Journal of Natural Disaster Science, Volume 31, Number 2, 2009, pp21-32

Evaluating the Impact of Demographic Transitionin the Context of the Tokai-Tonankai-Nankai Earthquake, Japan

Haili CHEN*
Norio MAKI**
Haruo HAYASHI*** Graduate School of Informatics, Kyoto University, Japan**Disaster Prevention Research Institute, Kyoto University, Japan(Received for 20 Apr., 2010 and in revised from 1 Jun., 2010)
ABSTRACTThis study intends to adopt a geographical approach to building a region-type index
associated with recovery, which aims to examine the impact of ongoing demographictransition in the context of the expected the Tokai-Tonankai-Nankai Earthquake. A
great amount of population census data is applied in building the index that comprises
3 demographic transition patterns that represent the various degrees of regional selfsufficiency in a disaster. Applying the index to the population census data of 2005, withthe anticipated seismicity in 4 appointed scenarios via GIS mapping, a place assessment
is presented to examine the impact of a possible population demographic transition orderissued in pre-disaster recovery planning.
Keyword: population decline, aging, self-sufficiency, Tokai-Tonankai-Nankai Earthquake,pre-disaster management

I. Background

Capital Major Metro.
Chubu Major Metro.
Kinki Major Metro.

1. Next catastrophe in a population decline
periodThe Tokai-Tonankai-Nankai Earthquake is regarded as a potential catastrophe in the first half ofthe century (see fig. 1). According to the estimation
result,[１] the cumulative occurrence possibility for the
coming 30 years reaches 60-70%, especially during
the 2030s. The divergent core areas of the 3 metropolitan areas and their surrounding wide rural areas
will be exposed to the disaster at the same time.[２]Chugoku

Tokai

Shikoku

Tonankai
KyushuSeismicity

Nankai

M 4.5- 4.9
M 5.0- 5.4
M 5.5- 5.9
M 6.0- 6.4
M6.5 -

Figure 1. Anticipated seismicity of the Tokai-Tonankai-Nankai Earthquake[１]21H. CHEN, N.MAKI, H.HAYASHI

125

Tokai-TonankaiNankai Earthquake

42
35

Population

120
115

28

Aged 65+

110

Super Aged Society

21

21%

105
Aged Society

100
95
90

Aging Society

as shown in isolated villages in the Niigata Chuetsu
Earthquake 2004. Another issue is general population
aging that might make the analysis result less discriminative. Thus, it is difficult to examine the impact
of demographic transition in an ordinary vulnerability
assessment.
The lessons learned show that sufficient numbersof participants of working age, amply serviced in employment, education, etc, are critical for independentand constant regional recovery.[12], [13], [14] This implies
that, rather than the previously mentioned factors, demographic characteristics can represent place self-sufficiency that determines the continuity of an affected
region in disaster recovery. Most of all, demographic
characteristics provide a way of integrating disasterrecovery with the ongoing demographic transition ofthis aging developed country.
In this research, instead of a numerical indicator
such as population growth rate, aging rate, etc, a classification of demographic transition patterns in a token self-sufficiency index is built to visualize the impact of future demographic transition. Moreover, theself-sufficiency index and seismic intensity scale ofthe entire exposed areas in Japan are examined withthe aim of providing essential information for predisaster recovery planning for the Tokai-TonankaiNankai Earthquake.

Aged 65 and over (%)

Population (million)

