
might not be sharp geographic lines of demarcation between the locations
of different demographic groups within a community, there are variations
in the concentration of these groups in different neighborhoods. Thus,
GISs can be used to conduct disaggregated (e.g., census tract-level)
spatial analyses to identify the demographic segments most likely to be
vulnerable to disaster impacts.

The demographic predictors (e.g., gender, age, education, income, and
ethnicity) of social vulnerability are frequently also associated with
hazard exposure because the population segments with the fewest
psychological, social, economic, and political resources often
disproportionately occupy the most hazardous geographical areas.
Similarly, demographic predictors of social vulnerability are often
associated with structural vulnerability because those same population
segments disproportionately occupy the oldest, most poorly maintained
buildings. Thus, those who are most socially vulnerable are also likely
to experience the greatest physical impacts such as casualties and
property loss.

Because emergency managers rarely have access to direct measures of
social vulnerability, geographic analyses of social vulnerability are
conducted on Census data, preferably at the lowest possible level of
aggregation (e.g., block-group or tract). Recent research has shown
these aggregated indicators of social vulnerability are strongly
correlated, so it is advisable to use either a composite measure of
social vulnerability or a subset of these indicators. Table 6-5 lists a
sample set of social vulnerability indicators recently used in analyses
of social vulnerability to the impacts of earthquakes in Shelby County
(Memphis), Tennessee (Prater, et al., 2004).

Based on the recognition, described above, that hazard exposure,
structural vulnerability, and social vulnerability tend to be related,
Prater and her colleagues advocated identifying *vulnerability
hotspots*---the geographic areas occupied by demographic segments that
are most vulnerable to disaster impacts. These vulnerability hotspots
can be identified by using a GIS to either overlay or mathematically
combine data on hazard exposure (e.g., ground motion and ground failure
from earthquakes), structural vulnerability (e.g., due to dilapidated
housing), and lifeline vulnerability (e.g., due to old and poorly
maintained water, sewer, natural gas, electric power, and telephone
lines and streets, viaducts, and bridges). This concept of vulnerability
hotspot analysis is illustrated in Figure 6-4, below.

**Vulnerability Dynamics**

A major challenge for emergency managers is to understand the processes
by which communities increase or decrease their hazard exposure,
physical vulnerability, and social vulnerability. According to economic
theory, excessive hazard exposure and structural vulnerability arise
from systemic complexities that can be characterized as *market
failures* such as inadequate information, barriers to market entry and
exit, and capital flow restrictions (Kunreuther, 1998; Lindell, et al.,
1997). An ideal pattern of economic development would be one in which
risk area occupants purchase property on the basis of adequate
information about hazard exposure and structural vulnerability.
Moreover, they would locate only where it was economically advantageous
in the long term as well as in the short term, and would diversify their
assets over other locations and other forms of financial (e.g., savings
accounts, insurance, stocks/bonds) and social (e.g., extended family)
recovery assistance. Finally, risk area occupants would adopt hazard
adjustments to limit their losses if a disaster were to strike. These
adjustments would include hazard mitigation (e.g., land use practices
and building construction practices), emergency preparedness practices
(e.g., detection and warning systems), and recovery preparedness
practices (e.g., diversified investments and hazard insurance) to avoid
casualties and property damage.

**Table 6-5**. Indicators of Social Vulnerability.

+-------------------------+--------------------------------------------+
| Vulnerable Groups       | Vulnerability Indicators                   |
+-------------------------+--------------------------------------------+
| Female headed           | Percent female headed households           |
| households              |                                            |
+-------------------------+--------------------------------------------+
| Elderly                 | Percent individuals over 65                |
|                         |                                            |
|                         | Percent of elderly households              |
+-------------------------+--------------------------------------------+
| Low income/high poverty | Percent of households below poverty level  |
|                         |                                            |
|                         | Percent of households below HUD standards  |
+-------------------------+--------------------------------------------+
| Renters                 | Percent of households residing in rental   |
|                         | housing                                    |
|                         |                                            |
|                         | Percent of households residing in rental   |
|                         | housing by type of dwelling units          |
+-------------------------+--------------------------------------------+
| Ethnic/racial/language  | Percent of individual from Black,          |
| minorities              | Hispanic, and other minorities             |
|                         |                                            |
|                         | Percent of non-English speakers            |
+-------------------------+--------------------------------------------+
| Children/youth          | Percent of population in selected age      |
|                         | groupings                                  |
|                         |                                            |
|                         | Percent of households with dependency      |
|                         | ratios above a specified level             |
+-------------------------+--------------------------------------------+
| Social vulnerability    | Areas with combined social vulnerabilities |
| hot spot analysis       |                                            |
+-------------------------+--------------------------------------------+

Actual patterns of development are significantly different from the
ideal. In many cases, there is migration to hazard-prone areas because
of beneficial land uses for agriculture, transportation, and recreation
(i.e., people are "pulled in", Bolin & Bolton, 1986). This is compounded
by a lack of accountability for investment decisions. Developers are at
risk for only a short period of time before they pass an investment on
to others (homeowners, insurers, mortgage holders) who will ultimately
experience the disaster impact. Such transactions can occur because many
risk area residents are new arrivals who are unaware of the hazard. Even
long-term residents of risk areas sometimes have little or no
information about hazards and adjustments to those hazards because such
information is suppressed by those with a major stake in the community's
economic development (Meltsner, 1979). Even when there is local
knowledge about hazards, there often is a lack of hazard intrusiveness
because events that are not recent or frequent tend not to be thought
about or discussed (Lindell & Prater, 2000). Moreover, many people
ignore low probability events, think of them as occurring far in the
future, or have an *optimistic bias* that the negative consequences of
these events will not happen to them (Weinstein, 1980). In particular,
politicians tend to ignore consequences that they expect to occur only
after their term of office is over, so only frequent, recent, or major
impacts lead to increased adoption of community-wide hazard adjustments
such as land use controls or more stringent building codes. Even then,
the *window of opportunity* for the adoption of these adjustments is
open only temporarily (Birkland, 1997; Prater & Lindell, 2000).

