Volume 4, Issue 4 December 2019

Special Contribution

Gross National Safety Index for Natural Disasters (GNS)
Kazuya Itoh1, Mamoru Kikumoto2, and Tomofumi Koyama3
1 Associate Professor, Urban and Civil Engineering, Tokyo City University
2 Associate Professor, Institute of Urban Innovation, Yokohama National University
3 Associate Professor, Faculty of Societal Safety Sciences, Kansai University
1. Introduction
Japan is one of the countries prone to various natural disasters such as earthquakes, floods, sediment disasters, andvolcanos. In recent years, damage due to natural disasters is becoming more and more serious. It has suffered from thedevastating damage caused by not only earthquakes but also a succession of localized torrential rains. However, current
city’s flood protection systems are not enough since the budget and personnel used for disaster prevention and mitigation
measures are limited.
Needless to say, establishment of comprehensive disaster prevention and mitigation system is urgent and essential, which
is an effective combination of hardware measures such as infrastructure and reinforcement of structures, and software
measures such as hazard map development/disclosure and disaster prevention education.
Immediately after the Great East Japan Earthquake, Prof. Kusakabe advocated to create an index of nation-wide safetyindex, together with Gross Domestic Product (GDP) and Gross National Happiness (GNH), to steadily transform Japan toresilient land and coined GNS. GNS is an abbreviation of Gross National Safety for natural disasters, which is an index
expressing quantitative risks for natural disasters. Roger Pulver (2012) showed his keen interest in GNS and stated in his
article, “Japan’s disaster must prompt a radical rethink of citizen’s quality of life”, that “Here’s my point: The aftermath of
the triple calamity in Tohoku has shown that Japan’s government and industry have been neglecting the safety and theintegrity of the people and the land. A paradigm of growth for the 21st century must consider the kind of scientificmethods advocated by Kusakabe.”, “The creation of investment security and the husbanding of the land can bring about
a merger of the three Gs: GDP, GNS and GNH. Any country or region striving for this would be a magnet for investment
and a beacon of hope for the world.” Fig. 1 shows the article issued in the Japan Times on March 11, 2012.

Fig. 1. Japan Times on March 11, 2012

In 2012, we have formed a research committee in Japanese Geotechnical Society(JGS) Kanto branch, with an aim ofdeveloping a safety index system for natural disasters for policy and decision makers to prioritize prevention andmitigation measures to be implemented. As a result, we published leaflets of GNS2015 and GNS2017 which were
presented the concept of the GNS index and the way to calculate the year of 2015 and 2017 version GNS, together withthe calculated results of GNS in the prefectural scale. Fig. 2 shows the leaflets of GNS2015 and GNS2017. These leaflets
can be downloaded as PDF files from following URL;
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Volume 4, Issue 4 December 2019

GNS2015 (Japanese Version): http://www.jgskantou.sakura.ne.jp/group/pdf/GNS2015.pdf
GNS2015 (English Version): http://www.jgskantou.sakura.ne.jp/group/pdf/GNS2015English.pdf
GNS2017 (Japanese Version): http://www.jgskantou.sakura.ne.jp/group/pdf/GNS2017.pdf

GNS2015
Japanese Ver.

GNS2015
English Ver.

GNS2017
Japanese Ver.