130

14

14%

7

7%

0

0%

1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Figure 2. Transition and estimation of populationin Japan (1960-2050), illustrated from
IPPS.[３] Data since 2009 are estimated.
General population growth in Japan turned tominus in 2005, heralding the coming of the population decline period (see fig. 2). The National Instituteof Population and Social Security Research (IPPS)
estimates that the population in 2030 might remain at
90% of the current population and the elderly population of 65+ might go beyond 30%.[３] This reveals an
age structure different from the current situation.
Yet Maki el al. [４] argued that no attempt has
been made to examine population exposure in predisaster planning in the context of demographictransition. Seismicity simulation[５] is not sufficiently
complete to reduce vulnerability of the population inadvance. A general consideration of regional social
characteristics is also suggested in disaster management.[６]II. Methods
2. Demographic aspect in vulnerability
assessment
To better connect hazards and the characteristics of the affected regions, Cutter suggests a spatial
analysis to assess vulnerable places.[７] This implies
the necessity of a geographic approach to integrating
more regional information such as demographic factors.
Generally, population density, population growthrate, and aging rate are critical demographic factors
in vulnerability assessment[８], [９], [10] especially while
examining population exposure considering seismic
intensity and amount of exposure [11]. Population exposure is based on physical calculation of numbers ofcasualties rather than recovery considerations. It may
be appropriate to evaluate densely populated areas or
population growth areas. Yet the calculation mightlead to an opposite conclusion of a safer environment
when it comes to a population decline period, e.g.,

There have been studies that aim to classify
region types concerning the population structure,e.g., [15], but none of them discusses the demographictransition of the concluded types. Therefore, thesetypes are unable to represent a clear description of regional demographic transition. Also, a method of applying the classification standard to every time period
is unavailable. This is because the classification is developed by the correlative difference in the collected
samples from the census data. Another year’s census
data might lead to a completely different classification. Applying classification to different time periodsof data leads to inexplicable results. To cope with thisproblem, we provide an approach to integrating several demographic classification results.
This study has 3 parts. We apply cluster analysis
to the population statistics data of several time periods
to acquire a regional classification for each period.
22

EVALUATING THE IMPACT OF DEMOGRAPHIC TRANSITION IN THE CONTEXT OF THE TOKAI-TONANKAI-NANKAI EARTHQUAKE, JAPANThen, discrimination analysis to integrate the series ofregional classifications is undertaken, and to develop
demographic transition patterns, a self-sufficiency
index is developed. With mapping using the seismic
intensity data of the Tokai-Tonankai-Nankai Earthquake, analysis is conducted using GIS to discuss theimpact of demographic transition. The details of eachpart are given as follows.

3. Application to place evaluation
The regional exposure characteristics and regional self-sufficiency are integrated in GIS in order
to conduct spatial analysis in the affected area of theTokai-Tonankai-Nankai Earthquake.
Regional exposure characteristics are determinedby the exposure scenario numbers and the maximum
seismicity intensity among these appointed scenarios.
And regional self-sufficiency is based on the previous
section. Discrimination analysis is applied in the latest Population Census 2005 to obtain the current situation. To reach a more accurate assessment, the above
analysis is conducted on a 1-km2 scale.

1. Conducting regional classification
It is noted that detailed aging population proportion can provide more complete family structure
information in the regions. [16] Cluster analysis is applied to a great amount of 1-km2 mesh-based population data to acquire a regional classification.
Eighteen levels of 5-year population proportions(aged 0~4 to 80~84 and aged 85+) and the variablesof each mesh are collected from the population census
data. Repeating the same application to various population census time periods, a regional classification at
every particular time period is obtained.

III. Building a classification of demographictransition patterns
Study area
Osaka Prefecture and Wakayama Prefecture are
selected as study areas due to the variety of regional
development, from the center of Osaka City to the
mid-mountainous area of the Kii Peninsula. As themost aging metropolitan area, Osaka’s population
increase has been slowing down since 1990. Meanwhile, population decline started to accelerate in Wakayama, one of the worst prefectures in 2005. Thus,
the study area is representative of a near-future demographic transition. Another reason is that the study
area is exposed to both the Nankai Earthquake and theTokai-Tonankai Earthquake (see fig. 3).

