Nat Hazards (2014) 74:1209–1222
DOI 10.1007/s11069-014-1240-2
ORIGINAL PAPER
A multi-criteria evaluation model of earthquake
vulnerability in Victoria, British Columbia
Blake Byron Walker • Cameron Taylor-Noonan • Alan Tabbernor •
T’Brenn McKinnon • Harsimran Bal • Dan Bradley • Nadine Schuurman
John J. Clague
•
Received: 30 October 2013 / Accepted: 13 May 2014 / Published online: 27 May 2014
Ó Springer Science+Business Media Dordrecht 2014
Abstract Researchers have recently examined the geographic variability of the vulnerability of populations to earthquakes. These studies focus mainly on the complex modelling of geophysical processes or identification of socio-economically disadvantaged
populations. However, no studies to date have integrated different components of vulnerability with metrics of travel distance to hospitals and trauma centres (systemic vulnerability). We argue that this previously unaccounted component is an important
conceptual and practical aspect of earthquake vulnerability. Accordingly, this paper presents a multi-criteria model for combining physical, social, and systemic components,
moving towards a more comprehensive assessment of vulnerability. An analytic hierarchy
process is used to produce a place-specific index of social vulnerability, which we combine
with soil liquefaction and amplification index and a road network model for access to
hospitals and trauma services. Using a geographic information system, we implemented
this model for the Greater Victoria region (483 km2, 2011 population: 345,000) in British
Columbia, Canada. Clustering of total vulnerability was found in outlying areas, highlighting the importance of access to trauma centres. We conclude by identifying challenges
in measuring earthquake vulnerability and advocate integration of systemic vulnerability
components in natural hazards research.
Keywords Earthquakes Risk Vulnerability Disaster response GIS Victoria British Columbia
B. B. Walker C. Taylor-Noonan A. Tabbernor T’BrennMcKinnon H. Bal D. Bradley N. Schuurman
Department of Geography, Simon Fraser University, Burnaby, Canada
J. J. Clague (&)
Department of Earth Sciences, Simon Fraser University, Burnaby, Canada
e-mail: jclague@sfu.ca
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1 Introduction
From 2000 to 2009, earthquakes caused a global annual average of $US 18.65 billion in
total economic damage (Guha-Sapir et al. 2011). This estimate and others incorporate the
effects of physical damage to the built environment, health care costs, and losses in
economic productivity, but they fail to capture neighbourhood-scale variations in damage.
Consequently, there recently has been a shift in the literature towards local analyses of
vulnerability. Identification of areas that are most vulnerable to earthquakes enables targeted disaster preparedness and response programmes to reduce an earthquake’s total
impact. A population’s vulnerability to an earthquake or other disaster depends on a
combination of factors that affect its resilience both directly (e.g. structural damage and
access to trauma centres) and indirectly (e.g. socio-economic disadvantage) (Rygel et al.
2006; Flanagan et al. 2011; Fekete 2012; Smith 2013).
For analytical purposes, we deconstruct vulnerability into three components: physical
damage based on geological features and the built environment; socio-economic barriers to
resilience and recovery; and access to trauma and other support services. Spatial variations
in these components result in different levels of vulnerability to an earthquake (Wisner and
Luce 1993). While previous earthquake research has made significant progress in developing vulnerability assessments within the physical and social categories, no researchers
have included the effect of access to trauma centres and hospitals and few have sought to
integrate them into a comprehensive index. Accordingly, we present a multi-criteria
evaluation (MCE) model of earthquake vulnerability that incorporates access to trauma
centres and hospitals using a geographic information system to identify and rank residential areas in Victoria, British Columbia.
