As discussed on Day 147, now we are investing the social vulnerability index of the district, sub-district, and village levels in Ayutthaya. To calculate the social vulnerability index, exposure, susceptibility, and capacity data are examined. Notably, the district level of the index is figured out, as shown in Figure 1, using principal component analysis.

（Please enlarge the screen to see the figure well. Darker blue means more vulnerable. The detailed factors of PCA will be explained later. ）

Figure 1: Social Vulnerability Index Industrial Complex Area(SVI-ICA) Ref. 1)

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As mentioned above, the district level of the social vulnerability index can be figured out by statistical data. However, sub-district and village levels data are challenging to collect. We also need to understand the capacity is a factor that includes not only hard but also soft countermeasures against natural disasters, as discussed before. Especially, capacity-soft is not stable by time with circumstances and could be changing from time to time. The stats data is not enough to indicate their actual capacities.

Based on the above fact, the capacity assessment is considered to fill the gaps. The capacity assessment method was based on the FDPI project experience.

The population of the target areas is indicated below:

Table 1:  The Population of the Tambons　（Source: registration office 2019）

Below is the category (indicators) to measure the capacity.

Table 2: Indicators and Sub-Indicators for Capacity Assessment

The results are as indicated in Figure 2.

Figure 2: Four Sub-District Capacity Assessment 

The findings show the western side and eastern side have a big gap, as you can see in Figure 2.

Figure 3 explains the education and training part is much different among the four target sub-district. The results mean we can monitor and evaluate their progress after we provide education, training, system, or so on there.

Figure 3: Capacity Assessment Analyses

For example, each sub-indicators are examined as follows:

Figure 4: Information and Education Sub-Indicators Gaps 

The analyses (the detailed sub-indicators from IE1 to IE11) will be explained later.

Related Book and Info.

At Risk: Natural Hazards, People’s Vulnerability and Disasters

*This is the baseline research for the SATREPS project.

http://disasterresearchnotes.site/archives/3746

As discussed on Day 147, now we are investing the social vulnerability index of the district, sub-district, and village levels in Ayutthaya. To calculate the social vulnerability index, exposure, susceptibility, and capacity data are examined. Especially, the district level of the index is figured out as shown in Figure 1 using principal component analysis.

Figure 1: Social Vulnerability Index Industrial Complex Area(SVI-ICA) Ref. 1)

The district level of the social vulnerability index can be figured out by statistical data. However, sub-district and village level data should be difficult to collect such data. Based on the fact, the capacity assessment is firstly conducted to the target four sub-districts as indicated in Figure 2. The capacity assessment method was based on the FDPI project experience.

Figure 2: Four Sub-District Capacity Assessment 

The findings say the western side and eastern side have a big gap as you can see in Figure 2.

Figure 3 indicates the education and training part is much different among the four target sub-district. This means we can monitor and evaluate their progress after we provide education, training, system, or so on there.

Figure 3: Capacity Assessment Analyses

The detailed examination will be explained later.

*Exposure, Susceptibility, and Capacity data list will be shown later. The theoretical frame is base on the PAR model. The below book can be referred.

At Risk: Natural Hazards, People’s Vulnerability and Disasters

**This is the baseline research for the SATREPS project.

Ref. 1) Tadashi Nakasu, Ruttiya Bula-or, Sutee Anatsuksomsti, Korrakot Positlimpakul (2019)Social Vulnerability Changes and Sustainable Development in the Flooded Industrial Complex Area The 2nd multidisciplinary International Conference on Humanities (ICH 2019) “Innovation and Transformation in Humanities for a Sustainable Tomorrow.” 30-31 October 2019, School of Humanities, Universiti Sains Malaysia, Penang, Malaysia

Disaster researchers often refer to the PAR (Press and Release) model to understand the risk.
The PAR model was described in the book “At Risk”. This book is a kind of bible for disaster researchers. Disaster Risk is described as an overlapped area between Hazard and Vulnerability.

The Disaster risk should also consider “Exposure” and “Capacity”. The capacity has mainly two parts, Hard and Soft. In short, Capacity Hard (CH) means tangible factors and  Capacity Soft(CS) means intangible ones. For instance, infrastructure is CH and education is CS. The Disaster risk usually can be identified by the following picture. Figure 1 indicates the above.

Figure 1  Disaster Risk

Using the below equation, disaster risk would be identified.

Disaster Risk = H*E*V/ (CH+CS)

Each factor such as E (Exposure) could be identified by mainly statistic data in the target area.
To do this, the indices can be established. The data to contribute each factor should be carefully examined.

Figure 2 is the national level Index Image of Thailand.

Figure 2 Social Vulnerability

To be continued…..