2. Developing demographic transition
patterns
Discrimination analysis is conducted to adjust
several sets of regional classifications and to integrate
the adjusted sets into a demographic transition pattern.
With the dendrogram graph of cluster analysis results,
we can render clustered meshes in GIS. Based on thespatial distribution of regional classification, we canconclude which common regional types consistently
exist. The meshes that remain stably classified at every time period are collected as “sample meshes” andare used as samples for discrimination analysis.
For one regional type, the meshes are ruled inmesh code and equally divided into the “experimentalgroup” and the “control group.” The experimentalgroup is made to be the discriminant. And the control
group is used to test the reliability of the discriminant
using a true positive rate test. And the discriminantwith the best performance is applied to the study area
to adjust the regional classification. Furthermore,transformation of the demographic transition patterns
is discussed regarding self-sufficiency by a reference
study.
Four methods of discrimination are applied here.
The details and results of each are described in Chapter 3.

Osaka

Osaka

Wakayama

Tonankai

Wakayama

Seismicity

Nankai

M 4.5- 4.9
M 5.0- 5.4
M 5.5- 5.9
M 6.0- 6.4
M6.5 -

Tonankai
Seismicity

Nankai

M 4.5- 4.9
M 5.0- 5.4
M 5.5- 5.9
M 6.0- 6.4
M6.5 -

Figure 3. Osaka and Wakayama, both exposed in the
scenario of the Nankai Earthquake (L) andthe Tokai-Tonakai Earthquake (R)23

H. CHEN, N.MAKI, H.HAYASHI

way, while analyzing the difference among 4 maps in
fig. 5, it is difficult to identify the difference resulting from the cluster analysis or the actual transitionof the meshes. To deal with the first reason, “sample
meshes” with the same classified result from 1990 to2005 in fig. 5 are collected (N=1,223).
For each regional type, all of the selected meshes
are individually separated by a ruled mesh code into
the experimental group (N=612) and the control group(N=611). In this study, the following 4 methods, linear discrimination, multi-logistic regression, the nearest neighborhood method, and the weighted nearest
neighborhood method, are applied to the experimentalgroups to develop an effective discriminant.
Linear discrimination: Eighteen classes of 5-year
population proportions Pm= {P1, P2…P18} and regional
type Q= {A, B, C} of meshes are collected to build 3
sets of linear discrimination coefficients XQm= {XQ1,
XQ2…XQ18} and constantof function FQ (see eq. 1).
The highest score of FQ determines Q of the discriminated mesh.

TABLE I. COLLECTED MESH NUMBERS
CensusCollected
Meshes*Selected
Meshes (N)

1990

1995

2000

2005

3,521 3,450 3,495 3,862
3,117 3,280 3,118 3,031* For privacy protection, a few low-inhabited meshes are
only collected in total numbers. As for the gendered 5-year
aged populations, these meshes are combined into others.

(Eq. 1)
Multi-logistic regression: While regional type A
is a reference category, Pm= {P1, P2…P18} and regional type Q of meshes are applied for building 2 sets ofcoefficients YQm= {YQ1, YQ2…YQ18} and constantofnonlinear function RQ (see eq. 2). RQ shows the probability, while regional type Q is discriminated as B or
C. And eq. 3 shows the probability if regional type Q
is discriminated as A, the appointed type at first. The
highest RQ leads to the result.

Figure 4. Cluster analysis result (1990, 1995, 2000,
and 2005)
Regional classification
Following the previous demographic transition of Osaka and Wakayama Prefectures, the meshbased Population Censuses of 1990, 1995, 2000, and2005 are collected (see table 1). For each period of
population census data, cluster analysis using Ward’s
method is applied to mesh 5-year population proportions. Based on the 4 dendrogram graphs (see fig. 4),
3 common regional types, A, B, and C, are found (seefig. 5).

1990

1995

Type A
Type B
Type C

2000

Type A
Type B
Type C(Eq. 2)(Eq. 3)
Nearest neighborhood method: In the experimental group, the gendered 18 classes of 5-year mesh
population proportions Pn={P1, P2…P36} are collectedby type Q to calculate the average PQn={PQ1, PQ2…
PQ36}. Collective distance DQ represents the similarity
between the population pyramid of regional type Q
and the pyramid of the discriminated mesh (see eq. 4).
The smallest DQ determines the result.