Fig. 2. Leaflets of GNS
In GNS2015 and GNS2017, five natural events were considered including earthquake, tsunami, storm surge, sediment
related to disaster events, and volcanic activity. While flood disaster, one of the most serious natural disasters in Japan,
was not considered in GNS2015 and GNS2017 because the river basin level is usually needed for the calculation so that
it is difficult to consider it in the prefectural scale using statistics data. Moreover, hardware and software measures fordisaster prevention and mitigation are usually planned and carried out on various administrative units such as nation,
prefecture, and/or municipality. Therefore, the disaster risks also should be evaluated in multi-scale (from national tomunicipal scales) to increase the effectiveness of investment for disaster prevention and it is also necessary to increase
resolution of the damage estimation maps and use consistent statistical data to evaluate natural disaster risks in municipal
scale as well.
Furthermore, in Japan, Basic Act on the Advancement of Public and Private Sector Data Utilization was established in2016, and statistical and geographic information are becoming accessible for free. Statistics and GIS could make it possible
not only to provide damage prediction depending on each flood basin but also to consider the most adequate
countermeasures by municipality.
In this study, the new calculation methodology using GIS (Geographic Information System) was introduced to evaluate“exposure” of natural disasters more precisely in municipal scale. The newly extended GNS was applied to evaluate
natural disaster risks of each municipality in East Japan. Additionally, the “exposure” of flood disasters can be evaluatedby superposing damage estimation maps and population distribution using GIS (Mukai et al., 2018).
2. GNS Concept
2.1 Definition of GNS
The natural disaster risk, R can be expressed as the function of hazard H, exposure E, vulnerability V and resilience Re, by
the following equation:(1)
R = f (H , E,V ,Re)
Here, H×E means “exposure” in a broad sense, which is determined by population distribution, geology and topography
in a particular region. Also, V x Re is a value expressing the relation between society and natural disasters. In GNS2015,
vulnerability can be expressed by V = V (V, Re). Thus, the natural disaster risk R can be expressed as follows;(2).
R = f (H , E,V )The Eq (2) is a form of function adopted in the GNS calculation. One of the simplest forms may be
R = H  E V
(3)
The Eq (3) is the actually used equation for GNS2015 and 2017. One of the features of the Eq (3) is that R becomes null
when one of the three parameters are null. Namely, in the cases where no physical event causing hazard occurs (H=0),
nor no people lives in the affected area caused by hazard (E=0), society is resilient enough against natural disasters, R
10Volume 4, Issue 4 December 20191) Earthquake, 2) Tsunami, 3) Storm surge,
4) Sediment disaster, 5) Volcano,6) Flood disaster1) Hardware measures2) Software measures