2 Study area
This study focuses on the Greater Victoria region, located on the south-east coast of
Vancouver Island along the Cascadia Subduction Zone (Fig. 1). In this area, the oceanic
Juan de Fuca plate moves in a north-east direction beneath the continental North American
plate. Convergence between the two plates and deformation of different blocks within the
North American plate result in frequent earthquakes in south-west British Columbia and
the US Pacific Northwest. In 2013, 171 earthquakes were recorded within 100 km of
Victoria (National Earthquake Database 2014). Moderate-to-strong earthquakes (moment
magnitude 5–7) occur in this region, on average once every 15–20 years, and geological
evidence suggests that great and giant earthquakes (moment magnitude 8.0?) have an
average recurrence of 500–600 years (Atwater et al. 2003; Frankel and Petersen 2008;
Cascadia Region Earthquake Workgroup 2011; Goldfinger et al. 2012; Kulkarni et al.
2013; Natural Resources Canada 2013; Oregon Department of Geology and Mineral
Industries 2013). The most recent major earthquake near the study area occurred in 1946
on central Vancouver Island (M7.3), and a smaller M6.9 event occurred in Puget Sound,
Washington, in 2001 (Clague 1997; Chakraborty et al. 2005).
Greater Victoria has a population of 345,000 and numerous old masonry buildings that
make it particularly vulnerable to damage from earthquakes. Although much of the
metropolitan area is located on firm rock, there are areas of thick Holocene and late
Pleistocene sediments that are prone to ground motion amplification, as well as beaches
and areas of artificial fill along the shoreline that are vulnerable to liquefaction. Potential
seismic sources include the megathrust fault separating the Juan de Fuca and North
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Fig. 1 The study area, highlighted in green, is located on the south-east coast of Vancouver Island. Victoria
is situated near the west edge of the North American plate, above the subducting Juan de Fuca plate. Traces
of known crustal faults are shown in red
American plate, crustal faults in the area, and the subducting Juan de Fuca plate, which
flexes beneath the Salish Sea as it moves down into the asthenosphere. Great earthquakes
on the megathrust trigger large tsunamis that are likely to reach elevations of somewhere
between 2 and 5 m at Victoria (Clague et al. 2003).
3 Methods
3.1 Social vulnerability
The concept of social vulnerability draws on empirical and theoretical evidence of less
favourable disaster outcomes among socio-economically disadvantaged populations. The
ability of a disadvantaged population to recover from an earthquake is impaired by limited
economic and political capital (Schmidtlein et al. 2011). Numerous studies examining
social vulnerability use quantitative indices, often modelled using census data (Wisner and
Luce 1993; Morrow 1999; Delor and Hubert 2000; Lindsay 2003; Few 2007; Hewitt 2013).
Most of this literature originates in the USA and thus tends to emphasise the proportion of
minority populations as an explanatory variable (Schmidtlein et al. 2008). Other widely
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used metrics of social vulnerability are average income, age, family composition, language, and time spent living at one address (Chou et al. 2004; Chateau et al. 2012; Noriega
and Ludwig 2012). Of these metrics, average income is commonly ranked as the leading
predictor, followed by average or median age (Cutter et al. 2003; Rashed and Weeks
2003). However, geographic variations in population composition and social structures
require that such indices be place-specific (Bell et al. 2007). This article details the
development of a social vulnerability index specific to the study area and based on census
data.
The study area contains 452 census dissemination areas; data from seven of the 452
areas are unavailable due to publication restrictions. Where possible, we inferred missing
values from these seven areas using lower-resolution census data. We selected nine census
variables on the basis of previous findings in the literature (Table 1) and tested them for
multi-colinearity (Table 2). Strong correlations between variables, for example, dependent
population and seniors living alone, suggest that these are robust indicators of social
vulnerability in the study area.
We recalculated all values as standard scores and, where necessary, reversed their signs
so that higher scores correspond to higher social vulnerability. We derived variable weights
using an analytic hierarchy process, a method used in previous natural hazards research
(Saatay 1994; Youssef et al. 2011). This method quantitatively ranks variables based on
pair-wise comparisons and assigns weights as model coefficients. Reference to the literature is crucial when using this method, as it enables the implementation of expert opinion
and prior knowledge. The input priority and pair-wise combinations were thus established
based on the literature and observed multi-colinearity (Table 2), and mediated by team
discussion. We then multiplied resulting weights (Table 3) by the relevant census variables
and entered them into an additive model for each census dissemination area to produce the
social vulnerability index scores. These scores were regressed against a similar algorithm
for nearby Vancouver and found to correspond (r = 0.25, p \ 0.01), suggesting a suitable
model fit (Bell et al. 2007).
