Day_167: Imagine from Disaster Damage Statistics

NIED-DIL mail magazine: 6
Imagine from disaster damage statistics
Contribution day and time: 2013/08/19

There is an index called the World Risk Index. The world risk report ranks Bangladesh as one of the high-risk countries in the world in 2019.

Indonesia and Haiti were easy to imagine, linked to the damages caused by recent earthquakes. Previously, there was an opportunity to learn from a land environment perspective about the past major disasters that struck Bangladesh, especially the large-scale cyclone disasters in 1970 and 1991. During a study session at the institution, I leaned the reported number of the casualties caused by the disasters was 500,000 and 140,000 people each. I was surprised to see the large numbers, but I was wondering why these numbers are so rough. When I looked at the table showing the breakdown numbers, I felt, “Oh!”

Building damage, human suffering, and livestock damage are listed. For example, the cyclone disaster in 1991 resulted in 1,630,543 house damage, 140,000 human suffering (dead or missing), and 584,471 livestock damage.

Yes, human suffering seemed to be a rough figure, while house damage and livestock damage were written down to one digit. And when we looked at what kind of country Bangladesh was like such as caste, religion, and livestock.

The background of the numbers, such as meaning, etc., has come into view. Regarding the number of dead and missing people in 1970, there are no accurate figures, and reports from 200,000 to 550,000 people have been reported in various fields.

Day_117 : Bangladesh-Disasters, Lands, and Statistics (2)

When there is a disaster, numbers about the damage come out, but I thought it was a very important viewpoint to think about where the numbers came out and what you can imagine from those numbers.

By the way, the web site is
Issued July 5, 2010 No. 6

Related Books and Info. for Further Understanding

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Day_155: Recoveries from Disasters

I will update a column of the NIED e-mail magazine which I wrote a long time ago because the content is not faded with time. (I will do this step by step in Japanese and English) I will also add comments to update the situation.

Published March 5, 2010
NIED-DIL e-mail magazine: Recoveries from Disasters

■ Disaster Recoveries ■
Global attention is being focused on how recovery will take place after the Haiti earthquake. I have studied a lot about disaster recovery. Still, as a valid theory of thinking, a researcher named Haas says, “A rapidly growing city will recover quickly after the disaster but will remain unchanged and stagnant or downhill cities will recover very slowly after the disaster or will quickly decline “(1977). When considering what kind of area or a growing city or not is in this case, the population before the disaster could be examined as an indicator. I’ve researched a lot and predict that no matter how massive the distraction maybe, an area with a growing population may be easier to recover. For example, in the city of Nagoya, due to the Typhoon Ise Bay disaster, the scale of economic and social damages were plentiful, and the amount of aid was small, but it was said that it was revived in less than a year. In comparison, the scale of economic and social disasters in New Orleans due to the Hurricane Katrina disaster was not so large relatively as statistics, but the amount of aid was enormous. Nevertheless, it may be useful to say that five years have passed and that recovery has not yet been well. New Orleans was even expressed to be a surviving city, even before the disaster. Regarding the recovery of the stricken area of ​​the Indian Ocean tsunami, it is not clear here, but there were many similar trends.
Let’s return to the example of Haiti. Examination of the population growth rate in Haiti (Port-au-Prince) showed that it was overgrowing until the disaster occurred. Haiti’s revival should be relatively quick, given the population index alone. However, it is also possible that Haiti has an entirely different social situation that cannot be applied in the above example. You may have to think that Haiti’s revival will be heavily influenced by the very elusive variables of political steering and social conditions. There is an article in the magazine “ Economist ” that fears that similar problems may occur in Haiti, such as the problem of contributions and aid in the Indian Ocean cases where oversupply was unevenly distributed, and the damage was widened.
What do you think of Haiti’s recovery?

The data below indicates a lot about the theory.

Haiti Population Data

Port-au-Prince Population Data

Day_154 : (In Japanese) 災害からの復興




Day_146 (Rev): A Text Mining : Trends of disaster research on aging

Have just conducted a text mining as follows:

Overviews of the Literature concerning elderly and disasters by text mining.
1. Search the keywords in Web of Science (Core Collection)
2. Selected Information (Titles and Abstracts) was gathered into one text file.
3.1st contents analysis has been carried out and omitted the unnecessary words.
4. Cleaning process
5. 2nd contents analysis has been carried out. >>This process was repeated
6. Research findings are examined.
7. Examine the extracted words and phrases by reaching to the original abstracts and original papers.

*To do the text mining, KH Corder was used.