[**Figure 6-4.** Disaster Impact Vulnerability Assessment
Model.]{.underline}

Source: Prater, et al. (2004)

Increased hazard exposure also is caused by displacement from safer
areas due to population pressures (i.e., people are "pushed in"). When
this occurs, the demographic distribution of risk tends to be
inequitable because geographical locations often are systematically
related to their residents' demographic characteristics---especially
their (lack of) economic and political power to decrease hazard
vulnerability. This pattern is very common in developing countries such
as Brazil, where *favelas* are located in flood plains and on
landslide-prone slopes because the residents cannot afford to purchase
homes in safer areas.

There also are problems in the adoption of effective hazard adjustments.
One of these arises from households' and businesses' concentration of
hazard exposure (i.e., having physical and financial assets located
*only* in the risk area). Diversification is an effective way of
avoiding concentration of hazard vulnerability, but low-income
households and small businesses often have so few physical or financial
assets that they cannot afford to locate some of them in safer areas.
Hazard insurance is problematic because it tends to suffer from *adverse
selection*, which means that only those who are at the greatest risk are
likely to purchase it (Kunreuther, 1998). Moreover, the actions of one
party can sometimes increase the vulnerability of another. In
floodplains, upstream development cuts down trees and replaces it with
hardscape, thus increasing the speed of rainfall runoff and downstream
flooding. Technological protection works such as dams and levees can
offset such increases in hazard exposure, but many risk area occupants
overestimate the effectiveness of such hazard adjustments (Harding &
Parker, 1974). This can cause further development of floodplains and,
thus, increased hazard exposure that exceeds the risk reduction provided
by the adjustment that was adopted.

######## Conducting HVA with HAZUS-MH

HAZUS-MH (HAZards US-Multi Hazard) is a computer program that models
potential losses from earthquakes, floods, and hurricane winds. HAZUS-MH
uses GIS software to analyze and display data on estimated structural
damage and economic loss estimates for buildings and infrastructure. It
also provides estimates of the casualties resulting from earthquakes.
HAZUS-MH can be used to conduct analyses in support of hazard
mitigation, emergency preparedness, and recovery preparedness planning.
In addition, HAZUS-MH can be used to conduct rapid analyses in support
of postimpact emergency response and disaster recovery operations.

HAZUS-MH supports three levels of analysis. A Level 1 analysis uses
national average data to produce approximate results. Accordingly, a
Level 1 analysis is best considered to be an initial screen that
identifies the communities at highest risk. A Level 2 analysis takes
refined data and hazard maps provided by the user to produce more
accurate estimates. Input for a Level 2 analysis is obtained from local
emergency managers, urban and regional planners, and GIS professionals.
A Level 3 analysis uses community-specific parameters to produce the
most accurate loss estimates. Input for a Level 3 analysis is obtained
from structural and geotechnical engineers, as well as other technical
experts to examine threats such as dam breaks and tsunamis.

Data input to HAZUS-MH is supported by the Inventory Collection Tool
(InCAST), a Building Inventory Tool (BIT), and Flood Information Tool
(FIT). InCAST is a database that is designed to support the management
of local building data needed for Level 2 and Level 3 analyses. BIT
supports the importation of building data from large files (e.g., over
100,000 records from a tax assessor data file). FIT allows users to
transform flood data to the HAZUS flood model's required format.

HAZUS-MH has separate models for earthquakes, floods, and hurricane
winds. The earthquake model accounts for ground motion and ground
failure; the flood model accounts for flood frequency, depth, and
discharge velocity. The hurricane model accounts for wind pressure,
missile damage, and rain.

Direct damage can be calculated for the general building stock,
essential facilities, high potential loss facilities, transportation
facilities, and lifelines. Induced damage can be estimated for fire
following, hazmat release, and debris generation. Direct losses can be
estimated for the cost of repair, income loss, crop damage, casualties,
shelter needs, and recovery needs. Indirect losses include supply
shortages, sales declines, opportunity costs, and economic losses. These
impact modules are most complete for earthquake (only the crop loss
module is unavailable) and floods (only the fire following and
casualties modules are unavailable). The hurricane model has the fewest
features (direct damage to the general building stock, essential
facilities and high potential loss facilities, induced damage from
hazmat release, debris generation, direct losses from cost of
repair/replacement, shelter needs, and recovery needs).

HAZUS-MH can be used in multihazard analyses that provide average
annualized loss and probabilistic results from the three hazard models
(earthquake, flood, and wind). HAZUS-MH can also link to external models
for blast, radiological, chemical, and biological hazards. Further
information about HAZUS-MH is available from the HAZUS Resource Center
located at www.fema.gov/hazus. This source includes data on HAZUS-MH
hardware and software requirements, manuals, case studies, and contacts
for membership in users groups.

**The Community Vulnerability Assessment Tool**

The NOAA Coastal Services Center also provides guidance on conducting
community vulnerability assessment on their website
www.csc.noaa.gov/products/nchaz/startup.htm. First, the *hazard
identification module* asks the user to list the community's hazards and
rate each of them on a 1-5 scale in terms of its frequency and area
impact (these scores are added together) and potential damage magnitude
(this score is multiplied by the sum of the previous two factors) to
produce a total score for each hazard. Second, the *hazard analysis
module* recommends using risk maps to identify areas within the user's
jurisdiction that vary in their degree of risk from each hazard and to
rate each of these areas on a 1-5 scale in terms of its risk. Third, the
*critical facilities analysis* module prompts the user to identify the
community's critical facilities and develop an inventory listing each
facility's type, name, address, and other important information. This
module concludes by identifying facilities located in the highest risk
areas (which were identified in the hazard analysis module). Fourth, the
*societal analysis module* is designed to identify neighborhoods
requiring special consideration because they are high in social
vulnerability (refer to Table 6-5 for sample criteria). This module
continues with an analysis of the neighborhoods located in the highest
risk areas (which were identified in the hazard analysis module). The
module concludes with an inventory of the special consideration/high
risk locations listing the neighborhood name, location, and other
important information such as the number of households and the nature of
their needs. Fifth, the *economic analysis module* identifies the
principal economic sectors in the community and their geographic
locations. This module continues with an analysis of the identification
of the businesses that are located in the highest risk areas (which were
identified in the hazard analysis module). The module concludes with an
inventory of economic sector/high risk locations listing the economic
sector name, location, and other important information such as the
number of businesses, individual businesses having the most employees,
and the nature of these businesses' vulnerabilities (e.g., the length of
time they can operate without infrastructure). Sixth, the *environmental
analysis module* prompts users to identify locations of secondary
hazards (e.g., hazardous materials) and key environmental resources
(e.g., wetlands). This module continues with an analysis of the
identification of the secondary hazards and key environmental resources
that are located in the highest risk areas (identified in the hazard
analysis module). The module concludes with an inventory of locations at
which secondary hazards located in the primary hazard areas could affect
key environmental resources by their proximity. The module concludes
with an inventory of secondary hazard sites and their addition to the
critical facilities list. Seventh, the *mitigation opportunities
analysis* identifies undeveloped land in high hazard areas to support
development of mitigation strategies focused on new development. It also
encourages review of the community's status in the National Flood
Insurance Program (NFIP).