3.2 Physical vulnerability
Physical vulnerability to earthquakes can be conceptualised as a population’s risk of
adverse impacts from ground shaking and secondary phenomena, such as liquefaction,
landslides, surface rupture, and tsunamis (Douglas 2007; Duzgun et al. 2011; Fekete 2012).
Distance from the hypocentre influences the intensity of ground shaking as well as the
dominant components of the seismic wave spectrum, but near-surface and deeper geological conditions are also important (Sica et al. 2014). Amplification of ground motion, for
example, can occur as waves travel through loose sediment, and steep slopes can reflect or
refract seismic waves (Clague 2002).
In some cases, liquefaction, landslides, or tsunamis can be more damaging than the
primary ground motion itself (Clague 2002; Pathak and Dalvi 2013). Liquefaction is a
process whereby saturated, non-cohesive sediment is transformed from a solid to a liquid
state through intense shaking (Xue and Yang 2014). River deltas, shorelines, and reclaimed
land are particularly susceptible to this phenomenon. Shaking during earthquakes can also
trigger landslides that damage and bury buildings and infrastructure. Most tsunamis are
triggered by the sudden displacement of the sea floor during earthquakes and can be
extremely destructive in coastal areas, particularly if there is little time to evacuate before
they make landfall (Xie et al. 2012). Further, building design and construction have been
shown to significantly influence physical vulnerability (Hengjian et al. 2003). Significant
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Table 1 Census variables used to construct the social vulnerability index based on previous findings in the
literature
Census variable
References
Average income
Chou et al.
(2004)
Cutter et al.
(2003)
Kuhlicke et al.
(2011)
Housing ownership
Martins et al.
(2012)
Morrow (1999)
Noriega and
Ludwig (2012)
Percentage of single-parent
families
King and
MacGregor
(2000)
Kuhlicke et al.
(2011)
Martins et al.
(2012)
Percentage of no high school
completion
Armas (2008)
Cutter et al.
(2003)
Martins et al.
(2012)
Unemployment rate
Armas (2008)
Cutter et al.
(2003)
Kuhlicke et al.
(2011)
Percentage of dependent
population
Cutter et al.
(2003)
Morrow (1999)
Schmidtlein
et al. (2011)
Percentage of seniors living
alone
Chakraborty
et al. (2005)
King and
MacGregor
(2000)
Schmidtlein
et al. (2008)
Percentage of foreign
language only
Noriega and
Ludwig (2012)
Morrow (1999)
Percentage of recent movers
(within the past year)
Cutter et al.
(2003)
King and
MacGregor
(2000)
Morrow (1999)
Morrow (1999)
Schmidtlein
et al. (2011)
Morrow (1999)
Morrow (1999)
progress has been made in recent years in modelling structural risk at the scale of individual buildings (Ventura et al. 2005; Onur et al. 2006; Mück et al. 2013; Xu et al. 2014),
but structural data availability poses a significant limitation in this field (Dell’Acqua et al.
2013; Ehrlich et al. 2013).
For our analysis, we define physical vulnerability as the potential risk of structural
damage from amplification and liquefaction. Thicker clay, peat, or organic soil layers are
substantially more prone to amplification than other materials, and anthropogenic fill and
shoreline sediments are most susceptible to liquefaction. We used the combined liquefaction and ground motion amplification ratings created by the British Columbia Geological Survey, with six soil categories ranked on a scale of increasing amplification/
liquefaction risk (Monahan et al. 2000). We calculated an average soil amplification and
liquefaction rating for each census dissemination area. These ratings were used as physical
vulnerability index values (top-left map in Fig. 3). Although this procedure does not
account for building conditions and some secondary damage phenomena, it provides a
baseline to implement a MCE. Future research will examine the potential for incorporating
terrain modelling and structural risk indices in a MCE model.