Table 1 Keywords


Co-occurrence network_elderly
Figure 1 A co-occurrence network analysis

Table 2  The hit numbers with disaster management cycles’ stages

disaster management cycle elderly

Table 2 indicates that elderly issues on disasters are more discussed in relation to “response” and “preparedness” than “recovery” and “mitigation” in the disaster management cycles’ stage.

correspond analysis elderly
Figure2 Correspond analysis (The related words with disaster management cycles’ stages)

For example, Figure 2 suggests the close relationship between response and related words such as planning, management, medical, evacuation, vulnerability, and patient. This suggests the elderly’s difficulties for evacuation because of their physical conditions.

**This article is a revised version of Day_26(rev)


Collection and Analysis of Overseas Disaster Evacuation Related Papers and Documents(in Japanese)

Day_137 : Aging Asia to Natural Disasters -Thailand(2)-

Day_68 indicates Thai population in 2012 was 64,460,000 and the proportion of those over 65 is 8.6 percent (11 percent in 2016) compared to 3.1 percent in 1970. This shows that Thailand is facing an aging society and the World Population Prospects* predicts this trend will accelerate. This situation is not exclusively in Thailand, but can likewise be viewed in almost all Asian nations. In addition, Asia is the most vulnerable in terms of natural disasters such as 7 of 10 of the deadliest natural disasters (1980-2014) took place in this region(Day_79). The World Bank mentions how Thailand faces the aging society from an economic development perspective, however, we also need to recognize this from a disaster reduction viewpoint. Okushiri island case can give us a significant insight(Day_75). This gives us a challenge of how disaster-resilient society can be established in the situation.

The World Bank notices**:
The Thai population is aging rapidly. The declining share of the working-age population will affect economic growth.
– As of 2016, 11% of the Thai population (about 7.5 million people) are 65 years or older, compared to 5% in 1995.
-By 2040, it is projected that 17 million Thais will be 65 years or older – more than a quarter of the population.
-Together with China, Thailand has the highest share of elderly people of any developing country in East Asia and Pacific.
-The primary driver of aging has been the steep decline in fertility rates, which fell from 6.1 in 1965 to 1.5 in 2015, as a result of rising incomes and education levels and the successful National Family Planning Program launched in 1970.
-The working-age population is expected to shrink by around 11% as a share of the total population between now and 2040 – from 49 million people to around 40.5 million people.
This decline in the working-age population is higher in Thailand than in all other developing East Asia and Pacific countries, including China.

Day_79 : Disaster trends in developed and developing countries

Day_75 : Okushiri Island (2)

* World Population Prospects

** World Bank, 2016
Thailand Economic Monitor – June 2016: Aging Society and Economy

Disaster data and statistics can be referred by the following link:

Day_133 : Science, Technology, Population, and Lessons for DRR

Japanese people have tended to trust the government and science & technology so much.
These are one of what we learned from recent disasters. After the second world war, Japanese gov. has built high sea walls along the coastline especially potential risk areas all over Japan. We have also developed warning systems along with rapid economic growth. Not only those, but we have also developed soft countermeasures such as disaster education and training, especially after the 1995 Kobe Earthquake. After the Great East Japan Earthquake and Tsunami (GEJET) disaster, we have realized what has happened because of our over trust to the government and science&technology. This is why Japanese gov. has particularly focused on the community since the disaster, establishing a new frame on the community disaster planning in the disaster countermeasure basic act. The recovery plans on the affected coastal communities tended to change more integrated manners and so did disaster countermeasures than before, looks like turning back to the time when we did not have advanced science and technologies.

We need to know the limitations of the gov. and science&technology’s roles. We also can consider the demographic change to do the job for disaster risk reductions. For example, Japan is facing a severe aging society, so our government resources will be decreasing to cover the situations. We need to have more self-help and mutual help than public help.

Learning from the lessons and past wisdom with those considerations is also very important. “Inamura no Hi” is one of the important lessons we can learn from the past.

Day_88 : Inamura no Hi

“Inamura no Hi” is a story of a man who noticed a precursor of a large tsunami at the earliest stage and led village inhabitants to a higher ground by burning harvested rice sheaves. This story was based on a true story at the time of Ansei-Nankai Tsunami (1854), which claimed around 3,000 lives in the coastal areas of Western Japan (ADRC).

Hirokawa Town’s video well explains the background of the story in short and their tsunami disaster education.




Day_100 : A Human Suffering Exacerbation-Data from Greater New Orleans Community Data Center

The Greater New Orleans Community Data Center (GNOCDC) website was found after the field survey on Hurricane Katrina in Louisiana and Mississippi in 2005. I was so amazed. This is the one of the demographers great contributions to disaster research.

The site provides the information of the pre-Katrina situations by parish and also by ward. This is very useful to examine the social backgrounds of the areas in detail.

gnocdcPrekatrinaFigure 1 GNOCDC (Pre-Katrina data site)

The paper on Karina disaster using these data is to explain how human sufferings were exacerbated by the stage with the social background as shown in Figure 2 (Nakasu, 2006 :Sorry in Japanese, however, summary and figures are in English).

human suffering
Figure 2 Victimization Process

Figure 3
Victimization Process by Stage

Table 1 Found Dead Bodies in New Orleans  

dead in neworleans

The process can be divided into five stages with time such as A) Pre-disaster B) Direct damage C) Social disorder D) Life environment  E) Reconstruction and recovery. Then, these are examined with the social background data (Figure 3).