Fig. 3. Concept of GNS
becomes null. In the course of development of GNS2015, the following points are taken into consideration in such a waythat the decision and policy maker responsible for budget plan can easily access; 1) Data to be used should be free accessfor the purpose of continuous updating, 2) Data to be used should be available at the prefectural level to compare one
prefecture to another, 3) Prioritizing the items affecting for improving natural disaster measures and the items with higher
propriety should be selected and 4) The values of hazard, exposure and vulnerability should be hierarchically calculated
by weighted linear summation as shown in Fig. 3.
2.2 Exposure
As for “exposure”, the following 6 natural disasters are considered; (a) earthquake, (b) tsunami, (c) high tide (storm surge),(d) sediment disaster, (e) volcanic disaster, and (f) newly added flood disaster. In this study, in order to precisely evaluate
the “exposure” of natural disasters except for earthquake and volcanic disaster in municipal scale, the population
distribution exposed by each natural disaster was calculated using GIS software (by superimposing population data inmunicipal scale and estimation of damage caused by each disaster) (Mukai et al., 2018). While, the “exposure” ofearthquake and volcanic disaster was evaluated in the prefectural scale due to the difference in the time scale and thefrequency of occurrence. The frequency coefficient, which varies from 0 to 1, was defined by the following equation,
Fi = 1 − exp(− Ni N )(4)
The Eq (4) is used and this frequency coefficient is expressed as H in Eq (3). In Eq (4), Ni is the cumulative number of̅ is the average number of occurrences in 47 prefectures in Japan.
disaster occurrences in each prefecture, and 𝑁
For the earthquakes, a further grouping is required. There are two types of earthquake; inter-plate earthquakes andearthquakes located directly above the focus. In GNS2015 and 2017, the data were normalized by different methods foreach type of earthquake. Exposure Sub-goals are determined by 6 Normalized indictors based on 10 different Original
data bases (Abe, 2006; Active faults research group, 1991; Arakawa et al., 1961; Cabinet Office, Government of Japan,
2015a; Geospatial Information Authority of Japan, 2015; Japan Meteorological Agency, 2015; Jiban-net, 2015; Ministryof Internal Affairs and Communications, 2015c; Miyazaki, 1956; Nakata and Imaizumi, 2002; Japan Meteorological Agency
and Volcanological Society of Japan, 2003; National Research Institute for Earth Science and Disaster Prevention, 2015;
National Land Information Division, National Spatial Planning and Regional Policy Bureau, MLIT, 2015; Statistical
Information Institute Consulting and Analysis, 2015; The Headquarters for Earthquake Research Promotion, 2015).
For the inter-plate earthquakes, J-SHIS (Japan Seismic Hazard Information Station) Map prepared by National ResearchInstitute for Earth Science and Disaster Prevention (2015) is utilized. The Map provides the distribution of population(population seismically exposed; PSE) in the areas, of which seismic intensity exceeds a certain value, for a given focus
and a given magnitude of earthquake. In GNS2015 and GNS2017 calculations, equal or over the seismic intensity of 6
caused by the above 13 earthquakes was taken as “Exposure” for the inter-plate earthquakes. For the earthquakes locateddirectly above the focus, extended lengths of active faults are used, and the extended lengths are divided by the total
area of the prefecture, which is equivalent to a density of active faults. Since a clear separation of exposure calculated
due to these two types of earthquake is not straightforward, the average value of the two exposures is used in thecalculation.
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Volume 4, Issue 4 December 2019
For tsunami, “Tsunami Inundation Prediction” map provided by the Ministry of Land, Infrastructure, Transport andTourism (MLIT) is used to evaluate the population in the affected areas. For storm surges (high tide), the area 3 meters
below the sea level is considered as the affected area and population in the affected area is calculated.
For sediment disasters, “sediment disaster hazard points” in the national land numerical information download service
provided by MLIT are used to calculate the population in the affected area.
For volcanic disasters, the data are used in the chronological table of volcanic disaster from the year of 1600 onwards
published by the Meteorological Agency (2003). The exposure of volcanic disasters is multiplying the percentage of people
living in the volcanic areas and the frequency coefficient.
For flood, which is newly introduced, “Inundation Prediction” map provided by MLIT is used to evaluate and the
population in the affected area with over 2 meters in inundation depth is considered. For each natural disaster, theexposure (E) can be calculated accurately by superimposing the above-mentioned affected area by natural disasters andthe population distribution obtained from the portal site of Japanese Government Statistics (so-called e-Stat) provide by
Statistics Bureau of Japan by using GIS. The national census is taken every five years and the population distribution data
are also updated every five years.
2.3 Vulnerability
For vulnerability assessment, available data are categorized into two; hardware measures and software measures as an
ordinary accepted classification. Hardware measures means physical disaster prevention methods such as aseismic
methods of structures and upgrading methods of aged infrastructures to mitigate against natural disasters. Hardware
measures indicators are classified into a group of sub-indicators named a sub-category. Four sub-categories are
selected:(1) house, public facilities, (2) utility lines such as gas, water, sewage network (3) infrastructures (4) information,
telecommunication. In the process of calculation of hardware measures indictors, sub-categories are calculated from 14
different data bases and then sub-categories are multiplied by weighted coefficients, leading to the values of thesesubcategories.
Software measures means measures other than hardware measures, including a social system of conducting frequentdisaster education, stocking food for emergency and preparing manuals at the time of disasters. Four sub-categories are
selected. (1) relief goods, food stock, (2) medical services, (3) economy and population structure, (4) insurance, (5)regulations/autonomy. Similar to that in the calculation process of hardware measures index, sub-indicators are obtained
from a cluster of 22 database, and subcategories are multiplied by weighted coefficients. Finally, both hardware andsoftware measures indicators are obtained by multiplying the weighted coefficient (see Table 1).