3.3 Systemic vulnerability
Medical geography research has demonstrated that travel distance to trauma centres is
significantly negatively correlated with the probability of patient survival (Amram et al.
2011). Immediately after a major earthquake, populations farther from a trauma centre are
at a greater risk of adverse health outcomes (Gitis et al. 2012). In this context, access to
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-0.13*
Unemployment
-0.02
Recent movers
0.73*
-0.01
0.06
0.16*
-0.15*
0.28*
0.16*
Home
ownership
0.04
0.08
-0.19*
-0.35*
0.13*
0.20*
Single
parent
0.52*
0.24*
0.09
-0.04
0.12*
No high
school
0.00
0.12*
0.05
-0.09*
Unemployment
Values are Pearson’s R (linear); asterisks indicate significance of correlation for the study area (p \ 0.05)
-0.01
-0.12*
Seniors living alone
Foreign language
only
0.24*
-0.25
No high school
Dependent
population
-0.27*
0.18*
Average
income
Single parent
Home ownership
Average income
Census variable
Table 2 Colinearity matrix of selected census variables for the social vulnerability index
-0.03
-0.08
0.65*
Dependent
population
-0.01
-0.03
Seniors living
alone
0.15*
Foreign language
only
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Table 3 Variable weights for the social vulnerability index, derived using an analytic hierarchy process
Factor
Average income
Weights
Rank
0.2855
1
% of seniors living alone
-0.2371
2
Dependent population
-0.1528
3
% of single-parent families
-0.1239
4
0.0771
5
Unemployment rate
-0.0412
6
% of population without high school completion
-0.0349
7
Number of movers within the past year
-0.0239
8
% of population without knowledge of an official language
-0.0237
9
% of home ownership
medical care is commonly defined as a function of distance, time, and travel barriers to
care facilities (McLafferty 2003).
The two primary ways of measuring access are distance-based and area-based. Distance-based measures focus on the time required for people at risk to reach a care facility,
whereas area-based measures describe the ratio of population to services in an area
(McLafferty 2003). Measuring access based on area suffers from a dependence on the
spatial containers used (e.g. municipalities vs. provinces), a phenomenon known as the
modifiable areal unit problem. Distance-based measures circumvent this problem and thus
are commonly more accurate than area-based measures.
Previous studies have used geographic information systems to integrate census and road
network data to measure population access to hospitals and physicians (Brabyn and Skelly
2002; Luo and Wang 2003; Schuurman et al. 2010). Others have used similar approaches
to model evacuation and information dissemination in the event of a natural disaster (Cova
and Church 1997; Chiba 2011; Wood and Schmidtlein 2012; Zhang et al. 2013). However,
no known earthquake vulnerability assessments to date have implemented access to health
care. We built upon these approaches by incorporating an index of systemic vulnerability
for the study area, which we define as the population’s accessibility to trauma services,
contingent on the spatial arrangement and quality of transportation infrastructure and
emergency service response time (Rashed and Weeks 2003; Peleg and Pliskin 2004;
Nallamothu et al. 2006; Amram et al. 2011; Horner and Widener 2011).
To calculate systemic vulnerability, we performed a distance-based network analysis
(Brabyn and Skelly 2002). Six hospitals serve the study area, one of which is a trauma
centre. Driving distances based on speed limits, travel time, walking time, and the number
of road lanes per kilometre were separately calculated from the trauma centre and the
nearest hospital to each census dissemination area and entered into a simple additive
model. We then standardised the resulting accessibility value for each dissemination area
to produce a systemic vulnerability score. Disaster response routes are designated roadways that only authorised vehicles can travel on in the event of an earthquake or tsunami.
