Vulnerability Mapping (Vulnerability Assessment)

Vulnerability Mapping
(Vulnerability Assessment)
Ariel C. Blanco, Dr.Eng.
EnviSAGE Research Lab
Department of Geodetic Engineering
Training Center for Applied Geodesy and Photogrammetry

capable of being physically or emotionally
wounded; open to attack or damage (Merriam-Webster)
•endangered,
•exposed,
•liable,
•open,
•sensitive,
•subject (to),
•susceptible

The quality or state of being exposed to the possibility
of being attacked or harmed, either physically or
emotionally. (Oxford Lexico)
Defenselessness
Susceptibility Helplessness
Absence of protection
Weakness
Degree to which people, property, resources, systems,
and cultural, economic, environmental, and social
activity is susceptible to harm, degradation, or
destruction on being exposed to a hostile agent or
factor. (Business Dictionary)

What the Sendai Framework says…
• The need for improved understanding of disaster risk in all its
dimensions of exposure, vulnerability and hazard
characteristics;
• Policies and practices for disaster risk management should
be based on an understanding of disaster risk in all its
dimensions of vulnerability, capacity, exposure of persons
and assets, hazard characteristics and the environment.

• Evidence indicates that exposure of persons and
assets in all countries has increased faster than
vulnerability has decreased, thus generating new risks
and a steady rise in disaster-related losses, with a significant
economic, social, health, cultural and environmental impact
in the short, medium and long term, especially at the local
and community levels.

Priority 1
Understanding disaster risk
• Prevent new and reduce existing disaster risk through the
implementation of integrated and inclusive economic, structural,
legal, social, health, cultural, educational, environmental,
technological, political and institutional measures that prevent
and reduce hazard exposure and vulnerability to
disaster, increase preparedness for response and recovery, and
thus strengthen resilience.

• Enhanced work to reduce exposure and vulnerability,
thus preventing the creation of new disaster risks, and
accountability for disaster risk creation are needed at
all levels.

• To encourage the use of and strengthening of
baselines and periodically assess disaster risks,
vulnerability, capacity, exposure, hazard
characteristics and their possible sequential effects at
the relevant social and spatial scale on ecosystems, in
line with national circumstances;

• To systematically evaluate, record, share and publicly account for
disaster losses and understand the economic, social, health,
education, environmental and cultural heritage impacts, as
appropriate, in the context of event-specific hazard-exposure
and vulnerability information
• To make non-sensitive hazard-exposure, vulnerability, risk, disaster
and loss-disaggregated information freely available and accessible,
as appropriate

Risk and Vulnerability
Risk = Hazard x Exposure x Vulnerability

Risk
• Risk is defined as the probability that a particular level of loss
will be sustained by a given series of elements as a result of a
given level of hazard impact (Alexander 2000).
• Risk = hazard x vulnerability

Vulnerability Framing
• In 2012, the IPCC Special Report on Extremes (IPCC 2012) introduced
a risk framework, also adopted by the Fifth Assessment Report (AR5)
(Oppenheimer et al. 2014), which distinguished between exposure
and vulnerability, the latter combining the sensitivity and adaptive
capacity elements of the earlier frameworks.
• The “vulnerability” element in this risk framing thus represents social
vulnerability or other types of vulnerability (e.g., ecosystems or
infrastructure), depending on the study.

Climate and Weather-
Related Hazards

• Climate hazard could be represented by past, present,
or future climate variability, extremes, and change
(trends or delta), and in some instances the hazard
could be a function of climate extremes in
combination with other factors such as land use
changes that increase susceptibility to, e.g., floods and
landslides.

Vulnerability
• Provincialized Human Development
Indices of the year 2000 was obtained
from the Philippine Human Development
Report 2000 published by the UNDP and
HDN.
• HDI encompasses health, education and
income factors.
• The inverse of the HDI represented the
vulnerability score.