2005

Type A
Type B
Type CType A
Type B
Type C

Figure 5. Distribution of region-type meshes fromthe cluster analysis result
Demographic transition patterns
As 2.2 described, cluster analysis is based on arelative difference among classified samples. In this(Eq. 4)24

EVALUATING THE IMPACT OF DEMOGRAPHIC TRANSITION IN THE CONTEXT OF THE TOKAI-TONANKAI-NANKAI EARTHQUAKE, JAPAN

Weighted nearest neighborhood method: M n
is mid-age number of Pn. It aims to give different
weights while discriminating places with large numbers of juveniles or elderly people. Equation 5 shows
the calculation of weighted distance D’q.

Table 2 shows the TP rate of the 4 discrimination
methods in 4 time periods. Linear discrimination andthe nearest neighborhood method perform the best.
Yet no linear relationship is found in XQm= {XQ1, XQ2…
XQ18} among the 4 periods. In this case, it is difficult
to characterize the patterns from the discriminants.
Also, when it comes to estimation, there is insufficient information to predict new sets of variables XQm.
In multiple logistic regressions, 18 explanatory
variables Pm cannot be imported at once. To render
the function ultimate, certain variables are selected.
Thus, the combination is different between any 2 time
periods, which is difficult to explain demographically.

(Eq. 5)For one time period, we can obtain one discriminant using every method. Thus, 4 discriminants areapplied to the meshes of the control group. Comparing the original classified result with the discriminatedresult, we can measure the true positive rate (TP rate)of each discriminant using these 4 methods.

TABLE II. FOUR METHODS OF DISCRIMINATION AND THEIR TP RATE APPLIED TO THE CONTROL GROUP IN 4 PERIODS
MethodVariables
Discrimination
Year TP Rate
Note
1990
99.0%
The discriminant funcLinear Discrimination
tion result determines 1995 99.5% A sample mesh must be(FQ)
the classification
2000 99.0% used for estimation.
Dependent:
result.
2005 96.7%
Q= {A, B, C}
Explanatory:
The regression func- 1990 *92.1% Certain variables can be
Pm= {P1, P2…P18}
Multiple Logistic Retion result is the clas- 1995 *95.3% selectively integrated
gression (RQ)
sification probability 2000 *95.2% for optimizing the TP
between any 2 types. 2005 *93.9%
rate.
There is similarity to 1990 99.5%
The pyramid can be
Nearest Neighborhood PQn={PQ1, PQ2…PQ36} each type of population 1995 99.7%
connected with cohort
Q= {A, B, C}
pyramid, by the calcu- 2000 99.0%(DQ)
analysis for estimation.
lated distance.
2005 96.7%
Based on the above, 1990 99.3%
Weighted
PQn={PQ1, PQ2…PQ36}
distance is defined as 1995 98.9% The TP rate is lower
Nearest Neighborhood MQn={MQ1, MQ2…MQ36}
distance in N and the 2000 97.9% than in the NN method.
(D’Q)
Q= {A, B, C}
number of N.
2005 96.9%* The functions of multi-logistic regression analysis in 4 time periods are all built with 2 different variables among 18.

1990

1995

Type A
Type B
Type C

2000

Type A
Type B
Type C2005

Type A
Type B
Type CType A
Type B
Type C

Figure 6. Distribution of region-type meshes after
adjustment

Figure 7. Comparison of adjusted results of 1990
and 2005: It is found that that the identified
meshes listed below correspond to actual
regional development during the period.