We removed these routes from the road network when calculating access to hospitals, as
we assume personal vehicle use for less severe trauma, but we included them for access to
the trauma centre on the assumption that emergency medical services vehicles would use
them. Walking was included to account for the potential impassability of roads due to
surface rupture.
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3.4 Constructing a multi-criteria evaluation model
Multi-criteria evaluation is a quantitative method of geographic problem-solving and
decision support that integrates multiple spatial data sets with score areas based on a set of
predetermined criteria (Malczewski 1999). With roots in early constraint mapping methods
in the 1960s, this process has historically been applied to site selection studies. However,
recent studies have applied MCE to natural hazards research, including floods (Scheuer
et al. 2011; Youssef et al. 2011; Feizizadeh and Blaschke 2013), landslides (Castellanos
Abella and Van Westen 2007; Akgun and Türk 2010; Pourghasemi et al. 2012), and
earthquakes (Rashed and Weeks 2003; Anbazhagan et al. 2010; Martins et al. 2012; Armaş
and Rădulian 2014).
The choice of evaluation criteria has differed since MCE was adopted as a problemsolving and decision support tool, with most studies focussing on geophysical factors
(Akgun and Türk 2010; Anbazhagan et al. 2010; Pourghasemi et al. 2012; Feizizadeh and
Blaschke 2013); others have integrated social vulnerability (Scheuer et al. 2011; Martins
et al. 2012). Armaş and Rădulian (2014) present an algorithmically sophisticated MCE of
earthquake vulnerability that includes geological risk, social vulnerability, and building
condition. However, previous research has yet to account for the effect of systemic vulnerability, as described in the previous section.
To combine the physical, social, and systemic vulnerability components of this MCE,
we rescaled each component linearly from 0 to 1 and summed them to produce an equally
weighted combined vulnerability score for each census dissemination area. Future research
may integrate the analytic hierarchy process with expert input to derive customisable
component weights (Youssef et al. 2011). A summary of the components is shown in
Fig. 2.
4 Results
Each component differs in its spatial distribution throughout the study area, as shown in
Fig. 3. Physical vulnerability tends to be higher near shorelines, floodplains, and steep
slopes, a finding concordant with previous studies (Clague 2002; Anbazhagan et al. 2010;
Sica et al. 2014). Social vulnerability clusters in the urban core of Victoria with smaller
pockets dispersed throughout the periphery of the urbanised area (Saanich, Langford, Oak
Bay). Systemic vulnerability follows a predictable distance decay pattern along major
roads.
When all vulnerability elements are combined, the most vulnerable areas are in
Langford, Victoria, Saanich, and Oak Bay. The pattern is similar to the physical vulnerability pattern, suggesting the significance of this component in our model. Neighbourhoods in the lowest quintile of overall vulnerability tend to have social vulnerability scores
near the average value, but those scores are offset by their proximity to hospitals and their
low soil amplification rating. Social vulnerability spatially deviates most from systemic
and physical, which we interpret to be a mitigation of socio-economic deprivation by
proximity to the expensive city centre.
The five most and least vulnerable dissemination areas and their corresponding social,
physical, and systemic scores are shown in Table 4. As expected, those with the highest
systemic vulnerability are located at the fringes of the study area. These spatial patterns
appear to fit with expected outcomes, but future research incorporating heterogeneous
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Fig. 2 Census data, a soil amplification/liquefaction index, and a road network analysis were rescaled to
create three components of earthquake vulnerability
component weights may yield results that deviate from ours. The analytic hierarchy process is a particularly powerful, yet easy-to-implement tool for this purpose.
5 Discussion
Spatial interactions between components augment the geographic patterns in earthquake
vulnerability, but additional patterns exist within these components that may influence the
nature of vulnerability in a specific disaster. For example, vulnerability due to liquefaction
is augmented in coastal areas by the threat of tsunami. Similarly, landslide and rockfall
danger from primary ground shaking contribute to greater vulnerability due to increased
ground motion amplification along hillsides, for example in north-west Saanich. The
exclusion of these secondary effects from our model suggests that it may underestimate
physical vulnerability in coastal and mountainous regions.