For example,  1) Pre-disaster stage, I picked up an evacuation aspect to explain the social background of this stage.

Using the GNOCDC database, I could check the possession ratio of the vehicle in some areas.

Figure 4 No Vehicle Available Ratio (GNOCDC)

Table 1 and Figure 4 show the people in Lower 9th ward, one of the most severely affected areas, had a low possession ratio of the vehicle. This can explain so many residents needed to have government help to evacuate and they could not evacuate before the Hurricane hit.

The general social background, such as ethnic groups, household incomes, and others with other stages of examinations will be discussed later.

The Great Deluge: Hurricane Katrina, New Orleans, and the Mississippi Gulf Coast (English Edition)

Day_92 : Disaster Recovery Theory (2)

The followings are the two disaster recovery theories used for this study.
1) Theoretical framework 1
Disasters contribute to change, they do so primarily by accelerating trends that are already under way prior to impact <Bates et al.(1963);Bates(1982);Bates and Peacock (1993)><Haas et al.(1977)>

2) Theoretical framework 2
Disaster Recovery Process is influenced by
① Devoted aid volume from outside society
② Disaster scale
③ Community strength (Social System Strength) <Hirose (1982) >

The first theory was already explained.
Concerning the second theory, the following Figure 4 can be referred.
Figure 4  Disaster Recovery

The disaster recovery progress is influenced by the following three indicators: S, Disaster scale, H, devoted an aid volume from outside society, and P, community strength(social system strength). For example, the large scale disaster has a high S which makes the recovery progress slow (Figure 2 indicates ①minus).

Table 1 Four Cases


The above Table 1 explains four disaster cases. Two growing communities before the disaster such as Nagoya city destroyed by Typhoon Isewan and Kobe city hit by Kobe earthquake.The other two were declining communities such as Nagaoka city (Yamakoshi village) affected by the Niigata Chuetsu earthquake and New Orleans devastated by Hurricane Katrina.

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As you can see the above Table 1, Nagoya and Kobe have recovered rapidly after the disaster even if they had extensive Ss (disaster scales) with small Hs (help, aid from outside).
On the contrary, Yamakoshi and New Orleans have not recovered well after the disaster even if they had small Ss with large Hs.

Figure 5  Community Strength (Social System Strength)

Therefore, we can recognize the P(Community Strength) is the key indicator to influence the progress of the disaster recovery as shown in Figure 5.

These are presented at ICFM5 (The 5 th International Conference on Flood Management)

Day_89 : Disaster Recovery Theory (1)

First, the theoretical examination’s concept is explained and two disaster recovery theories are introduced. Second, the first theory is explained and studied. Third, the second theory is explained and examined*.

The concept is explained as follows:

The concept

Figure1  Disaster Recovery Concept

The followings are the two disaster recovery theories used for this study.
1) Theoretical framework 1
Disasters contribute to change, they do so primarily by accelerating trends that are already underway prior to impact <Bates et al.(1963); Bates(1982); Bates and Peacock (1993)><Haas et al.(1977)>

2) Theoretical framework 2
Disaster Recovery Process is influenced by
① Devoted aid volume from outside society
② Disaster scale
③ Community strength(Social System Strength) <Hirose(1982)>

The first theory is confirmed by some cases. You can see the following figures such as Kanto earthquake, Fukui earthquake, Typhoon Isewan in Japan, and Hurricane Katrina in US.
 Disaster Recoveries in Japan

Figure3 The Disaster Recovery from Hurricane Katrina in US.

To be continued…………………..

*This is  the presentation summary. The presentation was made in 2011 after the tsunami in Japan.

Related Info. and Books

Day_75 : Okushiri Island (2)

The 1993 southwest-off Hokkaido earthquake hit okushiri island severely. The number of casualties was 165. Okushiri town had faced population decreasing and aging issues before the disaster. After the disaster, Okushiri town had a lot of aids, especially from inside of Japan. The Japan had a very good economy at that time, so the situations enabled them to have such huge aids. Even though the large economic assistance, the town’s demographic tendency before the disaster was facilitated. Total population is from 4,604 (1990) to 2,662 (2015), an aging proportion, over 65, is from 15.6% (1990) to 38.6% (2015)*.

The below is the graph which indicates the demographic changes of the island.
okushiri population

Some disaster recovery theories can be referred to explain this tendency clearly.We should learn from this lesson to consider for our common sustainable futures.


Day_43 : Okushiri Island