The Coastal Services Center Web site also contains an overview of LIDAR
(LIght Detection And Ranging) beach mapping to obtain highly accurate
elevation data. It also describes a damage assessment tool for rapid
postimpact that allows personnel to retrieve parcel data in a GIS
database and integrate it with FEMA damage assessment forms. Finally,
the Web site also explains how remote sensing can be used to provide
broad area views of the impact area after a disaster strikes.

**Analyzing and Disseminating Hazard/Vulnerability Data**

The widespread availability of powerful desktop computers provides an
important method for conducting hazard/vulnerability analyses in
identifying areas at risk (Dash, 1997; Griffith, 1986; Berke, Larsen and
Ruch, 1984) and projecting the damages resulting from a major incident
(French, 1986; Haney, 1986; Scawthorne, 1986). To accomplish these
tasks, emergency managers can use software such as GIS (Environmental
Systems Research Institute, 2000), CAMEO (National Safety Council,
1995), ALOHA (FEMA, no date, a), and HAZUS (National Institute for
Building Sciences, 1998).

In addition, desktop computers provide emergency managers with a
powerful tool for obtaining hazard/vulnerability data from the Web sites
of many government agencies, university research centers, private sector
organizations, and NGOs. In particular, the Internet has become an
important means of obtaining the data needed for conducting HVAs and
disseminating their results---a technological development that is
important for three reasons. First, federal and state agencies have
generated many hazard analysis documents, maps, and databases that
already are in digital form and are available to put onto Web sites.
Second, SEMA Web sites can be linked electronically to other
organizations' Web sites, thus allowing users to immediately access
additional hazard analysis information that might otherwise take months
to obtain if they were to request it in paper copy. Third, hazard
analyses disseminated over the Internet can be updated frequently and,
by avoiding the printing costs associated with hundreds of paper copies,
can be disseminated less expensively.

*Analyzing Hazard/Vulnerability Data*

Despite the great promise of computers in analyzing and disseminating
hazard/ vulnerability data, there is little documentation of the extent
to which these tools are actually being used. Some indication of the
degree to which progress remains to be made can be seen in Lindell and
Perry's (2001) data from LEPC Chairs in Illinois, Indiana, and Michigan
indicating that only 59% of the LEPCs had calculated VZs around their
communities' hazmat facilities. Of those who had calculated VZs, only
36% had used computer models such as CAMEO (National Safety Council,
1995) or ARCHIE (Federal Emergency Management Agency, no date, a) to
perform the analyses. Thus, only a small fraction of the LEPCs used
computer-based methods to calculate VZs. In addition, there were
differences among types of computer use, with some LEPCs using
computerized databases more extensively for the management of data on
chemical hazards (i.e., chemical inventories at local plants) and
community emergency response resources than did other jurisdictions.
Thus, these data, though very limited in scope, indicate local emergency
management agencies have a long way to go in using emergency management
information technology to its fullest advantage.

*SEMA Dissemination of Hazard/Vulnerability Data Via Web Sites*

A recent examination of SEMAs' Web sites revealed that most SEMAs
provide some hazard analysis information (Hwang, et al., 2001). The most
commonly addressed hazards on SEMA Web sites were hurricane, earthquake,
flood, fire, tornado, hazardous material, storm, terrorism, drought, and
radiological material (see Table 6-6). This list includes some of the
most significant hazards, as indicated by the fact that of the 468
Presidential Disaster Declarations between January, 1992 and September
1999, 172 were for storms, 170 for floods, 58 were for tornadoes, 37
were for hurricanes, 17 were for blizzards, 5 each were for fires and
earthquakes, and 4 were for landslides. However, many states that are
vulnerable to these hazards failed to address them and there are other
hazards that were not addressed on SEMAs' Web sites that also should
receive attention.

**Table 6-6**. Hazard Agents and their Frequency of Mention on SEMA Web
Sites

  ---------------- --------- --------- ----------------- --------- ---------
  Hazard Agent     Records   Percent   Hazard Agent      Records   Percent

  Hurricane        50        15.6%     Heat              8         2.5%

  Earthquake       40        12.5%     Structure failure 6         1.9%

  Flood            33        10.3%     Tsunami           4         1.2%

  Fire             29        9.0%      Landslide         3         0.9%

  Tornado          29        9.0%      Explosion         3         0.9%

  Hazardous        27        8.4%      Avalanche         2         0.6%
  material                                                         

  Storm            22        6.9%      Erosion           2         0.6%

  Terrorism        15        4.7%      Blight            1         0.3%

  Drought          14        4.4%      Freeze            1         0.3%

  Radiological     13        4.0%      Meteor            1         0.3%
  material                                                         

  General          9         2.8%      Pollution         1         0.3%

  Volcano          8         2.5%      Total             321       100.00
  ---------------- --------- --------- ----------------- --------- ---------

Source: Hwang, et al. (2001).

