Ranking
Ranking Name of Prefecture GNS

16

High Risk

Low Risk
1

8

Rank

Prefecture

GNS

Exposure

Vulnerability

1Tokushima

9.2

23.7

38.9

15

22

26

33

40

47

Hardware measures Software measures
34.9

43.0

2

A i c h i

8.6

21.6

39.7

37.4

42.0

3

O s a k a

8.5

21.8

39.2

40.6

37.8

4

Niigata

8.4

20.4

41.4

38.2

44.6

5

T o k y o

8.1

21.9

37.2

34.9

39.4

6

M

e

7.9

20.0

39.6

33.6

45.7

7

C h i b a

i

7.8

18.2

42.7

35.9

49.6

8

Saitama

7.4

17.6

42.1

36.7

47.5

9

Kanagawa

7.4

20.2

36.4

34.6

38.2

10

Wakayama

7.2

18.1

39.7

34.5

44.9

11

Shizuoka

7.1

17.9

39.6

35.5

43.7

12

E h i m e

6.9

17.2

39.8

35.7

43.9

13

Kagawa

6.7

16.6

40.2

39.5

41.0

14

K o c h i

6.2

15.0

41.1

37.5

44.7

15

Yamanashi

5.6

16.8

33.4

27.7

39.2

16

Yamagata

5.5

13.2

41.4

39.5

43.3

17

O h i t a

5.1

13.1

39.3

37.5

41.0

18

Nagasaki

5.0

12.0

42.0

35.0

49.0

19

G

4.8

13.0

36.6

31.0

42.3

i

f

u

20

M i y a g i

4.7

11.4

41.2

39.5

43.0

21

A k i t a

4.3

10.4

41.5

36.0

47.0

22

Ibaraki

4.3

10.2

42.1

38.6

45.5

23

Nagano

4.3

11.2

38.1

36.5

39.7

24

K y o t o

4.2

10.1

41.3

43.6

39.1

25

S h i g a

4.0

10.0

39.6

38.3

40.9

26

H y o g o

4.0

10.2

39.1

40.8

37.5

27

Kumamoto

3.9

9.7

40.5

37.8

43.1

28

Toyama

3.9

10.9

35.9

33.5

38.4

29

F u k u i

3.8

10.7

35.9

33.7

38.0

30

Fukushima

3.8

8.7

44.0

40.0

48.0

31

N

a

3.7

9.2

40.3

37.2

43.5

32

Okinawa

a

r

3.7

7.9

46.8

42.5

51.1

33

Yamaguchi

3.6

9.0

39.7

42.4

37.1

34

Okayama

3.6

8.6

41.5

40.8

42.3

35

I w a t e

3.4

8.0

42.3

35.1

49.5

36

Aomori

3.3

8.4

39.3

32.8

45.8

37

Fukuoka

3.0

7.3

41.2

41.6

40.9

38

Ishikawa

2.9

7.6

38.1

36.9

39.3

39

Kagoshima

2.5

6.3

39.5

39.4

39.7

40

Hokkaido

2.4

5.3

45.2

43.5

47.0

41

Shimane

2.3

6.2

38.1

34.9

41.4

42

G u n m a

2.1

5.3

40.1

38.1

42.2

43

Hiroshima

2.1

5.1

41.3

40.8

41.8

44

S

1.4

3.4

41.3

38.0

44.7

a

g

a

45

Miyazaki

1.2

3.2

39.3

34.3

44.2

46

Tochigi

0.9

2.2

41.6

43.0

40.2

47

Tottori

0.8

2.2

35.0

29.5

40.6

GNS

Gross National Safety
for natural disasters［ Version 2015 ］

Kanto branch of Japanese Geotechnical Society (JGS-Kanto)
Technical committee for Geotechnical risk and social system such as law and lawsuit

1

What is GNS?
GNS is an abbreviation of Gross National Safety for natural disasters and an index, expressing
quantitative risks for natural disasters. The key aim of GNS is to provide a tool for decision and policy

makers, responsible for budget plan for preventing and mitigating natural disasters. Mitigation of natural
disasters is closely related to the UN Millennium Development Goals, in particular, (1) to eradicate
extreme poverty and hunger, and (7) to ensure environmental sustainability. International community has
been working towards creating an index of disaster risk and vulnerability, in accordance with the Hyogo
framework for action 2005-2015, both from the aspects of natural and social sciences.
Japan is prone to natural disasters. There is a fundamental need for creating a kind of safety index
against potential natural disasters to guide us to transform our land into truly safe land. The index

