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INT
“App
King
TRODU
INFO
plicatioVuln
D
Date
Dept. o
g Fahd U
C
UCTIO
ORMA
TE
SEC
TERM
on of Gnerabili
Su
Dr. Baqe
e of Subm
SubMd. G
Student
of City a
Univers
CRP 51
ON TO
ATION
ERM-1
CTION
M PRO
GIS in Aity in B
ubmitted
er M. Al-R
mission:
bmittedGolam Mt ID: 201
and Reg
ity of Pe
14
GEOG
SYST
22
N-01
OJECT
AssessiBanglad
to:
Ramadan
May 18,
d by Mortoja
202420
gional P
etroleum
GRAP
EMS
ing Cydesh”
n
2013
lanning
m & Mine
HIC
clone
g
erals
Abstract
The problems addressed in this study are to examine the vulnerability of neighborhoods to
cyclone. The research considers both the social and spatial criteria and thereby has made a
composite vulnerability index to depict the neighborhoods posing potential hazards to
cyclone. The research analyzes the spatial distribution of Cyclone Shelters among the
neighborhoods and superimposes this finding with the composite social vulnerability to
investigate the neighborhoods vulnerable to potential hazard of cyclones. This research
finding can help the emergency planner to launch the evaluation program before the cyclone
attacking based on the degree of vulnerability each neighborhood poses.
Table of Contents 1. INTRODUCTION 1
1.2 RESEARCH QUESTIONS 2
2 METHODOLOGY AND CALCULATION 2 2.1 METHODOLOGY 2.2 CALCULATION 3
2.2.1 CALCULATION OF SOCIAL VULNERABILITY 3
2.2.2 CALCULATION OF SPATIAL VULNERABILITY 4
2.2.2.1 CALCULATION OF SPATIAL DISTRIBUTION OF CYCLONE SHELTERS
(CSS) 4
2.2.2.2 ANALYZING PATTERNS OF CS DEVELOPMENT 4
2.2.2.3 ANALYZING SPATIAL DISTRIBUTION OF CS DEVELOPMENT 5 2.2.2.3.1 MEAN OF CSs 5
2.2.2.3.2 DIRECTION OF DEVELOPMENTS OF CSs 5
2.2.2.3.3 STANDARD DISTANCE 5
3 STUDY AREA 7 4 DATA USED 11 5 LITERATURE REVIEW 12 6 DATA ANALYSIS 15
6.1 ANALYSIS OF SOCIAL VULNERABILITY TO CYCLONES 15
6.2 ANALYSIS OF SPATIAL VULNERABILITY 21
6.2.1 ANALYZING PATTERN OF CS DEVELOPMENT 21
6.2.2 ANALYZING SPATIAL DISTRIBUTION OF CS DEVELOPMENT 22
6.2.2.1 TREND OF CS DEVELOPMENT 22
6.2.2.2 SPATIAL DISTRIBITION OF CS DEVELOPMENT 31
7 CONCLUSION 35
List of Figures
Fig-1: Methodology of the research 3
List of Tables
Table-1: Dataset of Vulnerability Analysis 11
Table-2: Vulnerability Based on CS Capacity 15
Table-3: Vulnerability Based on % of vulnerable population` 17
Table-4: Vulnerability Based on Composite Social Vulnerability Index (CSVI) 20
Table-5: Statistics of Cyclone Shelters 30
List of maps
Map 1: Study Area Location-I 8
Map-2: Study Area Location-II 9
Map-3: Poverty level of the Study Area 10
Map-4: Vulnerability in Terms of CS Capacity 16
Map-5: Vulnerability in Terms of Vulnerable Population 18
Map-6: Vulnerability Based on Composite Vulnerability Index 19
Map-7: CS Constructed up to 1970 23
Map-8: CS Constructed Between: 1972-1975 24
Map-9: CS Constructed in 1976 25
Map-10: CS Constructed Between 1983-1987 26
Map-11: CS Constructed Between 1990-1999 27
Map-12: CS Constructed Between 2000-2008 28
Map-13: Distribution of CS among the neighborhoods 29
Map-14: Direction of CS Development 32
Map-15: CS within ± 1SD 33
Map-16: Overall Spatial Distribution of CS Development 34
Map-17: Vulnerable Population and Spatial distribution of CS 36
1
Application of GIS in Assessing Cyclone Vulnerability in Bangladesh
1. INTRODUCTION
Bangladesh is a country that has been intrinsically associated with natural disaster and
vulnerability. Bangladesh’s geographical vulnerability lies in the fact that it is an exceedingly
flat, low-lying, alluvial plain covered by over 230 rivers and rivulets with approximately 580
kilometres of exposed coastline along the Bay of Bengal (Mohanty, 2007). In addition, there
are three geological faults running underneath the capital of Dhaka. As a result of its
geography, Bangladesh frequently suffers from devastating floods, cyclones and storm
surges, tornadoes, riverbank erosion, and drought as well as constituting a very high-risk
location for seismic activity. With the prolong natural calamities and geographical location
makes it one of the poorest countries among the third world countries. Whole economy of
Bangladesh ruined and level of poverty increases with the prevailing disaster scenario.