Othervulnerabilityfactors:
AnthropogenicorHumanand
Developmental

Risk Assessment
• The hazard scores, exposure scores and HDI scores were normalized
by setting the highest values as 100 and readjusting the rest
accordingly.
• Risk here distinctly represents risk to human life. As mentioned, it was
computed using the UNDP framework R = H*E*V, or, in this case:
Risk = (Normalized hazard score) x (Normalized exposure score) / (Normalized HDI).

Vulnerability
• A broad definition:
the degree to which a system or population is likely to
experience harm due to exposure to perturbations or
stress (Turner et al. 2003).

Vulnerability
• “Vulnerability defines the characteristics of a person
or group and their situation that influence their
capacity to anticipate, cope with, resist and recover
from the impact of a hazard” (Wisner et al, 2004).

Vulnerability
• Vulnerability is defined in the Hyogo Framework for Action as:
“The conditions determined by physical, social,
economic and environmental factors or processes,
which increase the susceptibility of a community to the
impact of hazards”.

Vulnerability
•Vulnerability is the propensity or
predisposition to be adversely affected. It
encompasses a variety of concepts and
elements including sensitivity or
susceptibility to harm and lack of capacity to
cope and adapt (IPCC, 2014).

Vulnerability framework
Exposure
Sensitivity
Potential
Impact
Adaptive
Capacity
Vulnerability
The IPCC Third Assessment Report (TAR) and AR4 vulnerability frameworks (McCarthy et al. 2001 and Parry et al. 2007,
respectively) identify vulnerability as a function of exposure to climate hazards, on the one hand, and the sensitivity and
adaptive capacity of the system or society on the other.

Intensity and severity of physical
environmental condition that drive changes
in the state of the biophysical system
EXPOSURE
System’s present state and other factors
that contribute or respond to exposure
factors from climate change
SENSITIVITY
Factors affecting the ability of the system
to cope with impacts associated with
changes in climate
ADAPTIVE
CAPACITY

Maps of climate vulnerability have addressed a range of issues de Sherbinin et al. (2019)

Risk vs. Vulnerability
• While both risk and vulnerability framings may include
social vulnerability, risk management tends to focus
on the probability distributions of extreme weather
events and long term trends of certain magnitudes,
which is vital for disaster preparedness and
infrastructure construction, whereas vulnerability
assessments tend to emphasize underlying factors
that put people and infrastructure at risk (de Sherbinin
2014).

Social Vulnerability Assessment
• Social vulnerability refers to ‘the characteristics of a person or group
and their situation that influence their capacity to anticipate, cope
with, resist, or recover from the impact of a hazard’ (Cutter et al.
2003; Wisner et al. 2004).
• Uses a combination of several factors to represent a population’s
differential access to resources and its ability to cope with and
respond to hazards.

Social Vulnerability Assessment
• The assessment of social vulnerability includes various factors or
characteristics, such as age, gender, race, overcrowding,
ethnicity, social class, unemployment rate, immigrant status,
density and quality of the built environment, land use, housing
tenancy, and the presence of informal support networks

Social Vulnerability Maps
• Representing the spatial variability of social vulnerability
• Allow for the representation of anticipated community needs
at differing levels of disaster response (Morrow 1999).
• They can be used as planning tools, and emergency managers can
analyze the causes of vulnerability.

MCDA
• Multicriteria decision analysis (MCDA) is a set of methods and
procedures for evaluating decision alternatives based on multiple
conflicting criteria and selecting the best alternative.
• MCDA is an expert-based modelling approach and includes
techniques that allow for a better understanding of social
vulnerability within a hierarchical structure.
• MCDA provides a rich collection of techniques and procedures for
structuring decision problems and for the design, evaluation, and
prioritization of alternative decisions (Malczewski 2006).

GIS-MCDA
• GIS-MCDA assigns
weights to the criteria
and exposes the
geography of social
vulnerability under
different scenarios
using aggregation
methods.