25

H. CHEN, N.MAKI, H.HAYASHI

Despite 2005, the nearest neighborhood method constantly has a TP rate of over 99%, almost the same as
linear discrimination. Because the discriminant itself
is the population pyramid of one regional type, connecting to the idea of cohort analysis while examining
the demographic transition from 4 pyramids (19902005) is clearly desirable.
Yet the weighted distance is not found to improve the TP rate of the nearest neighborhood methodin general. We believe that the weighted distancemight increase the difficulty in discriminating the lessaging areas.
Above all, the pyramids of each regional type areapplied to the whole meshes of Osaka and Wakayama
Prefecture to adjust the original cluster result (see fig.
5) using the nearest neighborhood method. Figure 6
shows the adjusted distribution of the 3 regional types
in 4 periods.

method, the discrimination can effectively identify
transformation at the local scale.
IV. examining self-sufficiency in demographictransition patterns
Based on Chapter 3, the pyramids of 3 regional
types are collected to analyze the characteristics ofthe main demographic transition patterns. Referringto studies related to disaster recovery, demographictransition patterns are analyzed to discuss the selfsufficiency that they represent.
Pyramids of 1990, 1995, 2000, and 2005 areshown in table 3. Following the idea of cohort analysis, 5-year aged population Pm in one pyramid and5-year older aged population Pm+1 in the pyramid 5
years later are considered to be the same cohort. Also,
the red line in table 3 marks the most aging cohort ofthe current population in 2030.

Examining the adjusted result
While the rescheduled distance of 4 cluster
analysis is generated, another factor associated withthe difference between the 4 time periods (fig. 6) is
the transition of the study area itself. In this way, theactual urban development of Osaka and Wakayama isgiven in order to examine whether the regional typein the 4 periods can be regarded as one demographictransition.
Comparing the adjusted result in 1990 with theresult of 2005 in fig. 6, we can collect meshes withdifferent regional types (see fig. 7). Remarkably, it isfound that the distribution of these meshes can correspond to certain events in urban development (seelegend in fig. 7).
Those transformed from regional type B to A are
inclined to experience urban renewal (central Osaka)or expanding local cities (Arida). By contrast, meshesfrom type A to B cover the first large-scale cohousing(Senri New Town) in Japan, decaying areas, urban
areas (Nishinari district) and certain aging local areas. And the distribution of meshes from type B to
C is also found to correspond to the marginalizationof Wakayama’s mountainous areas such as the Kiimi
district.
Ultimately, it is proved that the 3 concluded demographic transition patterns can clearly correspondto one specific demographic transition. And these patterns constantly exist. With the nearest neighborhood

1. Pattern A—Sustainable pattern
Baby boomers (those born in 1947-1949) andbaby boom juniors (those born in 1971-1974) lead thetransition in the pyramid of pattern A. Accompanying
the aging of these generations, there is no apparent
decrease in the population of those 20-35 years old. It
implies that, although the fertility decline since 1980
has contributed to a general decrease in the young
population, the young working population has still
constantly emigrated. As life step theory[17] notes,
sufficient employment and higher education are connected to the emigration of the young labor population. Additionally, the fertility of the next generation
can be secured. That is, the young labor populationand new birth rate are stable enough to support the
population structure.
As the white papers [13] and [14] note, with the two
critical factors of well-equipped facilities and sufficient human resources, pattern A presents the places
likely to be sustainable in recovery.
2. Pattern B—Dependent pattern
In pattern B, it is found that a more juvenile population occupies the base of the pyramid in the 1990s.
However, the abundant number of juveniles does
not contribute to the growth of the young workingage population in the next pyramid. This represents
constant and severe emigration of the young labor
26

EVALUATING THE IMPACT OF DEMOGRAPHIC TRANSITION IN THE CONTEXT OF THE TOKAI-TONANKAI-NANKAI EARTHQUAKE, JAPAN

population. Eventually, the decrease in the populationof those aged 20-35 has contributed to an apparent
decrease in new births (bottom of the pyramid) since2000.
Accompanying population aging and the decrease in the juvenile population, population aging
is expected to accelerate gradually. Insufficient advanced service and a lower proportion of the workingage population reveal less capability of the 2 noted
factors in disaster recovery.[13], [14] In contrast to thesustainable pattern, pattern B shows a dependent
characteristic.