12Volume 4, Issue 4 December 2019
Table 1 Source data, normalized indicators, sub-goals, and weighting coefficients for the calculation of GNS

Earthquake

1/6

1.0

Tsunami1/6

1.0

Storm surge

1/6

1.0

Sediment
disaster

1/6

1.0

Volcanodisaster

1/6

1.0

Flood disaster

1/6

Rate of non-fireproof wooden houses [%]Rate of non-fireproof houses
Rate of damaged buildings [%]Rate of damaged buildings
Rate of earthquake resistant main pipelines [%]Rate of earthquake resistance ofRate of earthquake resistant purification plants [%]water supply and drainage
facilities
Rate of earthquake resistant service reservoirs [%]Rate of decrepit pipeline (over 40 years) [%]Percentage of decrepit pipeline
Total length of road [km]Road density index
Repair rate of bridges [%]Repair rate of bridges
Rate of development of broadcast communication
Rate of development of radio
equipment system [%]communication facilities for
Rate of development of portable broadcast
disaster prevention
communication system [%]Rate of development of J-Alert system [%]Rate of development of J-AlertsystemRate of development of J-Alert automatic system [%]Number of stockpiling hardtacks [meals]
Number of stockpiling instant noodles
Amount of stockpiling rice [kg]Stockpiling foods
Number of stockpiling canned staple foods
Number of stockpiling side dishes
Amount of stockpiling water [l]Stockpiling water
Number of stockpiling blankets
Stockpiling blankets
Number of supermarket store
Supermarket store index
Number of convenience store
Convenience store index
Number of Physicians per 100,000
Number of physicians
population
Number of Hospital beds per
Number of hospital beds
100,000 population
Financial capability index
Financial capability index
Gini coefficient
Gini coefficient
Old-age index [%]Old-age index
Rate of persons who received
Rate of persons who received public aid [%]public aid
RateofparticipationinRate of participation in earthquake insurance [%]earthquake insurance
Number of dangerous sites subject to sediment disaster Rate of specification of sedimentdisaster prone areas
Number of sediment disaster prone areas
Number of municipals publishing hazard maps fortsunami disaster
Number of municipals publishing hazard maps for floods
Rate of publication of hazard maps
disaster
Number of municipals publishing hazard maps for
sediment disaster
Rate of households covered by voluntary disaster Coverage rate for the voluntary
prevention organization [%]disaster prevention organization

1/3
1/3

Buildings

1/4

Lifelines

1/4

-

-

1/2

1/2

1/3

1/2
1/2
1/2
1/2
1/2

Infrastructures

1/4

Information,
networks

1/4

Emergency
stockpile

0.225

Medicalservices

0.225

1/2
GNS

1/5

1/5
1/5
1/5
1/5
1/2
1/2
1/4
1/4
1/4

Economy and0.225
population

1/4
1

Insurance

0.10

Regulations
andgovernance

0.225

1/3

1/3

1/3

Vulnerability

13

Weight
wi

Risk
index

1/2
1/2

Subgoals

Exposure

J-SHIS Map (Disaster affected people)
Inter-plate earthquake
Total length of active faults [km]Epicentral earthquake
Tsunami inundation supposition
Tsunami area population rate of altitude less than 3m Tsunami[%]
Number of storm surge [times]
Storm surge
Population rate of altitude less than 3m [%]Sediment disaster hazard points
Sediment disaster
Sediment disaster area population rate [%]Number of volcanic disasters
Volcano disaster
Population rate in volcanic area [%]flood assumption area
Flood disasterflood area population rate of altitude less than 2m [%]Rate of earthquake resistant private building [%]Rate of earthquake resistance ofbuildings
Rate of earthquake resistant public building [%]