2

must indicate how effective current hard and soft measures are to resist against potential natural disasters,
and what measures are inadequate. Immediately after Great East Japan Earthquake on March 11, 2011,

O.Kusakabe advocated an urgent need to create such an index of nation-wide safety index, together with
Gross Domestic Product (GDP) and Gross National Happiness (GNH), to steadily transform Japan to safe
land. He presented an initial concept of GNS,
This booklet presents the proposed method how to calculate 2015 version GNS, and the calculated resultsof GNS in the prefecture scale. The authors of this booklet hope that GNS can offer a scientifically soundindex to assist the decision and policy makers to allocate proper and effective investment for prevention
and mitigation of natural disasters, by showing how each prefecture annually progresses to upgrade thehard and soft measures against natural disasters to a desirable level. To do so, the method and the range ofapplication of GNS will be continuously improved and data available will be continuously updated.

Damage by the 2007 Niigata-Chuetsu Oki earthquake at Ohmigawa Station in Kashiwazaki, Niigata Prefecture | Photographed by Kazuya ITOH
(2007.7.23)

3

4

Damage by the 2011 The Great East Japan Earthquake at Shirakawa, Fukushima Prefecture | Photographed by Kazuya ITOH (2011.5.6)

Gross National Safety for natural disasters

GNS Index 2015
Proposed method how to calculate 2015 version GNS, and thecalculation results of GNS index in the prefecture scale, Here after.

5How to calculate GNS

Fundamental concept of 2015 version GNS
Risk indices for natural disasters presently
widely used adopt the following form offunction.

wh ereR = f ( H, E, V, Re ) 
R:r i s k
H : h a z a rd
E : e x p o s u re
V: v u l n e r a b i l i t y
Re : re s i l i e n c e .

parameters is 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 ( l i m V ) , R
becomes null.

(1)

In the course of development of 2015 version
GNS, the following points are taken into
consideration in such a way that the decisionand policy maker responsible for budge plancan easily access.

In here, H x E means“exposure”in a broad
sense, which is determined by population
distribution, geology and topography in aparticular region. V x Re is a value expressing
the relationship between society and natural
disasters. In the 2015 version GNS, V = V ( V,Re) is used.
Thus the equation (1) yields
R = f ( H,E,V) (2)
The equation (2) is a form of function adopted
in the GNS calculation. One of the simplest
forms of the equation (1) may be
R = H× E× V (3)The equation (3) is the actually used equation
for the 2015 version GNS calculation.

1. Data to be used should be free access forthe purpose of continuous updating.
2. Data to be used should be available at theprefectural level to compare one prefecture
to another.
3. Prioritizing the items affecting for improving
natural disaster measures and the itemswith higher propriety should be selected.
4. The values of hazard, exposure andvulnerability should be hierarchicallycalculated by weighted linear summation asshown in Figure 1.

One of the features of the equation (3) isthat R becomes null when one of the three

Three layered hierarchy system is used. The

Figure 1（Hazard ×Exposure ）× ΣVulnerability
Risk = ΣPhysical event
causing hazard

Social and economic
vulnerability
against natural disasters

Population of aﬀectedarea caused by hazards

1) Earthquake, 2) Tsunami, 3) Storm surge,
4) Sediment disaster, 5) Volcano

Exposure andvulnerability are
hierarchicallycalculated
by weighted
linear summation

61) Hardware measures2) Software measures

Normalized
indicators

C1 W1
C2subW2sub
C3subW3sub
C4subW4sub
C i sub W isub
sub

sub

Sub-goals

C1ctg W1ctg
C2ctg W2ctg
C ictg Wictg

Framework of GNS in 2015 version

Souse data of vulnerability Table 1

Hardware
SoftwareVulnerability

Subgoals

Normalized indicator

Buildings

Rate of earthquake resistance of buildings, Rate of non-fireproof houses, Rate of damaged buildings

Lifelines

Rate of earthquake resistance of water supply and drainage facilities, Percentage of decrepit pipelines