This study also identified 174 linkages from SEMA Web sites to secondary
sources. Table 6-7 shows the Web sites for FEMA (28 links), National
Oceanographic and Atmospheric Administration (18 links) and US
Geological Survey (13 links) received the largest number of links.
Another 19 sites received between two and five links from SEMA Web sites
and just over 40% of the secondary sources identified in the study
received links from only a single SEMA web site. Taken together, the
data from Tables 6-6 and 6-7 indicate that there is a very erratic
pattern to the dissemination of hazard analysis information on SEMA Web
sites.

[**Table 6-7.** Percentage of all Links Accounted for by Each Secondary
Source.]{.underline}

  ---------------------------------------- --------------------- ---------
  Secondary Source                         Web Address (URL)     Percent

  ---------------------------------------- --------------------- ---------

Source: Hwang, et al. (2001).

+---------------------------------------+--------------------+--------+
| Federal Emergency Management          | www.fema.gov       | 16.09  |
| Administration                        |                    |        |
+---------------------------------------+--------------------+--------+
| National Oceanographic and            | www.noaa.gov       | 10.34  |
| Atmospheric Administration            |                    |        |
+---------------------------------------+--------------------+--------+
| US Geological Survey                  | www.usgs.gov       | 7.47   |
+---------------------------------------+--------------------+--------+
| American Red Cross                    | www.redcross.org   | 2.87   |
+---------------------------------------+--------------------+--------+
| US Department of Health and Human     | www.hhs.gov        | 1.72   |
| Services                              |                    |        |
+---------------------------------------+--------------------+--------+
| US Forest Service                     | www.fs.fed.us      | 1.72   |
+---------------------------------------+--------------------+--------+
| Weather Underground                   | ww                 | 1.72   |
|                                       | w.wunderground.com |        |
+---------------------------------------+--------------------+--------+
| Centers for Disease Control and       | www.cdc.gov        | 1.15   |
| Prevention,                           |                    |        |
+---------------------------------------+--------------------+--------+
| Department of Transportation          | hazmat.dot.gov     | 1.15   |
+---------------------------------------+--------------------+--------+
| Environmental Protection Agency       | www.epa.gov        | 1.15   |
+---------------------------------------+--------------------+--------+
| Florida Division of Forestry          | fl-dof.com         | 1.15   |
+---------------------------------------+--------------------+--------+
| Institute of Global Environment and   | grads.iges.org     | 1.15   |
| Society                               |                    |        |
+---------------------------------------+--------------------+--------+
| National Drought Mitigation Center    | drought.unl.edu    | 1.15   |
+---------------------------------------+--------------------+--------+
| National Fire Protection Association  | www.nfpa.org       | 1.15   |
+---------------------------------------+--------------------+--------+
| New England States Emergency          | www.nesec.org      | 1.15   |
| Consortium                            |                    |        |
+---------------------------------------+--------------------+--------+
| North Carolina Emergency Management   | www.d              | 1.15   |
| Agency                                | em.dcc.state.nc.us |        |
+---------------------------------------+--------------------+--------+
| US Nuclear Regulatory Commission      | www.nrc.gov        | 1.15   |
+---------------------------------------+--------------------+--------+
| Oklahoma Mesonetwork                  | www.mesonet.ou.edu | 1.15   |
+---------------------------------------+--------------------+--------+
| Stormdisplay.com                      | ww                 | 1.15   |
|                                       | w.stormdisplay.com |        |
+---------------------------------------+--------------------+--------+
| Tornado Project                       | www.               | 1.15   |
|                                       | tornadoproject.com |        |
+---------------------------------------+--------------------+--------+
| University of Illinois Dept. of       | www.atmos.uiuc.edu | 1.15   |
| Atmospheric Sciences                  |                    |        |
+---------------------------------------+--------------------+--------+
| Weather Channel                       | www.weather.com    | 1.15   |
+---------------------------------------+--------------------+--------+
| ######### Total                       |                    | 59.20  |
+---------------------------------------+--------------------+--------+

Curiously, there was not a very strong correspondence between states'
hazard exposure and their SEMA Web sites' dissemination of information
about those hazards. Specifically, analysis of six highly probable or
highly damaging hazards (storm, flood, tornado, hurricane, earthquake,
and landslide) shows substantial variation across states and hazards in
the degree to which hazard analysis information on SEMA Web sites
corresponded to those states' hazard vulnerability and recent disaster
experience. Table 6-8 indicates most SEMAs failed even to address many
common hazard agents on their Web sites and less common hazards such as
tsunami, structure failures, landslides, and avalanche are generally
neglected even though they also have the potential for significant
impacts. Some plausible explanations for the inadequate coverage of
these hazard agents in SEMA Web sites are addressed below.

*Some EMAs may lack the resources to deliver information through the
Internet.* Hwang and his colleagues (2001) found two SEMAs had no Web
sites, but this is likely to be a problem affecting other EMAs as well
because Drabek (1991d) found insufficient technical staff, as well as
hardware and software limitations, are impediments to emergency
management agency adoption of emergency management information
technology. Drabek's findings are consistent with the data from Hwang,
et al (2001), which indicate SEMAs with little or no hazard information
on their Web sites tend to be those from rural states with small
budgets. Thus, they have few financial resources for purchasing computer
hardware and software and either hiring computer support staff or
contracting with outside organizations for Web site development. One
possible way of overcoming the problem of staff limitations would be for
national- or state-level professional organizations such as NEMA or IAEM
to establish basic guidance for the development and maintenance of
hazard analysis Web sites. The availability of hazard analysis
information on SEMA Web sites could be significantly improved if one of
these professional organizations were to establish a list of qualified
contractors that could work with the states to upgrade their Web sites.
A longer-term solution would be for universities offering programs in
emergency management to emphasize emergency management information
technology within their curricula so that state and local agencies would
have a pool of applicants who are technically qualified in this area.