Though after 1991 Super Cyclone Bangladesh government has taken some bold steps like;
introduction of multi hazard warning system, National Disaster policy and action plan for
sustainable disaster management .Still its long to take to tackle the disaster effectively as we
seen in post math SIDR scenario.
This paper presents the use of Geographic Information System (GIS) to assess human
vulnerability to cyclonic in Bangladesh. Human vulnerability is conceptualized here as the
exposure to hazard by external activity (e.g. cyclones) and the coping capacity of the exposed
communities to reduce the risk. The assessment looks at the potential exposure of people
impacted by cyclonic damages in coastal areas by integrating the path of cyclone tracks, and
demographic distribution. The paper also explores how the coping capacity of the people is
interrelated to vulnerability. This research application can be helpful to identify vulnerable
populations in a geographic domain, thus enabling governments and agencies concerned with
disaster mitigation to more readily respond.
2
1.2 RESEARCH QUESTIONS
This project is aimed at answering two questions. Firstly, where is the location and extent of
natural hazards such as cyclones? Secondly, what are the relationship between population,
cyclones and vulnerability?
The goal of this research, eventually, is to facilitate effective emergency planning for the
evacuation of populations in coastal areas. Using a geographical information systems (GIS)
framework, various geophysical patterns and social vulnerability indicators are combined to
determine the spatial distribution of evacuation assistance needs and explore the answers to
these questions, on the basis of a case study conducted in a coastal county: Patuakhali,
Bangladesh.
2 METHODOLOGY AND CALCULATION 2.1 METHODOLOGY
For conducting the research, the researcher considers both social and spatial criteria to
analyze the zones posing potential vulnerabiliries to cyclone. Since, during cyclone, the
people having the age group of 0-4 and 65+ are more vulnerable, these two categories have
been considered as most important among the socio-economic components. These dataset
were collected from the Bangladesh Bureau of Statistice (2011). Moreover, the capacity of
cyclone shelters was also recorded as another important criteria for vulnerabilty assessment.
And, for the purpose of analyzing the spatial distribution of facilities those lessen the effect of
cyclone, the geographical distribution of cyclone shelters among the neighborghoods have
been selected as spatial criteria. The cyclone data was collected from the Bangladesh Institute
of Water Modelling (BIWM). Having analyzed, both social and spatial vulnerability were
investigated and eventually combined vulnerability was drawn. However, the methodology
applied to conduct this research is the following:
2.2
2.2.1 C
The pre
based o
neighbo
A. Vuln
Step 1:
Step 2:
Step 3:
(N.B.: H
B. Vuln
Step 1:
Step 2:
Step 3:
(N.B.: H
CALCULA
ALCULAT
e-logic VBA
on cyclone
orhood. How
nerability b
Capacity V
CV_A = CV
CV_AI = C
Higher the
nerability b
Population
PV_A = PV
PV_AI = P
Higher the
F
ATION
TION OF S
A script cod
shelters ca
wever, the s
based on C
Vulnerability
V/Area (in
CV_A/Sum
Value, Low
based on %
Vulnerabil
V/Area (in S
PV_A/Sum o
Value, Hig
ig-1: Meth
SOCIAL V
de of ArcG
apacity and
steps taken t
S Capacity
y (CV) = To
Sq. Km)
of CV_A =
wer the Vu
% of Vulner
ity (PV) = S
Sq. Km)
of PV_A =
gher the Vu
odology of
VULNERAB
GIS 10.1 wa
d on the pre
to measure
y:
otal Capaci
= Vulnerabil
ulnerability
rable Popul
Sum of Age
Vulnerabili
ulnerability
f the researc
BILITY
as employed
esence of v
the vulnera
ity/Total P
lity Index B
y)
lation (VP)
e Group: (0-
ity Index Ba
y)
ch
d to calcula
vulnerable
abilities are
op
Based on Ca
):
-4) & 65+
ased on VP
ate the vulne
population
the followin
apacity
+
3
erability
in each
ngs:
C. Com
– Vulne
(N.B., H
2.2.2 C
2.2.2.1
(CSS)
The sp
question
vulnera
tributar
to ident
applied
2.2.2.2
Calcula
nearest
1. The
•
•
2. Hyp
•
•
Illustra
mposite Vul
erability Ba
Higher the
ALCULAT
CALCULA
atial statist
ns identifie
able populat
ies were int
tify vulnera
to conduct
ANALYZI
ates a neare
neighborin
Nearest N
If the index
If the index
pothesis Tes
H0: The CS
Ha : The CS
ation of AN
lnerability
ased on the P
Value, Low
TION OF S
ATION OF
tical analys
ed above.