Infrastructures

Road density index, Repair rate of bridges

Information,
networks
Emergency
stockpile

Rate of development of radio communication facilities for disaster prevention, Rate of development ofJ-alert system
Stockpiling foods, Stockpiling water, Stockpiling blankets, Supermarket store index, Convenience store
index

Medical services

Number of physiciams per 100,000 population, Number of hospital beds per 100,000 population

Economy andpopulation

Financial capability index, Gini coefficient, Old-age index, Rate of persons who received public aid

Insurance
Regulations andgovernance

Rate of participation in Earthquake Insurance
Rate of specification of sediment disaster prone areas, Rate of publication of hazard maps, Coverage rate
for the voluntary organization for disaster prevention

Source data of exposure Table 2
Risk components
Exposure

Normalized indicator
Earthquake (Interplate, Epicentral), Tsunami, Storm surge, Sediment disaster, Volcano

highest layer composes of hazard x exposureand vulnerability. The two components
named indicator are calculated by summing up
weighted corresponding sub-indicators. These
weighted sub-indicators are obtained by thesummation of the corresponding sub-categories.
These sub-categories are determined by acluster of free access data base, which arelocated at the lowest level in the hierarchy.

Framework of GNS in 2015 version

We have decided to adopt the simplest
framework of GNS by the multiplying“vulnerability”and“exposure”for decisionand policy makers of mitigation as a simple
yet clear indicator. In the “vulnerability”
calculation, available data are categorized
into two; hardware measures and software
measures as an ordinary acceptedclassification.

Vulnerability

The vulnerability indictor is a summationof weighted hard measures indicators andweighted soft measures indictors. The
weighted coefficient ( w ) is selected as
wi = 0 . 5 . Table1 lists sub-indictors with thecorresponding sub-categories.

Hardware measures

Hardware measures means physical disasterprevention methods such as aseismic methodsof structures, and upgrading methods of aged
infrastructures to mitigate against natural
disasters. Hardware measures indictors
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 arecalculated from 14 different data bases andthen sub-categories are multiplied by weighted
coefficients, leading to the values of these subcategories. Finally the hardware measuresindicators are obtained by multiplying theweighted coefficient of wi = 0 . 2 5 .

Software measures

Software measures means a measures otherthan hardware measures, including a social

7

Framework of GNS in 2015 version

system of conducting frequent disastereducation, stocking food for emergency andpreparing manuals at the time of disasters.
Four sub-categories are selected. (1) relief
goods, food stock, (2) medical services, (3)economy& population structure, (4) insurance,(5) regulations/autonomy.

（2）Tsunami

Similar to that in the calculation process ofhardware measures index, sub-indicators are
obtained from a cluster of 22 database, and subcategories are multiplied by weighted coefficients.
Except the weighted coefficient for insurance,
wi =0 .1 . the same value of wi =0 .2 5 is used.

（3）High tide (Storm surge)

Exposure（1）Earthquake

There are two types of earthquake; trench
type earthquakes and earthquakes locateddirectly above the focus. In the 2015 version
GNS, the data are normalized by different
methods for each type of earthquake.

Frequency coefficient is evaluated by using thedata of the number of occurrence of high tide
up to the year of 1961. Exposure is calculated
by multiplying the number of people living
less than 3m above the sea level and thefrequency coefficient. The difference between
the exposure due to Tsunami and theexposure due to high tide is only the locationand the frequency of occurrence.

（4）Sediment disasters

For the trench type earthquakes, the Maps
indicating the population in the affectedareas in J-SHIS Map prepared by National
Research Institute for Earth Science andDisaster Prevention are used. The Map
provides the distribution of population(population seismically exposed. PSE) in theareas, of which seismic intensity exceeds
a certain value, for a given focus and agiven magnitude of earthquake. In the 2015
version GNS calculations, equal or over theseismic intensity of 6 caused by the above
13 earthquake is taken as "Exposure" for the
trench type earthquakes.

Exposure due to sediment disasters is
calculated by the number of people living
in a potential affected zone. There are twocategories in the potential affected zone;
debris flow and steep slopes, in terms of thenumber of people. In Category I, there are
more than five houses, and in Category II there
are one to four houses. We assume that there
are, in an average, ten houses in Category
I, while there are two and a half houses in
Category II. In land slide area, we also assume
that there are houses. By summing up all
the number of houses is divided by the total
number of people in the prefecture, Exposure is
obtained. The frequency coefficient is definedby a ratio of the number of occurrence per
year to the number of locations. Exposure
is calculated by multiplying the frequency
coefficient and exposure.

