4 Methodology 1 Introduction
4.6 Methodology process flow chart
The Figure 27 flow chart below summarises the research framework and methodology developed and implemented in the study.
Research problem
What is the nature of SV to tropical cyclones and how does it change spatially and temporally in a country like Oman?
Research problem
What is the nature of SVto tropical cyclones and how does it change spatially and temporally in a country like Oman? Research limitation and focus
Revealing the nature of risk from natural hazards (cyclones) using an adopted SV model (SoVI) (Cutter, 2003), this thesis focuses on developing SVI using the latest census data (2010) and explores the nature of SV through spatial representation using GIS. Also, it explores the temporal trend of SV to tropical cyclone by carrying out comparisons of SV using the same variables from census data for the years 1993, 2003, and 2010.
Research limitation and focus
Revealing the nature of risk from natural hazards (cyclones) using an adopted SVmodel (SoVI) (Cutter, 2003), this thesis focuses on developing SVI using the latest census data (2010) and explore the nature of risk through spatial representation using GIS. Also, it explores the temporal trend of risk to tropical cyclone by carrying out comparison of SVusing the same variables from census data for the years 1993, 2003, and 2010.
Case study area
The country of study is Oman, and the area is the Muscat capital region, specifically four coastal cities: A’Seeb, Bawsher, Mutrah and Muscat city. All are highly populated and have almost all types of social groups. Throughout
history, these cities have experienced several cyclone events that adversely impacted them.
Case study area
The country of study is Oman, and the area is the Muscat capital region, specifically four coastal cities: A’Seeb, Bawsher, Mutrah and Muscat city. All are highly populated and have almost all types of social groups. Throughout
history, these cities have experienced several cyclone events that adversely impacted them. Key concepts: Disasters, risk assessment, climate
change, natural hazards, disasters, vulnerability, resilience, social vulnerability.
Key concepts: Disasters, risk assessment, climate change, natural hazards, disasters, vulnerability, resilience, social vulnerability.
Research Area: Natural disasters, climate change, tropical cyclone, risk assessment, social vulnerability, factor analysis, principal components.
Research Area: Natural disasters, climate change, tropical cyclone, risk assessment, social vulnerability, factor analysis, principal components. Research sub-questions
The research question will be answered through the following sub-questions.
1. How does SV to natural hazards (tropical cyclone) vary spatially across Muscat governorate coastal cities? 2. How has the spatial pattern of SV changed temporally across the last three census years (1993 – 2010)?
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4.7 Conclusion
In this chapter, we have reviewed methodologies relevant to risk and vulnerability assessment. No consensus was found on risk and vulnerability terms, conceptual frameworks, or common indicators. Five conceptual models of SV found in the literature were reviewed in this study, and it is evident that few have been empirically operationalised. It is difficult to have one general representation of vulnerability that can be used in the disasters field due to the dynamic nature vulnerability and complexity of the drivers in each environment and the particular geography. The model selected to address our main overarching questions is Cutter’s SV (SoVI) model (Cutter et al., 2003), amended by adding the weighting method proposed by Rygel et al. (2006). Our study area has its own local conditions shaped by culture and geography, hence locally specific
Methodology
1. Selecting the most representative variables in terms of influence on vulnerability to tropical cyclones 2. Adopting a suitable model to construct a SV index used to explore the current social vulnerability, and 3. Using the developed SV model, explore the trend of SV in the study area through the last two decades.
Methodology
4. Selecting the best representative variables that influence vulnerability to tropical cyclone
5. Adopting a suitable model to construct a SVindex used to explore the current social vulnerability, and 6. Using the developed SVmodel, explore the trend of SVin the study area through the last three decades.
Secondary Data
• Census data for 24 variables to select the SV variables that influence the current SV to tropical cyclones and thence develop the SV indicators to work out the SV index.
• Socio-economic data from the census data of three consecutive censuses: 1993, 2003, and 2010.
Figure 1. Analyttical process adopted in the study Secondary Data
• Census data for 24 variables to select the SVvariables that influence the current risk to tropical cyclones and therefore develop the SVindicators to work out the SVindex.
• Socio-economic data from the census data of three consecutive censuses: 1993, 2003, and 2010. Step one:
1. Identify suitable variables that influence vulnerability to tropical cyclones.
2. Construct SVI using PCA for the year 2010.
3. Develop an SV assessment model. 4. Spatially represent SVI using GIS.
Step one:
5. Identify suitable variables that influence vulnerability to tropical cyclones.
6. Construct SVI using PCA for the year 2010.
7. Develop a risk assessment model. 8. Spatially represent SVI using GIS.
Step two: Explore SV temporal variation using data set of last three censuses: 1993, 2003 and 2010.
1. Spatially represent the SVI for the three-census data.
2. Reveal the trend of risk by assessing changes in vulnerability through time using cluster analysis.
Step two: Explore the SVtemporal variation using dataset of last three censuses: 1993, 2003 and 2010.
3. Spatially represent the SVI for the three-census data.
4. Reveal the trend of risk by assessing changes in vulnerability through time using cluster analysis.
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variables have been added to the wider list of generic variables suggested by the literature review, so as to develop a geographical context specific model. The next two chapters will supply further detail of the SV model’s application.
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