Compared with the dependent pattern, a low proportion of working people with social decrease showsthat not only advanced services but also basic services
for living might hardly be provided. The pattern refers
to regions that have less capability to support themselves and that are more likely to become isolated ina disaster.[12] Also, it implies that limited human resources are available for recovery. Accompanying thedemographic transition, a more unstable population
pyramid is expected. Based on the severe population
decline and decaying services, this marginal demographic transition represents places where energy
might be insufficient to continue their recovery.
Above all, the characteristics of the 3 demographic transition patterns are observed in the context
of disaster recovery. Based on the self-sufficiency thateach pattern represents, this index is provided to analyze the demographic transition of exposed areas.

3. Pattern C—Marginal pattern
In pattern C, an extremely aging and unstablepyramid is shown in table 3. The collective amountin every pyramid is 100%. In this case, while a greatamount of the most senior cohorts gradually disappears, the rest of the cohorts, the middle part of the
pyramid, should become wider in the next pyramid.
In contrast, no increase in the proportion of juveniles
and working-age population is found. It clearly represents a social decrease in population in the non-aging
population. Following cohort analysis, a more unstable pyramid with a wider top and a narrower bottom
is formed.

Application to the exposed areas of TTNN Eq.
Applying the 2005 pyramid of demographictransition patterns to the mesh-based Population Census 2005, discrimination analysis using the nearest
neighborhood method is conducted in 151,558 meshes of the whole of Japan. Figure 8 shows the result of
TTNN Earthquake-affected areas (over M4.5).

TABLE III. CHARACTERISTICS OF DEMOGRAPHIC TRANSITION PATTERNS ASSOCIATED WITH SELF-SUFFICIENCYPatterns
Sustainable
Pattern

1990

1995

2005

Characteristics of Self-sufficiency• Young working population and fertility of thenext generation are secured.
• There are well-equipped facilities and sufficient human resources for recovery.
• Ongoing population aging and emigration of
the young working are expected.
• Unsatisfactory advanced services and fewer
human resources weaken region capability.
• More unstable pyramid• Basic services might hardly be maintained
and sufficiency in recovery is unlikely.
• Human resources and service from outside are
required.

Baby boom generation

Junior baby boom generation

Dependent
Pattern

Marginal
Pattern

2000

Youth (20-35)

Main generation

27

H. CHEN, N.MAKI, H.HAYASHIFIGURE 8. Distribution of the sustainable pattern (blue), dependent pattern (brown), and marginal pattern (green):
The combined meshes (black) are the extremely lowly populated areas where the 5-year aged population from the census data is unvailable. According to the population census of 2005, metropolitan cities
are marked in red. Also, the captical cities of the prefecture are marked in orange.
(TTNN), and days of delay (Delay), 4 scenarios are
examined in this paper.

It is found that the sustainable-patterned meshes(blue) comprise wide areas in 3 major metropolitan
cities in the Kinki, Chubu, and capital regions. And
the metropolitan cities (e.g., Fukuoka, Kita Kyushu,
Hiroshima, Okayama, Hamamatsu, and Shizuoka)
are also covered by sustainable-patterned meshes.
Meanwhile, besides the areas surrounding themajor
metropolitan cities, a great amount of dependentpatterned meshes (brown) cover vast areas in the
Chugoku region, Shikoku region, and Shikoku region,even most of the area of the prefectural capital cities(e.g., Miyazaki, Oita, and Kochi). And the marginalpatterned (green) and combined meshes (black) sharea similarity of distribution. Most of these meshes apply to part of the coastline and mountainous areas in
the Kii Peninsula, Shikoku region, Chugoku region,
and Kyushu region.