Earthquakes located directly above the focus（5）Volcanic disasters

Because clear separation of exposurescalculated due to these two types ofearthquake is not easy, the average value ofthe two exposures is used in the calculation.

In the 2015 version GNS, weighted values for all
five indicators are taken as the same value of 0.20.

Trench type earthquakes

For the earthquakes located directly above
the focus, extended lengths of active faults are
selected. The extended lengths are divided
by the total area of the prefecture, which isequivalent to a density of active faults.

8Frequency coefficient is evaluated by using thedata of the number of occurrence of tsunami
during the period of 1498 to 2006. Exposure
is calculated by multiplying the number ofpeople living less than 3m above the sea level
and the frequency coefficient.

Frequency ratio is obtained from the data
book of volcanic activities since 1600
complied by Japan Meteorological Agency.
Exposure for volcanic disaster exposure ismultiplying the number of people living the
volcanic areas and the frequency ratio.

Calculated Results of GNS in 2015 version

Main Indicator

Vulnerability indicator×

Software measuresindicator

The vulnerability indicator is a summation of weighted hardware
measures indicators and weighted software measures indicators. The hardware measures indicators were
small in Yamanashi, Tottori and Gifu and large in Kyoto, Hokkaido and Tochigi prefectures. While thesoftware measures indicators were small in Yamaguchi, Hyogo and Osaka and large in Okinawa, Chiba and
Iwate prefectures. As a result, the vulnerability indicators were small in Yamanashi, Tottori, Toyama andFukui and large in Okinawa, Hokkaido andFukushima prefectures.
Multiplying“Vulnerability”

and“Exposure”

a GNS［ 2015］
Culculation Results of GNS

Vulnerability+

Hardware measuresindicatora

Earthquake
TsunamiHigh tide

Exposure

Sediment
disasters
Volcanic
disasters

Exposure indicator

The exposure indicators were large in Tokushima, Tokyo and Osaka andsmall in Miyazaki, Tochigi and Tottori prefectures.

9Gross Natural Safety for natural disasters. GNS Index 2015

［ 2015］
Culculation Results of GNS

aFeatures of G N S ［2015］

GNS is obtained by multiplying values of vulnerability and the value of exposure. The tablebelow lists the values of the best and worst four prefectures. It is noticed that GNS is largely
affected by the value of exposure. Suppose that the risks for natural disasters are substantiallygoverned by the exposure to natural disasters, an option may be feasible to reduce theexposure itself. Based on equation (1), it is impossible to make occurrence of natural disasterto be null, and measures for reducing the vulnerability may not be guarantee risk to be nulland a considerable expenditure is required. In this context, transference of population tosafer locations may become a possible option to reduce the value of GNS. Policy of changing
population distribution step by step, together with upgrading hard and soft measures, may bepossible to reduce potential risks for natural disasters.

10

Best four prefectures
Ranking Name of prefecture GNS

Worst four prefectures
Ranking Name of prefecture GNS

Rank

Prefective

GNS

Rank

Prefective

GNS

1

Tottori

0.8

1Tokushima

9.2

2

Tochigi

0.9

2

A ich i8.6

3

Miyazaki

1.2

3

Osaka

8.5

4

Saga

1.4

4

Niigata

8.4

Effective use of GNS

An example of effective use of GNS
The figure on the right side shows

measures. Doing such visualization ofcorresponding exposure values for variousof mitigation measures, which is a beneficial

insufficient indicators leads to prioritization

vulnerability values plotted against

merit of GNS.

prefectures. Dotted lines indicate the meanvalues. As an example, a close look of thevalues of Kanagawa Prefecture reveals