**Table 6-8**. Relationship between Hazard Exposure and Web Site
Records.

  ------------ --------- -------------- ------------- ------------- -------------
  Hazard       Number of Number of      Number of     Correlation   Correlation
               exposed   states with    states with   of exposure   of disasters
               states    major disaster hazard        with hazard   with hazard
                         declarations   analysis      analysis      analysis
                                        records       records       records

  Storm        28        47             15            -0.12         -0.04

  Flood        25        46             18            0.03          -0.08

  Tornado      23        24             17            0.22          0.17

  Hurricane    13        17             12            0.53\*\*      0.85\*\*

  Earthquake   16        2              16            0.38\*        0.16

  Landslide    22        3              1             -0.12         -0.03
  ------------ --------- -------------- ------------- ------------- -------------

Source: Lindell, et al. (2002). *\*p* \< .05; \*\* *p* \< .01

*Some EMAs may think a hazard analysis Web site is unnecessary because
people already know about most common hazards such as storms and
floods.* This rationale is entirely inappropriate because people
frequently have inaccurate beliefs about hazards, misjudge their
personal vulnerability, and lack information about methods of protecting
themselves (Lindell & Perry, 2000; Lindell & Barnes, 1986; Slovic, et
al., 1974; Whitney, et al., 2004). Indeed, it is precisely because local
emergency managers and the public lack accurate hazard information that
federal agencies such as FEMA and USGS disseminate this information.

*Some EMAs may not believe that the Internet is an effective method of
disseminating hazard analysis information.* There is some validity to
this belief because Internet access, though extensive, is far from
universal for local emergency managers and the public. Nonetheless,
Internet access is rapidly becoming more widespread and the number of
people who can be reached by this communications medium is becoming
increasingly large. This is especially true among younger population
segments that have spent their entire lives using computers. Moreover,
*cost-effectiveness* is a major incentive for EMAs to increase their use
of the Internet. EMAs bear essentially no reproduction or distribution
costs because users pay their Internet Service Providers for access to
the Web site and there is a cost of printing only if the users decide
hard copy is needed. Finally, electronic dissemination is advantageous
for local emergency managers because they can "cut and paste" portions
of the hazard analysis information from the SEMA Web site into their own
local hazard/vulnerability analyses.

*Some EMAs may provide little hazard information on their Web sites
because they already distribute this information through other media.*
This rationale has some validity because, for example, many states that
are vulnerable to hurricanes distribute brochures containing maps of
risk areas and hurricane survival tips. However, this explanation
ignores the fact that hazard analysis information developed for
distribution through other channels can be adapted quite readily and
inexpensively to a Web site. Similarly, hazard analysis information
received from other sources (e.g., Red Cross disaster preparedness
brochures) is becoming available on those organizations' Web sites and
EMAs only need to establish a link to that information. A cost-effective
method for disseminating hazard analysis information would be to provide
detailed information on the state Web site and publicize the address of
this Web site through other media such as one-page maps and leaflets,
newspaper articles, and radio and television public service
announcement.

*EMAs might overlook hazard agents with which they have little or no
recent disaster experience.* This does not appear to be the case because
more commonly experienced hazards such as storms receive less attention
than more dramatic agents such as hurricanes and earthquakes. The
problem seems to be that EMAs simply are not providing enough hazard
information themselves, or linking to others who do provide that
information.

*Conclusions.* Most EMAs are underutilizing an important channel for
delivering hazard analysis information to local emergency managers and
the public. As mentioned earlier, it is likely that the demand for
Web-based hazard information will increase over the time. According to
FEMA (www.fema.gov), Internet users visited FEMA's hurricane-related Web
sites more than 1.25 million times in the week after Hurricane Bertha
hit the US in 1996. Recognized authorities, such as emergency management
agencies and major research centers, need to play an important role in
disseminating hazard-related information (Fischer, 1998). SEMA Web sites
can play an important role in meeting this need by helping local
emergency planners collect needed information rapidly and easily; LEMA
Web sites can add value by providing information that is specific to
their jurisdictions. In both cases, EMA Web sites can help local
residents recognize their exposure to natural and technological hazards.
This can motivate them to adopt hazard adjustments that would reduce
their vulnerability to these threats.

In the course of examining SEMA Web sites, Hwang, et al. (2001) observed
a number of recurring deficiencies. Some of these related to the content
of the hazard analysis information, while other deficiencies arose from
the way in which the Web pages were structured. Table 6-9 lists 18
recommendations for improving the presentation of hazard analysis
information on EMA Web sites. These recommendations are specifically
directed toward improving the usability of those portions of a Web site
accessed by the public, but do not address many of the general issues of
Web site design that are covered in great detail elsewhere (e.g.,
Nielsen, 2000).

##### Local Utilization of Hazard Analysis Web Sites

In addition to knowing what is on SEMA Web sites, it is important to be
aware of the sources local emergency managers use to obtain their hazard
analysis information and the extent to which local government agencies
utilize different emergency management information technology
applications. A recent survey of Texas emergency managers and land use
planners found over one-third of the responding local government
agencies use few sources of hazard analysis information and that nearly
one-third use no hazard analysis information at all (Lindell, et al.,
2002). Of those who do use hazard analysis information, two-thirds of
the materials used are printed documents and only one-third of the
materials used were obtained from the Internet. The relative importance
of different Web sites can be seen in Table 6-10, which shows
respondents' ratings of the extent to which they used each agency's Web
site.

[**Table 6-9**. Recommendations for Hazard Analysis Web
Sites.]{.underline}

-   Recognize that your Web site will be considered an authoritative
    source by users, which means that you have a responsibility for
    ensuring the accuracy of the information you provide.

-   Recognize that a Web site transmits information directly to the
    public without passing through the usual print (newspapers and
    magazines) and electronic (television and radio) news media. This
    reduces message distortion, but requires a high standard of clarity
    and organization.

-   Coordinate the information provided through your Web site with the
    guidelines in the Red Cross's *Talking about Disaster: Guide for
    Standard Messages* (www.redcross.org/disaster/safety/guide.html).
    This guide---a joint effort of the Red Cross, FEMA, National Weather
    Service, and other organizations---is designed to standardize
    disaster information provided by authoritative sources so they do
    not issue conflicting messages.

-   Address all significant hazards to which your jurisdiction is
    vulnerable, but also provide information about the likelihood of
    major events so that people can judge which ones deserve the
    greatest priority. Display this information on maps to show where
    these hazards are most likely to occur.