tion, cyclon
tegrated in
able popula
this study a
ING PATT
est neighbor
g feature.
eighbor In
x is less than
x is greater t
sting:
S are Rando
S are not Ra
NNA:
Index (CSV
Presence of
wer the Vu
SPATIAL V
F SPATIA
sis of GIS
Data layer
ne shelters c
a GIS fram
ation expos
are the follo
TERNS OF
r index bas
dex = Obse
n 1, the patt
than 1, the t
mly Distrib
andomly Di
VI) = Vuln
f Vulnerable
ulnerability
VULNERA
AL DISTRI
tools was
rs represen
capacity, cy
mework and
sed to signi
owings:
CS DEVE
ed on the a
erved Mean
tern exhibits
trend is tow
buted
istributed
nerability B
e Population
)
ABILITY
BUTION O
used to ad
nting popul
yclone shel
resulting o
ificant cycl
LOPMENT
average dis
Distance / E
s clustering
ward dispersi
ased on CS
n (PV_AI)
OF CYCL
ddress the
lation dens
ter’s locatio
output surfa
lone. Howe
T
tance from
Expected M
;
ion
S Capacity (
ONE SHE
critical geo
sity, percen
on, rivers a
ces were co
ever, the G
each featu
Mean Distan
4
(CV_AI)
LTERS
ographic
ntage of
and their
ombined
GIS tools
ure to its
nce
2.2.2.3
1.
2.
3.
2.2.2.3.
Identifi
Illustra
2.2.2.3.
Creates
features
Illustra
ANALYZI
Trend of C
Mean of CS
Direction o
1 MEAN O
es the geog
ation:
2 DIRECT
s standard d
s: central ten
ation:
ING SPAT
S Developm
Ss
of CS Devel
OF CSs
raphic cente
TION OF D
deviational
ndency, dis
IAL DISTR
ment
lopment
er (or the ce
DEVELOPM
ellipses to
persion, and
RIBUTION
enter of con
MENTS OF
summarize
d directiona
N OF CS D
ncentration)
F CSs
the spatial
al trends
DEVELOPM
for a set of
l characteri
MENT
f features.
stics of geo
5
ographic
2.2.2.3.
Measur
mean ce
Illustra
3 STANDA
res the degr
enter.
ation:
ARD DISTA
ree to whic
ANCE
ch features are concenttrated or di
ispersed aroound the ge
6
eometric
7
3 STUDY AREA
Bangladesh, on the northern coast of the Bay of Bengal, is surrounded by India, with a small
common border with Myanmar in the southeast. The country is low lying riverine land
traversed by the many branches and tributaries of the Ganges and Brahmaputra rivers.
Tropical monsoons and frequent floods and cyclones inflict heavy damage in the delta region.
The country is located on the cyclonic fault line .Almost every year one disaster happens and
causes massive loss to its socio-economic development. Of the 508 cyclones that have
originated in the Bay of Bengal in the last 100 years, 17 percent have hit Bangladesh,
amounting to a severe cyclone almost once every three years (Government of Bangladesh,
2008)
For the purpose of conducting this research, some of the 28 neighborhoods of a coastal region
of Bangladesh named Patuakhali (i.e., is a district in South-western Bangladesh. It is a part of
the Barisal Division) have been selected as Case study. This region is also called the daughter
of sea and is famous for watching both the sun rise and sun set.