Dissemination areas with high systemic vulnerability were identified at the fringes of
the Victoria urban area. Beyond the study area, however, few roads penetrate into the
interior of Vancouver Island; thus, many small communities are more isolated than the
areas examined in this study. Systemic vulnerability thus may be a nonlinear function of
distance and time, rather than linear as we have modelled. Little empirical research work
has been conducted to examine this effect, presenting an important avenue for future study.
Airlift services for medical emergencies are available in British Columbia, but such
evacuation resources may be in high demand after a large earthquake, underscoring the
importance of systemic vulnerability in future disaster vulnerability research.
Furthermore, other resources will be required following an earthquake, such as grocery
stores, petrol stations, banks, and other non-medical services. Future research should move
towards the development of a weighted ‘local resource’ index for augmenting the applicability of systemic vulnerability.
While there may be trade-offs between vulnerability components, the situational
implications of the individual components differ. For example, low social vulnerability
does not necessarily compensate for structural damage as a result of high physical vulnerability. However, in combination, they certainly amount to an augmented risk. This
combinatory conceptualisation of total vulnerability is therefore a crucial concept for risk
modelling.
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Fig. 3 Social, physical, and systemic vulnerability scores (top) were equally weighted and combined to
produce the resulting earthquake vulnerability map (bottom). Highly vulnerable areas cluster around the
edge of the City of Victoria and are recommended as priority neighbourhoods for earthquake preparedness
and response programmes
5.1 Study limitations
The scale and spatial units used in this study were dictated by census geography, making
our findings vulnerable to the modifiable areal unit problem. The index of physical vulnerability is based on interpolated and aggregated data (Monahan et al. 2000); consequently, there is potential error in soil properties between sample sites. In addition, we did
not model other secondary geophysical phenomena. The inclusion of building information
would improve the accuracy of the model and significantly increase resolution. We
determined the weighting of social factors in relation to earthquake vulnerability from the
existing literature, which may not be directly applicable to Greater Victoria. The specificity
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Table 4 The five most vulnerable and five least vulnerable census dissemination areas in the Victoria
metropolitan area, based on the earthquake vulnerability index
Least vulnerable dissemination areas
Most vulnerable dissemination areas
Community
Vulnerability score
Community
Vulnerability score
Oak Bay
0.44
Saanich
2.38
View Royal
0.81
Langford
2.36
Esquimalt
0.83
Langford
2.34
Saanich
0.85
Saanich
2.31
Victoria
0.87
Saanich
2.17
of this model could be improved by adopting an expert survey approach (Bell et al. 2007).
Accordingly, other study areas and post hoc evaluations of previous earthquake events
might assist in the calibration of component weights. However, data availability and
quality may be an inherent barrier in low-resource settings, such as Haiti. Finally, the
outputs of any MCE are sensitive to component weights. Accordingly, future research
should consider scenario modelling based on individual components, which can then be
combined in consultation with disaster preparedness and response professionals (Yu and
Lai 2011). The inclusion of expert-determined weights may facilitate the uptake of MCE
methodologies in disaster planning, for example in dedicating evacuation resources or
disseminating public safety information.
6 Conclusion
We present a composite index for scoring a population’s vulnerability to earthquakes,
based on the intersection of high social, physical, and systemic vulnerabilities. Our study is
among the first to include a systemic component, which is an important consideration in
both urban and rural locales. Our methods, including the use of systemic vulnerability
indices, may benefit vulnerability assessments of other natural hazards, such as floods and
landslides. Integration of MCE with disaster preparedness and response programmes will
rely on stakeholder engagement initiatives that are beyond the scope of this work, but are
strongly recommended for future studies.
Acknowledgments The authors gratefully acknowledge the assistance of Fiona Lawson at the Vancouver
Island Health Authority.
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