Risk
compo
nents

Weightwi

SoftwareSub-goals

Normalized indicators

Hardware

WeightWi

Original dataVolume 4, Issue 4 December 2019

Earthquake

TsunamiStorm surge

Sediment disaster

Total Exposure

Volcano disaster

Flood
Fig. 4. Exposure

3. Natural disaster risks at East Japan area evaluated by GNS
3.1 Exposure
Fig. 4 shows the exposures of natural disasters in municipal scale in East Japan. The exposures of tsunami and storm surge
are larger in the coast coastal areas of Chiba, Tokyo, Kanagawa, Shizuoka and Miyagi prefectures. The exposure of
sediment disaster is larger near in mountainous areas of Kanagawa, Shizuoka, Nagano, and Niigata prefectures. As for14Volume 4, Issue 4 December 2019flood, there are some municipalities with large flood exposure along first-class rivers in Saitama, Tokyo, Shizuoka, Nagano,Miyagi, and Hokkaido prefectures. The final summary of exposures is shown below. The three prefectures of Gunma,Tochigi, and Ibaraki have low exposures, and the exposures in coastal areas such as Tokyo, Chiba, Kanagawa, and Shizuoka
tend to be high.
3.2 Vulnerability
Fig. 5 shows the vulnerability obtained from the sum of hardware and software measures. Both vulnerabilities of
hardware and software are not different in the prefectural scale but some differences are seen depending onmunicipalities, which are expressed with mosaic patterns.

Vulnerability related to hardware
measures

Vulnerability related to software
measuresTotal vulnerability
Fig. 5. Vulnerability

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Volume 4, Issue 4 December 2019