The residents of that region are consistently fighting against the onslaughts of cyclone,
inundation and river erosion. During the cyclone SIDR, the area was devastated highly and
recorded more than 1000 death tolls (Government of Bangladesh, 2008). This region is also
the poorest region of Bangladesh having the highest poverty level (52%).
Map 1: Sttudy Area LLocation-I
8
Map-2: Study Area LLocation-III
9
Mapp-3: Povertty level of tthe Study AArea
10
11
4 DATA USED
As it is previously mentioned in the methodology section about the parameters used to
conduct the analysis, the following is the overall dataset being used:
Table-1: Dataset of Vulnerability Analysis
Neighborhood Name
Total Population (Year: 2012)
Area (Sq.Km)
Population (Age: 0-4)
Population (Age: 65+)
Vulnerable Pop(%)
No. of Cyclone Shelter
Capacity
AMKHOLA 27178 45.7530 10.5 6.5 17 3 2850
AMRAGACHHIA 22524 30.6928 9.5 6.5 16 2 1650
AULIAPUR 21304 28.9206 10.1 6.1 16.2 5 5250
BARA BIGHAI 18798 33.2899 10.3 5.6 15.9 2 1525
CHAKAMAIYA 16472 36.9496 10.4 6.6 17 4 3775
CHHOTA BIGHAI 19630 28.5502 9.8 6 15.8 1 700
DEULI SUBIDKHALI
21925 27.8147 8.5 5.8 14.3 3 1200
DHANKHALI 26073 59.4235 10.6 5.5 16.1 13 12825
DHULASAR 18243 45.8362 12.7 5.5 18.2 16 10660
GOLKHALI 32169 65.3698 11.1 5.8 16.9 4 3800
ITABARIA 21478 27.2132 9.2 6.5 15.7 2 2025
JAINKATI 17514 28.8447 10.3 6 16.3 2 1650
KALIKAPUR 14285 14.7051 9.7 6.3 16 2 1550
KARABUNIA 17732 29.5986 8.9 6.5 15.4 1 1200
KHAPRABHANGA
22051 56.1603 8.5 6.5 15 15 14225
LALUA 21562 55.7840 11.3 5.4 16.7 15 13150
LATA CHAPLI 25925 58.6641 10.7 5.2 15.9 13 13325
LAUKATI 27504 32.2448 9.8 6.1 15.9 4 3300
LEBUKHALI 11881 28.9252 10.6 5.9 16.5 3 2100
MADARBUNIA 24177 31.3583 10.4 5.9 16.3 3 2475
MADHABKHALI 20641 28.9309 9.3 6.6 15.9 3 2475
MAJIDBARI 15909 28.7663 9.3 7.3 16.6 1 825
MARICHBUNIA 18153 27.5525 9.8 5.8 15.6 2 1650
MIRZAGANJ 22985 31.5873 9.8 6.3 16.1 1 825
MITHAGANJ 11587 76.5010 11.5 6 17.5 10 9200
NILGANJ 29019 67.6802 10.6 6.1 16.7 15 13200
PANGASHIA 12432 21.5130 9.7 6.3 16 2 1275
TIAKHALI 14342 34.9967 10.3 5.3 15.6 8 8375
Source: IWM & BBS, 2011
12
5 LITERATURE REVIEW
Hazards researchers from a number of disciplines have turned their attention to issues
associated with risk and vulnerability (Blaikie et al. 1994; Bernstein 1998; Kunreuther 1998;
Mileti 1999; Cutter et al. 2000; Slovic 2000; Jaeger et al. 2001). Although the approaches
differ and definitions vary, there has been increasing emphasis on the importance of the
intersection of spatial conditions and social systems (Liverman 1990; Dow 1992; Montz and
Tobin 2003). Spatial conditions are generally considered to define levels of risk, and the
social systems are considered to define variations in vulnerability. Various definitions of such
terms exist; but for these purposes, hazard is used to denote the overall problem; and a
disaster (i.e., an event that has occurred) is defined as some function of spatial risk and
socioeconomic vulnerability (Tobin and Montz 1997).
Recent hazard research has focused on vulnerability and the role that it can play in
exacerbating or ameliorating the effects of disasters. The combination of spatial risk and
vulnerability reflects the degree to which societies or individuals are threatened by, or
alternatively, protected from, the effects, of natural hazards (UNISDR 2001). Vulnerability,
therefore, is a human-induced situation that results from public policy and resource
availability/ distribution, and it is the root cause of many disaster impacts. Indeed, research
demonstrates that marginalized groups invariably suffer most in disasters. Higher levels of
vulnerability are correlated with higher levels of poverty, with the politically disenfranchised,
and with those excluded from the mainstream of society.