MCDA Key Concepts
• The criterion is one of a number of measures against which
options are assessed and compared in a multicriteria
analysis, being structured as objectives and attributes
(Malczewski 1999).
• Objectives indicate the directions of change in a system
desired by decision-makers; objectives are related and/or
derived from attributes and indicate which objectives are
essential for the decision process (Malczewski 1999).

MCDA Key Concepts
The set of attributes characterizes the properties of the
process being assessed. The attributes are classified as factors
or constraints.
• A factor is a measure that enhances or detracts from the
suitability of a specific alternative for the activity under
analysis.
• A constraint serves to affect or restrict the alternatives under
consideration; it is an element or feature that represents
limitations or restrictions and whether an area is considered
unsuitable.

Methodological
process for
assessing social
vulnerability
(1) HIERARCHICAL
STRUCTURE OF THE SOCIAL
VULNERABILITY MODEL,
(2) STANDARDIZATION OF
THE CRITERIA,
(3) CRITERIA WEIGHTING,
AND
(4) DECISION RULES AND
THE MAPPING OF SOCIAL
VULNERABILITY SCENARIOS.

Hierarchical structure of the social
vulnerability model
• When making complex decisions involving multicriteria analysis, the
first step is to decompose the main goal into its constituent
objectives, progressing from the general to the specific.
• In its simplest form, this structure comprises a goal, various
objectives, attributes, and factors.
• This process is based on theoretical considerations that have a degree
of uncertainty and subjectivity, and thus, it is normal to have difficulty
in selecting the criteria to represent the phenomenon and in
establishing the relationships between criteria.

Social vulnerability model to flood risk
Objectives
Attributes
Factors

• The extremes of the age spectrum affect
the movement out of harm’s way, and
thus, children and the elderly are more
vulnerable to flood events.
• Women have a higher perception of risk
and are better prepared for action (Fekete
2009a).
• However, women can have more difficulty
during a recovery period, often due to their
lower wages and family care
responsibilities (Hewitt 1997; Cutter et al.
2003; Fekete 2009a).

• Safety regulations were implemented
for building structures in Portugal in
1980. The main construction material
for floors and walls determines
physical fragility during a flood event
and indicates the resistance to
damage
• The height of buildings is important
for flood vulnerability
(Schneiderbauer 2007). The upper
floors of buildings can serve to protect
people and their property.
• Collective buildings (e.g.,
rehabilitation centers for disabled
people, nursing homes, hotels,
hospitals, and prisons) are more
vulnerable because they have
additional difficulties in the
evacuation and resettlement process.

• Education is linked to socio-economic
status, with higher educational
attainment resulting in greater lifetime
earnings. Lower education constrains
the ability to understand warning
information and access information
about recovery (Cutter et al. 2003).
• People with higher levels of education
are more capable of performing
emergency measures effectively
(Fekete 2009a). People with lower
levels of education appear to have less
recovery capacity compared to the
average person affected by floods
(Fekete 2009a).

• Floods in urban areas create greater difficulties in
emergency management and greater economic
damage because the temporary water covering
affects economic activities and a large number of
persons (Balica 2012).
• There is also economic damage in agricultural
areas due to the temporary covering of crops by
flood water, but the seasonality of agricultural
practice accords with periods of the year that
have a lower probability of flooding; however,
climate change and the increased frequency of
extreme events may contribute to a change in
this relationship (Morris & Brewin 2013).
• Forest areas may be covered by water, but the
damage will be extremely low. Some species have
greater flood tolerance (survived more than 150
days), and slightly tolerant species can withstand
up to 50 days of flooding (Whitlow & Harris
1979).

Evaluation of Criteria Weights
• The purpose of assigning weights to the criteria is to
express the degree of importance for each factor in
relation to the others in the evaluation process, and it
is a challenge in the decision-making process.