Exposure

GNS

Vulnerability

Fig. 6. GNS
3.3 GNS
Fig. 6 shows exposure, vulnerability, and GNS. The trend of GNS shows a tendency similar to that of exposure, whichmeans that exposure has a great influence on GNS. GNS can indicate natural disaster hotspots in municipal scale.
4. Conclusions
In this study, the new calculation methodology using GIS (Geographic Information System) was introduced to evaluate
exposure of natural disasters more precisely in municipal scale. The newly extended GNS was applied to evaluate natural
disaster risks of each municipality in East Japan. Additionally, the exposure of flood disaster can be evaluated bysuperposing damage estimation maps and population distribution using GIS.
5. Acknowledgements
The authors are grateful to Kosuke Mukai of Nippon Koei (former Kansai University), Hideyuki Fukushima of Shizuoka
Prefecture (former Tokyo City University), Sawa Kajitani of Kansai University and Reo Toma of Tokyo City University forassistance with the GNS calculation. The research was supported by JSPS Department Research Project JP16H03156,“Establishment of vulnerability assessment for Gross National Safety for Natural Disasters (GNS) construction andreflection in disaster prevention strategy” (Representative: Kazuya ITOH).
6. ReferenceAbe, K. (2006) Size of tsunamis around Japan for 1498-2006, wwweic.eri.u-tokyo.ac.jp/tsunamiMt.html (accessed
2015-12-09).
Active Faults Research Group (1991) Active faults in Japan, University of Tokyo Press, p. 437.
Arakawa, T., Ito, Y. and Ishida, C. (1961) The History of storm surge in Japan, Meteorological Research Institute.
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Volume 4, Issue 4 December 2019
Cabinet Office, Government of Japan (2015a) White Paper on Disaster Management (Damage costs), www.bousai.go.jp/
kaigirep/hakusho/index.html (accessed 2015-12-09).
Geospatial
Information
AuthorityofJapan(2015)
Active
Fault
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Area,
www.gsi.go.jp/bousaichiri/active_fault.html (accessed 2015-12-09).
Japan Meteorological Agency (2015) Summary of Tables explaining the JMA Seismic Intensity Scale,www.jma.go.jp/jma/en/Activities/intsummary.pdf (accessed 2015-12-09).
Jiban-net (2015) Jiban Anshin Map (Disaster prevention map) www.jibanmap.jp/map/main.php (accessed 2015-12-09).
Ministry of Internal Affairs and Communications (2015c) The earth and sand disaster prevention,www.soumu.go.jp/menu_news/ s-news/000066869.html (accessed 2015-12-09).
Miyazaki, M. (1956) Japan's coast menaced by storm surge damage, Proc. 3rd conference on coastal engineering, pp. 18 (in Japanese).
Mukai, Y., Koyama, T., Itoh, K., and Kikumoto, M (2018) Examination of natural disaster exposure between municipalities
in Osaka prefecture by extended GNS using geospatial information, The 73rd Annual conference of Japan Society of Civil
Engineers, pp. 279-280.
Nakata,T. and Imaizumi, T. (2002) Detail Active Faults of Japan, University of Tokyo Press, p. 59, DVD.
O. Kusakabe, M. Kikumoto, K. Shimono, K. Itoh, H. Inagaki, S. Ohsato, K. Watanabe：Development of Gross National Safety
Index for Natural Disasters，Geotechnical Engineering Journal of the SEAGS & AGSSEA，Vol. 48，No. 1，90-101，
2017/04．
Japan Meteorological Agency and Volcanological Society of Japan (2003) National Catalogue of the Active Volcanoes
Japan, www.data.jma.go.jp/svd/vois/data/tokyo/STOCK/souran_eng/menu.htm (accessed 2015-12-09).
National Research Institute for Earth Science and Disaster Prevention (2015) J-SHIS, Japan Seismic Hazard Information
Station, www.j-shis.bosai.go.jp/en/ (accessed 2015-12-09).
National Land Information Division, National Spatial Planning and Regional Policy Bureau, Ministry of Land, Infrastructure,
Transport and Tourism (2015) National Land Numerical Information download service, nlftp.mlit.go.jp/ksj-e/index.html(accessed 2015-12-09).
Roger Pulvers (2012) Japans’s disasters must prompt a radical rethink of citizens’ quality of life, The Japan Times,
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The Headquarters for Earthquake Research Promotion (2015) www.jishin.go.jp/main/index-e.html (accessed 2015-12-09).
◆ A brief CV of Dr. Kazuya Itoh
Kazuya Itoh graduated from Musashi Institute of Technology with Bachelor and Master degrees in thefield of Civil Engineering in 1998 and 2000, respectively. He obtained his Ph. D degree from Tokyo
Institute of Technology in the field of Civil Engineering in 2003. After his Ph. D, he worked in National
Institute of Occupational Safety and Health, Japan (JNIOSH) then move to Tokyo City University from
April, 2015. He is currently Associate Professor in Tokyo City University. His research field is physicalmodelling in geotechnical engineering, and he involves many civil engineering projects such as
prevention and mitigation of geo-disasters, labour accident caused by geotechnical field etc.
◆ A brief CV of Dr. Mamoru Kikumoto
Mamoru Kikumoto graduated from Kyoto University with Bachelor's, Master's, and Ph. D degrees in
the field of Civil Engineering in 2000, 2002, and 2005, respectively. Afterward, he worked for three
years as a JSPS postdoctoral fellow and for four years as an Assistant Professor at Nagoya Institute ofTechnology. Then, he moved to his current institution, Yokohama National University, to be an
Associate Professor. His main research field is the constitutive modeling of geomaterials.
◆ A brief CV of Dr. Tomofumi Koyama
Tomofumi Koyama graduated from Kyoto University with Bachelor and Master degrees in the field ofCivil Engineering in 1999 and 2001, respectively. He also obtained his Ph. D degree from Royal Instituteof Technology, Sweden (KTH) in the field of land and water resources sciences in 2007. After obtaining
his Ph. D, he worked in Kyoto University as Assistant Professor until the end of March, 2014 and move
to Kansai University from April, 2014. He is currently Associate Professor in Kansai University. His
research field is rock mechanics and he involves many civil engineering projects such as prevention
and mitigation of geo-disasters, geological disposal of high-level nuclear waste, maintenance of aged
infrastructures etc.
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