The hazards literature has identified many of the components that comprise vulnerability
(e.g., Susman et al. 1983; Blaikie et al. 1994; Monte 1994; Kasperson et al. 1995; Cutter
1996a; Hewitt 1997; Tobin and Montz 1997; Mustaafa 1998) but few clear measures of
vulnerability have been established. An index of vulnerability would help to account for the
dynamic characteristics of the human system. A more recent challenge has been to address
the interaction of vulnerability components in the context of multiple hazards and risk. So far,
however, no predictive, scientifically based model that correlates measures of vulnerability
with the degree of hazard impact has been developed. Progress has been made; notably
through the work of Cutter (1996a,b), Cutter et al. (1997, 1999), and Emrich (2000), which
have attempted to place models on a quantitative footing, but these need consider- able
refining before they can be successfully employed within a policy-making framework.
13
Much of the research undertaken in recent years into assessing community vulnerability has
centered on the hazardousness of place, expanding on the early ideas of Hewitt and Burton
(1971). Some of these studies incorporate a multitude of geophysical threats to an area
(Cutter et al. 2000; Flax et al. 2002), encompassing measures of geophysical risk probabilities
and recurrence intervals (Montz 1994), whereas others explore the spatial extent of areas at
risk for different events (Montz and Tobin 1998; Odeh 2002). In addition, the vulnerability of
populations has also been included, with attention focused specifically on demographic
traits of those at risk, as well as issues of exposure and marginalization. For instance, Montz
and Tobin (1998) look at the location of critical facilities with regard to risk from riverine
flooding and tropical storm surge, whereas Cutter et al. (2000) develops quantitative
indicators to represent social vulnerability and incorporates them into maps that depict areas
at risk for multiple hazards. Frequently, the objective has been to produce indices of social
vulnerability and geophysical risk and ultimately provide a model of community
vulnerability. At this time, however, no single index fits all situations.
Moreover, combining geophysical risks presents methodological difficulties. For example,
Odeh (2002) combines various geophysical risk factors into a hazard score as a multiplicative
function of event frequency, scope (area), and intensity. Although the interpretation of each
of these variables is open to debate, the goal was to produce a common measure of such risk.
Others have summed the geophysical risk as a function of recurrence intervals (Montz and
Tobin 1998; Cutter et al. 2000). The problems are further exacerbated when one tries to
account for human factors of vulnerability. Odeh (2002) used measures of exposure (assets,
population, and resources) within a given region to determine social vulnerability; again, one
might argue with the selection of variables and actual measurements, but the attempt is
laudable. Many different variables have been identified as possibly affecting vulnerability
(Blaikie et al. 1994), but determining which of them are most significant under different
conditions has proved elusive. Clark et al. (1998) used factor analysis, whereas others have
advocated the use of “expert opinion.”
Integrating spatial risk and social vulnerability com- pounds the methodological problems.
Odeh (2002) combines the two scores (hazard and exposure) by multiplying the two indices,
as does Cutter et al. (2000), whereas Montz and Evans (2001) summed the two indices.
Further testing is needed to determine the appropriateness of these approaches. Flax et al.
14
(2002) took a broader perspective in looking at community vulnerability and used a
vulnerability assessment tool developed by NOAA’s Coastal Services Center. In outlining the
model under different conditions, and moving the discussion along, they strongly advocate
proactive action to address emergency response mitigation. Both Odeh (2002) and Flax et al.
(2002) go a long way in furthering our understanding of the vulnerability issue.
Integrating spatial risk and social vulnerability compounds the methodological problems.
Odeh (2002) combines the two scores (hazard and exposure) by multiplying the two indices,
as does Cutter et al. (2000), whereas Montz and Evans (2001) summed the two indices.
Further testing is needed to determine the appropriateness of these approaches. Flax et al.
(2002) took a broader perspective in looking at community vulnerability and used a
vulnerability assessment tool developed by NOAA’s Coastal Services Center. In outlining the
model under different conditions, and moving the discussion along, they strongly advocate
proactive action to address emergency response mitigation. Both Odeh (2002) and Flax et al.
(2002) go a long way in furthering our understanding of the vulnerability issue.