50that Kanagawa Prefecture has a smaller

V ave=11.8%
Okinawa（32）

vulnerability value and a larger exposure

value. Although Kanagawa prefecture has aactually have issues to be considered andimproved when further detailed is examined
figure below shows the values of various

vulnerability indicators relative to thenational average (indicated by a dotted

Chiba（7）、Saitama（8）Nigata（4）V ave=40.0%

40

（％）

in hardware and software measures. The、 V
Vulnerability

smaller value of vulnerability, the prefecture

Hokkaido（40）45Aichi（2）、Osaka（3）Tokyo（5）Kanagawa（9）
Toyama（28）、Fukui（29）35

circle) with respect to the hard and soft
measures, respectively. From the figures, it
is clear that road density in the prefecture

is insufficient for the hard measures, while

the numbers of medical doctors and beds,

areas covered by independent anti-disasterorganizations, are inadequate for the soft

Yamanashi（15）
30

0

5

10

15

20

25

Exposure, E （％）

Vulnerability value plotted against
corresponding exposure values forvarious prefectures

Example : The vulnerability indicators of Kanagawa prefecture

Rate of development ofJ-alert system

Average
Rate of earthquake
resistance of building

Rate of development ofradio communication
facilities for disasterprevention

Rate ofnon-fireproof
house
Rate ofdamagedbuilding

Repair rateof bridges

Road density
index

Percentage ofaged pipelines

Rate of earthquake
resistance of watersupply and drainage
facilities

Hardware measures

Rate ofpublication of
hazard maps

Coveragerate for thevoluntaryorganization
for disasterprevention

Emergency
stockpile

Average
Number of medical doctors
per 100,000 population

Number of
hospital beds
per 100,000
population

Rate ofspecificationof sedimentdisasterprone areas
Insurance
Population

EconomyIndex

Software measures

1112 Building Damaged by eruption of Mt. Usu at Toya caldera, Hokkaido Prefecture | Photographed by Hideki INAGAKI (2011.5)

Gross National Safety for natural disasters

GNS Index 2015
List of data used in GNS2015

13

List of Data used in GNS

1 Exposure related data base1 Web site

The data of population are released together with the indicatorstatistics about vulnerability in many cases. Then this list
includes it together with vulnerability indicator.

National Research Institute for Earth Science and Disaster Prevention (2015) J-SHIS, Japan Seismic Hazard Information

1 Station, http://www.j-shis.bosai.go.jp/en/

Japan Meteorological Agency (2015) Summary of Tables explaining the JMA Seismic Intensity Scale,2 http://www.jma.go.jp/jma/en/Activities/intsummary.pdf

Geospatial Information Authority of Japan (2015) Active Fault Map in Urban Area,

3 http://www.gsi.go.jp/bousaichiri/active_fault.html

4 The Headquarters for Earthquake Research Promotion (2015) http://www.jishin.go.jp/main/index-e.html
5 Jiban-net (2015) Jiban Anshin Map (Disaster prevention map) http://www.jibanmap.jp/map/main.php
6 Abe, K. (2006) Size of tsunamis around Japan for 1498-2006, http://www.eic.eri.u-tokyo.ac.jp/tsunamiMt.html
Japan Meteorological Agency and Volcanological Society of Japan (2003) National Catalogue of the Active Volcanoes

7 Japan, http://www.data.jma.go.jp/svd/vois/data/tokyo/STOCK/souran_eng/menu.htm

National Land Information Division, National Spatial Planning and Regional Policy Bureau, Ministry of Land, Infrastructure,

8 Transport and Tourism (2015) National Land Numerical Information download service, http://nlftp.mlit.go.jp/ksj-e/index.html

2 Publications
1 Active Faults Research Group (1991) Active faults in Japan, University of Tokyo Press, p. 437.
2 Nakata,T. and Imaizumi, T. (2002) Detail Active Faults of Japan, University of Tokyo Press, p. 59, DVD.
3 Arakawa, T., Ito, Y. and Ishida, C. (1961) The History of storm surge in Japan, Meteorological Research Institute.
Miyazaki, M. (1956) Japan's coast menaced by storm surge damage, Proc. 3 Conference on Coastal Engineering, pp. 1-8 (in4 Japanese).