-   Provide non-technical explanations of the physical aspects of
    hazards (e.g., how hurricanes form and how wind, rain, and surge
    behavior affects the built environment) to help people understand
    what will happen and why it will happen.

-   Provide information about hazard impacts so users can understand how
    a disaster will affect their communities. Important information
    about hazard impact includes the speed of onset, scope and duration
    of impact, and the magnitude of different types of consequences such
    as casualties (deaths and injuries), property damage, and economic
    impacts (disruption to industrial, commercial, agricultural, and
    governmental activity).

-   Provide information that *personalizes* the potential consequences
    for the viewers. These include the cumulative probability of being
    affected during the different periods of time that a person would
    live in a risk area (e.g., 10, 20, and 30 year intervals).

-   Provide a Web site index or table of contents to help users find
    needed information quickly and effectively. If your Web site is very
    large, provide a search engine for locating topics of interest.

-   Provide links to other emergency-related information such as
    situation reports about current incidents and information available
    from other local, state and federal emergency-related organizations.

-   Provide information about hazard adjustments---actions people can
    take to protect themselves, their families, and their property from
    environmental hazards. Explain how effective these actions are in
    protecting persons and property, whether they are useful for other
    purposes, how much they cost, what knowledge, skills, tools, and
    other resources they require. Describe the specific steps required
    to perform any unfamiliar actions.

-   Organize any links to other Web sites by referring the user to the
    page that addresses a specific topic, not to an organization's home
    page. For example, nonspecific links to FEMA and Red Cross home
    pages are of little help because these sites contain thousands of
    pages of information.

-   Keep text clear and succinct. Use suitably large and legible fonts
    and simple color design schemes so the information is easy to read.

-   Provide enough figures and pictures to explain the text and maintain
    interest, but avoid overuse of pictorial materials because this can
    cause the information to download so slowly that users become
    frustrated and abandon their information search.

-   Ensure your Web site is compliant with the Americans with
    Disabilities Act, which requires pictures and graphs to be described
    in words and that your site be navigable without a mouse.

-   Make it easy for viewers to download information by attaching
    documents in PDF or major word processor (e.g., Word Perfect^®^ or
    MS-Word^®^) format.

-   Include contact information with postal and e-mail addresses,
    telephone numbers, and fax numbers of persons from whom users can
    obtain additional information or to whom they can offer suggestions.

-   Publicize your Web site by seeking cross-links with news media Web
    sites, by notifying other state and local organizations, and listing
    the Web site URL on hazard awareness materials such as hurricane
    maps, refrigerator magnets, or preparedness checklists.

-   Verify that your server can handle elevated demand for information
    during an emergency. Ensure that high demand will not degrade the
    functionality of host organization's internal emergency management
    information [system or of any emergency management information
    network residing on that server.]{.underline}

These data indicate FEMA, SEMAs, and EPA are the most popular sources of
hazard analysis information, but the sources differ in the ways that
they are used. The respondents used slightly more print than electronic
resources from EPA and noticeably more print than electronic resources
from FEMA. However, the preference is reversed for the other federal
agencies---NOAA/NWS, and USGS---that are accessed only in electronic
form. One plausible explanation for this pattern of results is that FEMA
disseminates a large volume of hazard analysis information in print and
targets much of this information for state and local government
agencies. In addition, FEMA has an extensive Web site that contains
information on all phases of emergency management and also real-time
emergency and disaster information. By contrast, NOAA and NWS tend to be
better known among local government agencies for real-time weather and
flood forecasts than for long-term hazard analysis information. These
uses, obviously, would tend to promote electronic rather than print
access.

**Table 6-10.** Mean Extent of Internet Site Access by Local Government
Agencies.

  ------------------------------ -------- --------------------------- --------
  Web site                       Extent   Web site                    Extent
                                 of use                               of use

  Federal Emergency Management   2.90     US Geological Survey        1.91
  Agency                                                              

  Nat. Ocean. and Atmos. Admin.  2.92     Int. Assn. of Emergency     1.89
                                          Managers                    

  State Division of Emergency    2.84     US Department of            1.86
  Management                              Agriculture                 

  State Department of Health     2.61     Emer. Plan. Info. Exchange  1.85

  US Environmental Protection    2.57     Bureau of the Census        1.83
  Agency                                                              

  State Nat. Res. and Cons.      2.55     Nat. Emer. Mgmt Assn.       1.74
  Comm.                                                               

  Emer. Mgrs. Weather            2.23     State Department of         1.68
  Information Network                     Agriculture                 

  US Department of               2.20     Salvation Army              1.57
  Transportation                                                      

  National Hurricane Center      2.07     Small Business              1.41
                                          Administration              

  American Red Cross             1.97     US Nuclear Regulatory       1.27
                                          Commission                  
  ------------------------------ -------- --------------------------- --------

Source: Hwang, et al. (2001).

By contrast, USGS tends to be better known among local government
agencies for long-term hazard analysis information than for real-time
forecasts. This might seem to favor print over electronic access, but
USGS is not as active as FEMA in disseminating its reports to local
government agencies. Consequently, its reports tend to be rather
difficult to access in print, but much of the essential information is
available on the agency's Web site.

Given the increasing popularity of the Internet, with its advantages of
low cost dissemination and ease of frequent updates, it would seem
advisable for federal and state agencies to increase their reliance on
the Internet to disseminate hazard analysis information. However, the
data from this survey indicate that hazard analysis information should
not be distributed exclusively through electronic channels because there
is a significant portion of the audience for this information that still
uses print media. Further research is needed to determine whether local
government agencies prefer to use paper documents even though they could
just as easily access them electronically; whether they lack reliable
hardware, software, or trained personnel to access these documents on
the Internet; or whether the information on the Internet is too poorly
organized for them to make effective use of it.

Other data from Lindell, et al. (2002) reveal a hierarchy of computer
applications ranging from the most popular, word processing, to the
least popular, hazard modeling (Table 6-11). Moreover, these computer
applications can be divided into three categories. The most frequently
used applications are word processing, email, Internet connection,
databases, and spreadsheets. The second category is graphics, desktop
publishing, project accounting, presentation graphics, and statistical
analysis. The least frequently used applications are GIS and mapping,
CAMEO, Web page design, and hazard modeling.