In undertaking such research, extensive use has been made of GIS. This technology is
particularly well suited for such research because it allows for (1) the integration of multiple
data sources, including hazardous locations and vulnerable populations; (2) the geographic
representation of complex data in map form; and (3) the application of spatial analytic
techniques, including buffering and overlay (Chakraborty et al. 1999; Sheppard et al. 1999).
To analyze social context, variables that represent various socioeconomic characteristics are
combined, either as absolute numbers, relative numbers, or quantitative indicators of
vulnerability. Similarly, data layers that represent various aspects of the spatial environment,
including hydrologic and topographic factors, are combined. The integration of these two sets
in a GIS environment provides a composite view of community vulnerability to hazards.
15
6 DATA ANALYSIS
6.1 ANALYSIS OF SOCIAL VULNERABILITY TO CYCLONES
Based on the capacity of the cyclone shelters, the following data table was produced to depict
the vulnerabilities of cyclone based on the capacity of the cyclone shelters.
Table-2: Vulnerability Based on CS Capacity
From the table presented above, it is shown that 16 neighborhoods out of 28 (i.e., 57%) is
highly vulnerable to cyclone based on the capacity of Cyclone shelters. In calculating the
index, it is shown that the higher the index value, the lower the vulnerability is. Based on the
index value, the neighborhood Mirzagonj has been recorded as most vulnerable and Tiakhali
as least vulnerable.
Sl No. Neighborhood Name CV_AI Ranking Vulnerability 1 MIRZAGANJ 0.007037 1
Highly Vulnerable
2 CHHOTA BIGHAI 0.007735 2 3 MAJIDBARI 0.011163 3 4 GOLKHALI 0.01119 4 5 DEULI SUBIDKHALI 0.012185 5 6 KARABUNIA 0.014159 6 7 AMKHOLA 0.014193 7 8 AMRAGACHHIA 0.01478 8 9 BARA BIGHAI 0.015091 9 10 MADARBUNIA 0.020216 10 11 JAINKATI 0.020226 11 12 MARICHBUNIA 0.020429 12 13 ITABARIA 0.021455 13 14 LAUKATI 0.023042 14 15 MADHABKHALI 0.025666 15 16 PANGASHIA 0.029521 16 17 LEBUKHALI 0.037841 17
Moderately Vulnerable 18 CHAKAMAIYA 0.038409 18 19 NILGANJ 0.04162 19 20 KALIKAPUR 0.045693 20 21 DHANKHALI 0.05126 21
Less Vulnerable
22 AULIAPUR 0.052767 22 23 LATA CHAPLI 0.054256 23 24 MITHAGANJ 0.064272 24 25 LALUA 0.067701 25 26 KHAPRABHANGA 0.071132 26 27 DHULASAR 0.078944 27 28 TIAKHALI 0.103328 28
16
17
Table-3: Vulnerability Based on % of vulnerable population
Sl No. Neighborhood Name PV_AI Ranking Vulnerability
1 KALIKAPUR 0.075697 1
Highly Vulnerable
2 PANGASHIA 0.051742 2 3 MAJIDBARI 0.040147 3 4 ITABARIA 0.040137 4 5 LEBUKHALI 0.039686 5 6 MARICHBUNIA 0.039391 6 7 JAINKATI 0.039314 7 8 AULIAPUR 0.038971 8 9 CHHOTA BIGHAI 0.038501 9 10 MADHABKHALI 0.038235 10 11 AMRAGACHHIA 0.036267 11 12 KARABUNIA 0.036197 12 13 MADARBUNIA 0.036163 13 14 DEULI SUBIDKHALI 0.035768 14 15 MIRZAGANJ 0.03546 15 16 LAUKATI 0.034306 16 17 BARA BIGHAI 0.033229 17 18 CHAKAMAIYA 0.032009 18 19 TIAKHALI 0.031012 19 20 DHULASAR 0.027624 20
Moderately Vulnerable21 AMKHOLA 0.02585 21 22 LALUA 0.020827 22 23 LATA CHAPLI 0.018856 23
Less Vulnerable
24 DHANKHALI 0.018849 24 25 KHAPRABHANGA 0.018582 25 26 GOLKHALI 0.017986 26 27 NILGANJ 0.017167 27 28 MITHAGANJ 0.015915 28
The Table-3 presented above shows the vulnerability of the neighborhood based on the
presence of the percentage of the vulnerable population. In this regard, the higher the index
value, the higher the vulnerability is. The result shows that about 68% neighborhoods are
highly vulnerable based on the presence of the percentage of vulnerable population.