AHP
• The analytic hierarchy process (AHP It compares criteria pairwise on a
fuzzy-linguistic ratio scale and then computes overall relative weights
based on aggregate calculations of all pairwise ratios (Schmoldt et al.
2001; Eastman 2009; Greene et al. 2011).

Decision Rules and Mapping
• The WLC is also known as simple additive weighting, which multiplies
normalized criteria scores by relative criteria weights for each
alternative (Nyerges & Jankowski 2010).
• The OWA extends the WLC by using criteria-order weights to control
the levels of criteria trade-off, allowing decision-makers to place
themselves along a continuous spectrum of risk tolerance
(Malczewski 2006b; Rinner & Malczewski 2002; Yager 1988).

Social Vulnerability
• Social vulnerability had to account for socioeconomic
characteristics or institutional dimensions affecting
the susceptibility of certain populations to climate
change impacts and related risks (i.e., differential
vulnerability) (Soares et al. 2012), and not simply
population exposure.

Value of vulnerability maps…
• Maps synthesizing climate, biophysical and socioeconomic data have
become part of the standard tool‐kit for communicating the risks of
climate change to society.
• Vulnerability maps are used to direct attention to geographic areas
where impacts on society are expected to be greatest and that may
therefore require adaptation interventions.
de Sherbinin et al. (2019)

Maps have been used to identify areas of
social vulnerability to…
• Climate hazards such as flood, drought, and sea level rise (Notenbaert
et al. 2010, Lam et al. 2015, Islam et al. 2013)
• Health impacts such as malaria (Hagenlocher & Castro 2015), dengue
(Dickin et al. 2013),
• Extreme heat (Reid et al. 2009, Weber et al. 2015) and
• Food insecurity (Kok et al. 2010, Thornton et al. 2008, van
Wesenbeeck et al. 2016)

Vulnerability Maps are useful for…
• Planning adaptation assistance (de Sherbinin et al. 2017)
• Understanding the underlying factors contributing to vulnerability
(Preston et al. 2009)
• Emergency response and disaster planning (Blaikie et al. 1994)
• Risk communication and informing risk‐reduction decision‐making
(Patt et al. 2005, Edwards et al. 2007), and
• Land use management (UNDP 2010).

At a minimum, any quantitative vulnerability
assessment requires…
• Definition of the system of analysis (what is vulnerable?),
• The valued attributes of concern (why are they important?),
• The external hazard (to what is the system vulnerable?), and
• A temporal reference (when?) (Füssel 2007).

(a) timeframes of analysis (%), (b) temporal nature of the climate parameters
considered (%)

(c) spatial data layers or parameters considered (no.), and (d) climate‐related
phenomena or parameters considered (no.)

Scale of Analysis
• The choice of bounding box (level of analysis) and spatial unit of
analysis are important, and have ramifications for the approach to
data integration (given multiple formats and scales of data inputs)
and the statistical properties of the inputs and outputs.
• Ideally, the choice of spatial unit would be determined by the scale of
action (Cao and Lam 1997), that is, the scale at which variation in
vulnerability is best observed or at which decisions need to be made.

Scale of Analysis
• This is partly a function of the diverse data streams from social and
natural sciences that are used to construct vulnerability maps, and
the uncertainties that are contained in each type, and partly due to
the emergent nature of vulnerability arising out of complex coupled
systems (Holling 2001, Soares et al. 2012) which forces developers to
use indicators as proxies (indirect measures) of the phenomenon
(e.g., likely or potential harm from impacts) of interest (Hinkel 2011).

Uncertainty
• Uncertainty results from lack of precision or accuracy in the
measurement of the climatic, natural or socioeconomic
variables that contribute to vulnerability, which in turn may
be due to a host of factors such as poor instrumentation,
systematic biases (sampling or model biases), and spatial
interpolation of data between measurement points, all of
which contribute to both systematic and random error.