Interestingly, agencies become increasingly involved with all members of
one computer application type as soon as they begin to use one member of
that type. For example, agencies do not appear to use applications in
Category 3 until after they have begun to use Categories 1 and 2
extensively. This finding is consistent with the notion of "training
transfer" (Ford & Schmitt, 2000) in which the mastery of less difficult
technologies provides the basis for learning how to use more complex
technologies. This suggests state emergency management agencies can
increase the utilization of more complex computer applications such as
GIS and hazard modeling by first ensuring that local government agencies
have mastered more basic applications such as databases and
spreadsheets. In addition, the statistically significant differences
between local emergency managers and land use planners in the
utilization of GIS, mapping, and Web site design suggest emergency
managers should be encouraged to seek partnerships with land use
planners to obtain the products of these technologies. Indeed, the fact
that land use planners have consistently greater experience with all
forms of information technology suggests that such partnerships could be
beneficial in many ways.

**Table 6-11**. Frequency of Computer Application Use by Emergency
Management Coordinators and Land Use Planners.

  ------------------ ------------------- ----------- ----------------------
  Type Of Computer   Emergency           Land Use    Statistical
  Application        Management          Planners    Significance Of
                     Coordinators                    Difference

  1\. Word           4.23                4.85        *t*~89~ = 2.31, *p* \<
  processing                                         .05

  2\. Email          4.05                5.00        *t*~90~ = 3.14, *p* \<
                                                     .01

  3\. Internet       3.70                4.42        *t*~88~ = 2.10, *p* \<
  connection                                         .05

  4\. Databases      3.56                4.35        *t*~88~ = 2.41, *p* \<
                                                     .05

  5\. Spreadsheets   3.44                4.27        *t*~88~ = 2.58, *p* \<
                                                     .05

  6\. Graphics       2.83                4.08        *t*~88~ = 3.61, *p* \<
                                                     .001

  7\. Desktop        2.74                3.20        *t*~88~ = 1.25, *ns*
  publishing                                         

  8\. Project        2.49                3.04        *t*~85~ = 1.46, *ns*
  accounting                                         

  9\. Presentation   2.25                3.46        *t*~88~ = 3.81, *p* \<
                                                     .001

  10\. Statistical   2.24                2.81        *t*~87~ = 1.76, *ns*
  analysis                                           

  11\. GIS and       1.98                3.54        *t*~86~ = 4.17, *p* \<
  mapping                                            .001

  12\. CAMEO         1.96                1.97        *t*~89~ = 0.27, *ns*

  13\. Webpage       1.77                2.46        *t*~88~ = 2.39, *p* \<
  design                                             .05

  14\. Hazard        1.75                1.75        *t*~86~ = 0.00, *ns*
  modeling                                           
  ------------------ ------------------- ----------- ----------------------

Source: Lindell, et al. (2002).

In summary, these data suggest that federal and state agencies should
provide increasing amounts of hazard analysis information on their Web
sites, but that the transition from print to electronic media should not
exceed the ability of local government agencies to access the Internet.
This is particularly likely to be a problem for smaller and poorer
jurisdictions (Quarantelli, 1997) Moreover, federal and state agencies
should consider facilitating local emergency managers' access to hazard
analysis information by utilizing the computer applications with which
their target audience is most familiar. At present, these are databases,
spreadsheets, and Internet connections to agency Web sites. Local
emergency managers are more likely to make use of sophisticated hazard
modeling applications such as ALOHA (Federal Emergency Management
Agency, no date, a) and HAZUS (National Institute of Building Sciences.
1998) if they have successfully mastered more basic computer
applications or if they have partnered with land use planners in their
communities who have mastered the advanced computer applications.

######## Assessing Risk

A major problem in applying the methods of HVA is risk and uncertainty.
FEMA's (1997) *Multi-Hazard Identification and Risk Analysis*, together
with information from state and federal agency Web sites, can be used to
identify the hazard agents to which a community is exposed. This is a
good start, but it seems insufficient. Logic suggests that an emergency
manager should consider the probability of a disaster rather than just
the possibility that it could occur. After all, an event that has a 50%
chance of occurrence should be given more attention than one that has
only a 1% chance of occurrence. Unfortunately, there are many
difficulties in analyzing risk (Frosdick, 1997). The desired
probabilities frequently have not been calculated (e.g., the probability
of a hazardous materials transportation accident in a given
jurisdiction), are highly uncertain (e.g., the probability of a Category
5 hurricane strike is difficult to estimate because the 150 year
historical record is too short), are unreliable (e.g., the true
probability of a major flood is often higher than its nominal value
because upstream development is increasing the probabilities to an
unknown extent), or are not available for disclosure even if they have
been calculated (e.g., the probability of a release from a toxic
chemical plant is information the owner won't share, in part for
security reasons).

Moreover, Figure 6-1 indicates that even if one knew the probability of
an event, there is significant uncertainty about the magnitude of its
physical impacts. That is, knowledge that there is a 20% probability of
a hurricane strike in the coming year is of limited use because
different hurricane categories have substantially different impacts and,
indeed, disaster impacts can vary significantly within hurricane
categories. In general, the number of casualties from a hurricane
depends not only on storm characteristics (including forward movement
speed and track stability), but also on the size and distribution of the
population at risk (which is greater on holiday weekends than midweek
during the fall) and authorities' ability to evacuate the risk area.
Similarly, the amount of damage to structures depends not only on the
wind speed and surge depth (which can be estimated from a hurricane's
Saffir-Simpson category), but also on the amount of inland flooding
(which is unrelated to its Saffir-Simpson category). For example,
Tropical Storm Allison had a devastating impact on Houston because it
stalled over the city and rained for days even though it never reached
hurricane wind speed. Finally, Figure 6-1 indicates that even if one
knew the probability of an event's physical impacts, there is
significant uncertainty about the magnitude of its social impacts. Some
communities recover rapidly from disaster impact whereas
others---sometimes ones with less damage and fewer casualties---never
recover.