18
19
20
Table-4: Vulnerability Based on Composite Social Vulnerability Index (CSVI)
Sl No. Neighborhood Name CVI Ranking Vulnerability 1 CHHOTA BIGHAI -0.03077 1
Highly Vulnerable
2 KALIKAPUR -0.03 2 3 MAJIDBARI -0.02898 3 4 MIRZAGANJ -0.02842 4 5 DEULI SUBIDKHALI -0.02358 5 6 PANGASHIA -0.02222 6 7 KARABUNIA -0.02204 7 8 AMRAGACHHIA -0.02149 8 9 JAINKATI -0.01909 9 10 MARICHBUNIA -0.01896 10 11 ITABARIA -0.01868 11 12 BARA BIGHAI -0.01814 12 13 MADARBUNIA -0.01595 13 14 MADHABKHALI -0.01257 14 15 AMKHOLA -0.01166 15 16 LAUKATI -0.01126 16 17 GOLKHALI -0.0068 17 18 LEBUKHALI -0.00185 18 19 CHAKAMAIYA 0.0064 19
Moderately Vulnerable 20 AULIAPUR 0.013796 20 21 NILGANJ 0.024453 21 22 DHANKHALI 0.032411 22 23 LATA CHAPLI 0.0354 23 24 LALUA 0.046874 24
Less Vulnerable 25 MITHAGANJ 0.048357 25 26 DHULASAR 0.05132 26 27 KHAPRABHANGA 0.05255 27 28 TIAKHALI 0.072316 28
The Table-4 presented above has been derived by deducting the index value of population
vulnerability from the index value of capacity vulnerability. In this regard, the lower the
index value, higher the vulnerability is. The analysis reveals that about 64% neighborhoods
are highly vulnerable to cyclones.
6.2
6.2.1
In this
analyze
distribu
distribu
The res
1 indica
that this
ANALYSI
ANALYZI
section sp
ed. Before
ution of cycl
ution of cycl
ult of Avera
ating that th
s distributio
IS OF SPAT
ING PATT
patial distri
conducting
lone shelter
lone shelter
age nearest
he distributi
on follows th
TIAL VUL
TERN OF C
ibution of
g the anal
rs follows n
rs are random
neighbor an
on of cyclo
he normal d
LNERABIL
CS DEVEL
cyclone sh
lysis, the r
normal distri
mly distribu
nalysis show
one shelters
distribution.
LITY
LOPMENT
helters amo
researcher
ibution or n
uted among
ws the Near
are clustere
.
ong the ne
wanted to
not being hy
g the neighb
rest neighbo
ed in form.
eighborhood
o see whet
ypothesized
orhoods.
or index is l
And, also it
21
ds were
ther the
d that the
less than
t reveals
22
6.2.2 ANALYZING SPATIAL DISTRIBUTION OF CS DEVELOPMENT
6.2.2.1 TREND OF CS DEVELOPMENT
If we consider the trend of CS development, it is seen that it is related with the death tolls
caused by the different periods of cyclone attacking. The Table-5 presented below is the
illustration of it.
Source: Oxfam, Bangladesh, 2008
Our analysis reveals that up to 1970, these neighborhoods had only one cyclone shelter. In
the year 1970, the area experienced a huge death toll and such devastation made policy
makers concerned about the construction of cyclone shelters and about 71% cyclone shelters
were built between the years: 1990-2008.