Uncertainty
Uncertainty can be affected by data processing decisions made
throughout the vulnerability mapping process, such as
• inclusion/exclusion of datasets,
• imputation of missing values (or lack thereof),
• spatial interpolation of data (to fill gaps),
• data normalization or scaling and
• the choice of weighting and aggregation schemes (Nardo et al. 2005).

Uncertainty
• Uncertainty estimates are especially important when
variables at differing scales are collected and overlaid
for interpretation.
• The issue of error induced with the introduction of
each variable can quickly render an analysis little more
than “guesswork” if error is not mapped or in some
other way accounted for,

Treatment of Uncertainty
• Acknowledge data issues that contribute to
uncertainty, including spatial variation in uncertainty,
owing to factors such as the density of measurement
points (or input unit size), sampling errors in
demographic data, and data quality issues across
jurisdictions (de Sherbinin and Bardy 2016).

Treatment of Uncertainty
• Preston et al. (2011) summarized the issue well when they stated that
the failure to address uncertainty “often results in questions
regarding the validity, accuracy and precision of
vulnerability maps, or, in other words, whether maps
themselves represent sufficiently robust visions of
vulnerability to guide stakeholders regarding the
potential for harm.”

Validation
• Many authors have noted the importance of validating vulnerability
maps and the lack of attention that such validation has received in
studies to date (Preston et al. 2011, Hinkel 2011, Tate 2012, de
Sherbinin 2013, Tellman et al. 2017).

Lack of Validation?
This is attributable to a number of factors:
• First, theoretical constructs of vulnerability are proxies for complex
socio‐ecological processes that are difficult to measure and,
therefore, validate (Vincent 2004).
• Second, vulnerability maps often represent vulnerability in a generic
sense – in the absence of the specific articulation of who or what is
vulnerable and to what, it is not clear what the associated outcomes
should be.
• Third, vulnerability maps attempt to represent an inherently
uncertain future, for which there is no observable information or data
to validate maps against.

Is validation necessary?
• Vulnerability maps can be used to open a dialogue around
vulnerability, its meaning, and its causes (Preston et al. 2009). So, if
the objective is to help stakeholders conceptualize rather than predict
vulnerability, validation may be unnecessary.

Is validation necessary?
• Often, vulnerability maps are intended as tools to support
decision‐making regarding the prioritization and targeting of
adaptation interventions and/or investments (Preston et al. 2011, de
Sherbinin 2014). This creates potential incentives for stakeholders to
manipulate the assessment of vulnerability in order to justify a priori
policy objectives.
• In such situations, demonstrating that indices are robust to both data
inputs and outcomes of interest, including the characterization of
their uncertainties and limitations, is important (Saisana et al. 2005,
Hinkel 2011, Tate 2012, Weeks et al. 2013).

Specific methods for validation generally follow one of
two approaches (Esnard et al. 2011, Tate2012).
• The most common is external validation, where vulnerability
metrics are validated against independent outcomes of interest such
as past health outcomes or economic losses from extreme weather
events (Patt et al. 2005, Preston et al. 2009, Preston et al. 2011, Tate
2012, Tellman et al.2017).
Finally, in many parts of the developing world, the data necessary for
external validation simply does not exist, nor is it likely to in the near
future.

Specific methods for validation generally follow one of
two approaches (Esnard et al. 2011, Tate2012).
•Internal validation—statistical tests and
sensitivity analysis—to assess the effects of metric
construction on results (Tate 2012, Carrão et al. 2016,
Heß 2017).

References
• de Sherbinin et al. (2019) Climate Vulnerability Mapping: A Systematic
Review and Future Prospects
• Edwards et al. (2007) Handbook for Vulnerability Mapping
• Fernandez et al. (2015) Social vulnerability assessment of flood risk
using GIS-based multicriteria decision analysis. A case study of Vila
Nova de Gaia (Portugal)

Vulnerability Mapping (Vulnerability Assessment)

Vulnerability Mapping (Vulnerability Assessment)

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