In summary, local emergency managers cannot currently obtain precise
information about their communities' hazard vulnerability and such
information is unlikely to be available in the near future. This might
seem to be a very pessimistic view of the usefulness of HVAs but, in
fact, it is not. Rather, it simply recognizes the current limitations in
the state of HVA technology and (quite probably) inherent limitations in
the resources local emergency managers can devote to this activity.

However, it is important to recognize that emergency managers can still
do their jobs effectively even if they lack extremely precise HVA data.
This is because managers in general, and emergency managers in
particular, only need enough information to make decisions about the
allocation of the resources under their control or to justify obtaining
more resources to do their jobs better. Thus, emergency managers need
enough HVA data to decide how to allocate their existing resources to
different emergency management activities such as hazard mitigation,
emergency response preparedness, and disaster recovery preparedness.
Indeed, HVA data alone would be insufficient to make these resource
allocation decisions because different hazard agents to which a
community is exposed impose similar demands. Consequently, investments
in those emergency management activities have payoffs for many different
hazards. For example, all hazards require a capability for incident
management actions such as establishing coordination among organizations
that are normally independent of each other and most hazards require a
capability for population protection actions such as evacuating local
residents. Thus, investments in the community's capability for
evacuation might reduce vulnerability more than investments in hazard
mitigation activities (which generally affect vulnerability to only a
single hazard). If so, the HVA only needs to be specific enough for
emergency managers to persuade themselves that the allocation of
resources among emergency management activities is appropriate.

Emergency managers also need HVA data to lobby for increased budget
allocations to emergency management rather than to other community
activities such as education, health care, and transportation. In this
case, the HVA should be specific enough for emergency managers to
successfully persuade other stakeholders that an increase in the
emergency management budget will benefit the community more than
allocating that money to some other activity. This is not a trivial
problem because, as noted in previous chapters, emergency managers must
lobby for money to solve *future* problems, which is inherently more
difficult than lobbying for money to solve current problems. In summary,
emergency managers need to allocate just enough time and money to HVA to
achieve the precision needed to achieve their budget allocation and
budget augmentation objectives.

**Case Study: Hurricane HVA for the Texas Gulf Coast**

For many years, the Texas A&M University Hazard Reduction & Recovery
Center conducted hurricane HVAs for the Texas Governor's Division of
Emergency Management. The results of these HVAs have been published in
the form of hurricane storm atlases ((Texas Governor's Division of
Emergency Management, 2004), contingency planning guides (Texas
Governor's Division of Emergency Management, 2002a, 2002b), and
evacuation maps. These hurricane HVAs include the analyses of hazard
exposure and physical vulnerability described in previous sections of
this chapter.

Each hurricane HVA begins with data generated by the National Hurricane
Center's Sea, Lake, and Overland Surges from Hurricanes (SLOSH---
Jelesnianski, Chen & Shaeffer, 1992) model. SLOSH uses data on coastal
topography and bathymetry (the topography of the sea floor) and storm
parameters (Saffir-Simpson Category, forward movement speed, and storm
track) as input to a complex computer model of hurricane behavior. SLOSH
output comprises tables of surge height over time for a series of
locations spaced approximately equidistantly along the coast. The data
in these tables represent average surge heights over a large number of
simulated hurricane scenarios within those storm parameters.

Analysts import the SLOSH output data into a GIS that already contains
data on coastal topography, political boundaries, physical features
(e.g., rivers and lakes), census data, and facility locations. The SLOSH
data are first overlaid onto the topographical data layer to compute
surge depths. This calculation subtracts the ground elevation (in feet
above sea level) from the surge height (also in feet above sea level) to
compute surge depth (in feet above the ground's surface). Computation of
the surge depth data allows the analyst to create a map that identifies
the expected surge inundation boundary and plot surge depth contours. As
will be discussed below, these maps allow emergency managers to identify
populations and facilities at risk from surge flooding. The storm surge
data are also used to generate hydrographs that plot the hourly surge
heights (from 24 hours before to 12 hours after landfall) at key coastal
locations for hurricanes of varying Saffir-Simpson category, forward
movement speed, and track direction. This information allows local
officials to identify locations where early flooding is likely to
interrupt evacuations.

The analysts also used Kaplan and DeMaria's (1995) Inland Wind Decay
Model to generate contours of the one-minute sustained surface wind
speeds at different distances from the coastline (Lindell, Prater &
Zhang, 2001). These sustained surface wind speed contours, which are
mapped as a function of hurricane category and forward movement speed,
are translated into expected peak gusts by applying wind gusting
factors. These maps allow emergency managers to identify populations and
facilities at risk from wind damage.

Once the surge and wind contours have been generated, they are used to
define exposure zones that vary in their threat to facilities that
provide essential services or key care services. Such facilities include
those performing communications, power generation, water/sewer
treatment, transportation, public health and safety, and postdisaster
sheltering functions. In addition, the exposure zones are used to
identify facilities about which there are special concerns, especially
industrial and hazardous materials facilities. Each facility is located
on a map and its position indicated by a facility code (indicating its
function). In addition, analysts prepare a table listing each of the
facilities, its location defined by an alpha-numeric code (e.g., H6)
indicating its location on the map grid, its latitude and longitude, its
elevation, and the lowest hurricane category at which it is likely to be
affected by surge flooding, waves over 3 feet, surge over 6 feet, or
dangerous winds.

Other data are collected on highway capacity, population size and
distribution, and coastal residents' evacuation expectations (Lindell,
et al., 2001) and integrated within an evacuation time estimation model
(Lindell & Prater, 2005; Lindell, Prater, Perry & Wu, 2002) to produce
evacuation time estimates for different hurricane categories (Lindell,
Prater & Wu, 2002). Finally, data are collected on inland counties'
evacuation guidance and transportation support for hurricane evacuees
(Prater & Lindell, 2002). All of these data can be used to estimate the
level of damage and casualties that would be expected from hurricanes.
For further information, see the Texas Governor's Division of Emergency
Management Web site (www.txdps.state.tx.us/dem/pages/planning.htm).