200,000
40,000
100,000
175,000
40,000
500,000
138,000 150,000
0
100000
200000
300000
400000
500000
600000
Deaths Associated with Noteworthy Tropical Cyclones in Bangladesh
Death Toll
23
24
25
26
27
28
29
30
Table-5: Statistics of Cyclone Shelters
Year
Range
Neighborhood
Name
Total No. of
Neighborhood
No. of
Cyclone
Shelter
Capacity % of Capacity
Up to
1970 Lalua 1 1 250 0.17
1972-
1975
Amkhola,
Chakamaiya,
Dhulasar, Golkhali,
Lata Chapli, Nilganj
6 16 10,760 7.49
1976
Auliapur, Deuli
Subidkhali,
Dhankhali, Itabaria,
Khaprabhanga,
Lalua, Lata Chapli,
Mithaganj, Nilganj,
Tiakhali
10 23 27,600 19.21
1983-
1987
Khaprabhanga,
Lalua, Mithaganj, 3 5 3,500 2.44
1990-
1999
Chakamaiya, Deuli
Subidkhali,
Dhankhal, Dhulasar,
Golkhali, Kalikapur,
Khaprabhanga,
Lalua, Lata Chapli,
Lebukhali,
Mithaganj, Nilganj,
Pangashia, Tiakhali
14 50 41,375 28.80
31
Year
Range
Neighborhood
Name
Total No. of
Neighborhood
No. of
Cyclone
Shelter
Capacity % of Capacity
2001-
2008
Amkhola,
Amragachhia,
Auliapur, Bara
Bighai, Chakamaiya,
Chhota Bighai, Deuli
Subidkhali,
Dhankhali, Dhulasar,
Itabaria, Jainkati,
Kakrabunia,
Kalikapur,
Karabunia,
Khaprabhanga,Lata
Chapli, Laukati,
Lebukhali,
Madarbunia,
Madhabkhal,
Majidbari,
Marichbunia,
Mirzaganj,
Mithaganj, Nilganj,
Pangashia, Tiakhali,
Municipality
28 67 60,175 41.89
Grand Total 162 143660 100.00
6.2.2.2 SPATIAL DISTRIBITION OF CS DEVELOPMENT
This analysis derived the geographic mean of cyclone shelters and is revealed that the mean
is inclined to the southern part of the study area and within 23.9 km from its geographic mean
there are about 68% cyclone shelters are distributed.
Finally,
SD, the
northern
social c
, having der
e composite
n part of th
criteria of vu
rived the ma
e social vul
his study ar
ulnerability
ap for stand
lnerability
rea is very
assessment
dard deviatio
map was s
vulnerable
t.
on ellipse an
super impos
to cyclone
nd standard
sed on it an
e considerin
d distance w
nd is revea
ng both spa
32
within ±1
aled that
atial and
33
34
35
7 CONCLUSION
The maps and quantitative analyses provide an empirical basis upon which the research
questions can be addressed. Firstly, form the composite social vulnerability index, we came
to know that the northern part of the area is highly vulnerable to cyclone. The same result has
also been illustrated based on the geographical distribution of cyclones shelters. From the
analysis of the trend of cyclone shelters development, it is revealed that up to 1980, the
northern part of the study area was less prioritized in getting the establishment of cyclone
shelters. This northern part got its boom after the massive death tolls happened due to cyclone
in the year 1991.
However, the results of this research have important implications for emergency management
and especially for evacuation planning. Evacuation planners cannot ignore the high-risk
areas, because no matter who lives in these areas, appropriate measures need to be in place
before an event. However, other areas are also at risk, because of their population
characteristics and not necessarily because of their spatial risk. Special needs populations are
not concentrated but may well require evacuation assistance in the form of early warning,
mobility assistance, or both. These results, then, call for a two-pronged approach to
evacuation planning, one prong concentrating on high-risk areas and the other on particular
needs of populations in particular areas, regardless of the magnitude of spatial risk. Indeed,
spatial risk is a rather static measure. Once the spatial extent of the high-risk areas has been
identified, plans can be developed and appropriate measures implemented. Social
vulnerability is not a static measure for at least two reasons. First, people move, so the
distribution of those with high evacuation assistance need will change over time. Second,
measures of evacuation assistance need change with different types of hazards. It makes a
difference whether mobility or communication is of primary importance. If mobility is of
primary importance, then those areas with special evacuation assistance needs should take
priority. If communication is of primary importance, then one would want to concentrate
efforts in areas that lack access to resources or in areas with high population densities. The
results of this research demonstrate the importance of evaluating both risk and vulnerability
from several perspectives for emergency management purposes. The emphasis here has been
on evacuation, but the results have more widespread implications. Clearly, it makes a
difference how the factors of concern are chosen and measured, and recognizing and
incorporating the dynamic nature of many of them is important.
36
37
GIS has greatly facilitated emergency management and evacuation planning, as the case
study used here illustrates. Yet, much more needs to be done if we are to develop dynamic,
effective, and efficient evacuation plans. For example, the location and capacity of
evacuation routes will greatly influence the success (or lack of success) of any evacuation
process. Within GIS, data layers representing transportation networks can be included to
identify optimal evacuation routes or locations for proposed emergency response facilities.
These data layers can also be used to evaluate and model “evacuation vulnerability” of
neighborhoods (Cova and Church 1997). Populations with special evacuation needs can be
more or less vulnerable depending on their proximity to transportation routes or facilities.
38
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