「Adaptation」カテゴリーアーカイブ

Day_164 : Development Environment Disaster Cycle Model

As mentioned before in Day_56, it is clear the model, development-environment-disaster cycle model is an analyzer that can be considered in a wide range of areas. In other words, this analysis perspective raises the sociological position of natural disasters, and the stepping stone of their historical and geographical connections become clearer. We believe that it will even be possible to provide various perspectives to prevent it from being guided.

Day_56 : A cyclic model of development-environment-disaster

Analytical Viewing Angle by Causal Cycle Model: Case of Isewan Typhoon Disaster and Indian Ocean Tsunami Disaster

In this section, Isewan typhoon disaster and Indian Ocean tsunami disaster are specifically analyzed using the analysis view angle, the causal cycle model of development, environment, and disaster. The first is the Isewan Typhoon that hit Nagoya on September 26, 1959. The disaster was a turning point of disaster management in postwar Japan, but focusing on driftwood damage, which is one of the important aspects of the disaster, the economic recovery of postwar Japan, trade with the United States, and Japan. Forest management, natural disasters such as landslides, the problem of hay fever, which is also called national illness, and the inter-relationship between deforestation and natural disasters in the Philippines, which becomes today, will become clear. Second, regarding the Indian Ocean Tsunami that caused enormous damage on December 26, 2004, mainly in the countries around the Indian Ocean, the damage in Thailand will be analyzed. This analysis reveals the development-environment-disaster in Thailand and its relationship with Japan and Western countries.

The figures are shown as follows:

Figure 1: Interconnections of Typhoon Isewan Disaster

Figure 2: Interconnections of Indian Ocean Tsunami Disasters in Thailand

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Day_163: PAR model : Hazard and Vulnerability (3)

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)

Day_147: PAR model : Hazard and Vulnerability

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.

Day_159: PAR model : Hazard and Vulnerability (2)

Day_147: PAR model : Hazard and Vulnerability

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

Day_147: PAR model : Hazard and Vulnerability

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…..

Day_90 : (Re)Evacuation research literature analysis-A Text mining

Evacuation’s research literatures are divided into two categories for this analysis. One is natural disaster’s research literature conducted by all specialties. The other is social scientist’s research literature on natural disasters. The database, the Springer link, is selected to conduct all field’s evacuation research literature analysis. The E. L.Quarantelli Resource Collection (See the Website), Disaster Research Center of the University of Delaware was chosen as a target database for analyzing social science literature. The collection is one of the world’s most complete ones on the social and behavioral science aspects of disasters. These two databases’ literatures were analyzed by a text mining. To conduct the text mining, the RH Corder was used.

The following is just one result example, a content analysis of the springer link database.

  1. Search words are “evacuation AND urban AND (tsunami OR flood OR typhoon OR hurricane)”
  2. The number of extracted literature is 824 (2000-2014)
  3. The titles, key words, and abstracts of the 824 were combined into one text file
  4. The extracted words which appear over 20 times in the text are shown in Table1
  5. A co-occurrence network analysis result is indicated in Figure 1

Table 1       Extracted Words (over 20 times) and Frequencies (sorry, original Japanese version’s words are left)

wordsfreq

140715_村上先生_共起ネットワーク2

Figure 1 A co-occurrence network analysis result

In Figure1, the circle sizes around the words (Nodes sizes) mean the frequencies of the words appeared in the text. Edges mean the connections between the words. Then, you can see the above analysis (by color) result.

For instance, emergency response-preparedness-decision-support with “event” are combined with evacuation as key words. Climate-change-impact was also detected with coastal-adaptation. We can estimate that detected Taiwan-assess-community-resilience represents the Typhoon Morakot disaster. (Then, this is confirmed by returning to the original text.)

Murakami et al. (Murakami, Nakasu, Shimamura, Goto, and Ogawa, 2015) is referred.

Day_67 : Disaster Terminology

The disaster terminology is very important to have a common picture to discuss among the related people. The UNISDR provides a very useful website to confirm the term. For example, the adaptation is defined as “The adjustment in natural or human systems in response to actual or expected climatic stimuli or their effects, which moderates harm or exploits beneficial opportunities”. The following is the website.

https://www.unisdr.org/we/inform/terminology