2 Vulnerability/ susceptibility/ coping capabilities/ adaptive
capabilities related data base1 Web site

Cabinet Office, Government of Japan (2015) White Paper on Disaster Management (Damage costs),

1 http://www.bousai.go.jp/kaigirep/hakusho/index.html

Ministry of Land, Infrastructure, Transport and Tourism (2015) Rate of seismic upgrades to buildings,

2 http://www.mlit.go.jp/common/000133730.pdf

3 Statistics Bureau (2015) National Survey of Family Income and Expenditure, http://www.stat.go.jp/english/data/zensho/index.htm
Ministry of Health, Labour and Welfare (2015) National Survey on Public Assistance Recipients,

4 http://www.e-stat.go.jp/SG1/estat/eStatTopPortalE.do

Cabinet Office, Government of Japan (2015) SNA (National Accounts of Japan),

5 http://www.esri.cao.go.jp/en/sna/menu.html

6 Statistics Bureau (2015) Population Estimates, http://www.e-stat.go.jp/SG1/estat/eStatTopPortalE.do
Japan Water Research Center (2012) WRC Hot News No342 (Rate of seismic upgrades to water system,）

7 http://www.jwrc-net.or.jp/hotnews/pdf/HotNews342.pdf

Japan Water Research Center (2012b) WRC Hot News No330-2 (Old tube to more than 40 years),

8 http://www.jwrc-net.or.jp/hotnews/pdf/HotNews330-2.pdf

Ministry of Land, Infrastructure, Transport and Tourism (2015) Road Statistics (Maintenance and Repair),

9 http://www.mlit.go.jp/road/sisaku/yobohozen/yobo5_3.pdf

Fire and Disaster Management Agency (2015) White Paper on Fire Disaster,

10 http://www.fdma.go.jp/concern/publication/index_2.html

11 Fire and Disaster Management Agency (2015) Prevent disasters in regional areas, http://www.fdma.go.jp/disaster/chihoubousai/
12 New Supermarket Association of Japan (2015) Supermarkets number of statistics, http://www.super.or.jp/?page_id=2646.
13 Nippon Telegraph and Telephone Corporation (2015) i Town Page, http://itp.ne.jp/?rf=1

14

Ministry of Health, Labour and Welfare (2015b) Survey of Physicians, Dentists and Pharmacists,

14 http://www.e-stat.go.jp/SG1/estat/eStatTopPortalE.do

Ministry of Internal Affairs and Communications (2015) Local Public Finance,

15 http://www.soumu.go.jp/iken/ruiji/(accessed 2015-12-09).

General Insurance Rating Organization of Japan (2014) Earthquake Insurance in Japan,

16 http://www.giroj.or.jp/english/pdf/Earthquake.pdf

Ministry of Internal Affairs and Communications (2015) The earth and sand disaster prevention,

17 http://www.soumu.go.jp/menu_news/s-news/000066869.html

18 Ministry of Land, Infrastructure, Transport and Tourism (2015) Hazard map portal site, http://disaportal.gsi.go.jp/
Ministry of Health, Labour and Welfare (2015) Securement and cultivation of medical professionals,

19 http://www.mhlw.go.jp/jigyo_shiwake/gyousei_review_sheet/2015/h26_1-2-1_saisyu.html
Ministry of Internal Affairs and Communications (2015) Local allocation tax,

20 http://www.soumu.go.jp/main_sosiki/c-zaisei/kouhu.html

2 Publications
1 Ministry of Internal Affairs and Communications (2008) Housing and Land Survey 2008,
Statistical Information Institute Consulting and Analysis (2015) Demographic statistics according to the topography and2 analysis

Gross National Safety for natural disasters

GNS［Version 2015］

Financial supports provided by Japan Society for the Promotion of Science（Grants-in-Aid
Science Research JP25560184 and JP16H03156）are acknowledged.
This booklet is written by
Osamu Kusakabe
Mamoru Kikumoto
Kanchi Shimono
Kazuya Itoh
Hideki Inagaki
Shigeto Ohsato
Koji Watanabe［Supported by］
Kanto branch of Japan Geotechnical Society (JGS-Kanto)
Technical committee for Geotechnical risk and social system such as law and lawsuit
GNS Working group［Cover Photo］
Damage by the 2011 The Great East Japan Earthquake at iwaki, Ibaragi Prefecture |Photographed by Kazuya ITOH (2011.5.7)［The editors］ Osamu Kusakabe, and Kazuya ITOH
［The date of publication］ 21th March, 2015 (in Japanese)／22nd, February, 2017 (in English)［Printed by］ Atomi Co., Ltd［Designed by］ Ayano Hirano (Atomi Co.,Ltd)

15