TABLE IV. SETTING THE SCENARIO OF THE TTNN
EARTHQUAKEScenarioN

TN

Mechanism Individual Individual

TTN

TTNN

Delay

Linkage

Linkage

Linkage

Magnitude

M8.4

M8.1

M8.4

M8.5

M8.4;M8.4

Possibility1)

50%

60-70%No Data

No Data

No Data

Exposure2)

50,41336,97048,17189,31584,536Sea Area

N

TN

T+TN

T+TN+N

T+TN;N

1) Probability in the coming 30 yrs (Headquarters for Earthquake Research Promotion, 2009)
2) Exposure: Areas (km2) exposed above M4.5

Regional seismicity exposure
To analyze the characteristics of regional exposure, it is essential to consider the intensity as wellas the probability of each scenario at the same time.
The meshes are collected using the rule of maximum
intensity (intensity scale) and exposure scenarios(scenario numbers). Note that the number of exposure
scenarios here is not representative of the times of occurrence but of the concept of potential.
The TTNN Earthquake will affect the entire plateof the Nankai Trough. Yet some of the affected areas
are exposed to stronger seismicity than individually in
the N or TTN Earthquake.[19] In this case, it is necessary to take another count of TTNN, apart from the
N and TTN Earthquakes. As for the scenario of “De-V. Place evaluation
Scenario settings
The Tokai-Tonankai-Nankai Earthquake (seetable 4; fig. 9) includes several scenarios as stated by
the Central Disaster Prevention Council.[２] Additionally, a month’s time delay between the Tokai-Tonankai Earthquake and the Nankai Earthquake is likely
to occur and result in severe damage.[18] Considering
the areas affected by the Nankai (N), Tokai-Tonankai(TTN), and Tokai-Tonankai-Nankai Earthquake28

EVALUATING THE IMPACT OF DEMOGRAPHIC TRANSITION IN THE CONTEXT OF THE TOKAI-TONANKAI-NANKAI EARTHQUAKE, JAPANlay,” the N and the TTN Earthquake are 2 individual
events; thus, the calculation is the same as a double
count of the 2 scenarios. Therefore, whichever placeis exposed to the 3 scenarios, it is certain that themeshes are also exposed in the “Delay” scenario.
Figure 9 shows that 2,347 1-km2 meshes among
the inhabited areas are exposed to all scenarios with amaximum intensity above M5.5~. The meshes mostly
cover the Kii Peninsula, Awaji Island, and part of
Osaka Prefecture. A total of 4,177 meshes are also
exposed to all scenarios with a maximum intensity of

M4.5~5.4. Considering the impact of the “Delay” scenario, Osaka City, the core area of the Kinki region, is
exposed to twice the impact.
It is also remarkable that 11,893 meshes are exposed to 2 scenarios with a maximum intensity above
M5.5. Despite the TTNN Earthquake, these areas are
exposed to either the strong seismicity of the N Earthquake, e.g., the southern coastline of the Shikoku
region, or the TTN Earthquake, e.g., Nagoya, Hamamatsu, Shizuoka, and other coastal cities of the Chubu
region.

FIGURE 9. Overlapping seismicity intensity of TTNN Eq., TTN Eq., and N Eq., rendering the distribution of the
inhabited meshes: Considering seismicity exposure, these meshes are classified separately in exposed
scenario numbers and maximum intensity scale. Intensively multi-exposed meshes are marked in astrong color. Rather than a probability map, the figure integrates the information of several scenariosand presents the regional characteristics in exposure.
TABLE V. INTEGRATIVE PLACE EVLUATION
Demographic
Exposure
Scenario
Numbers
3
Time Delay
3
Time Delay

Maximum
Seismicity2)
M5.5~
M4.5~5.4

2

M5.5~

2

M4.5~5.4

1

M4.5~5.4

Population Density3)

Demographic Transition (mesh number, population) 1)
Sustainable Independent Marginal
Pattern
Pattern
Pattern728(1,733)
2,597(13,693)
5,412(10,116)
10,350(21,960)
243
(251)
2,470

1,091
(625)
1,092
(974)
4,517
(2,284)
14,934(6,088)
426
(133)
458

Combined
Mesh

215

313
(19)

168

320
(20)

549

1,415
(62)

2,580

4,867
(240)

44

133
(6)

79

9

Main Distribution
Total
2,347
(2,377)
4,177(14,686)
11,893(12,462)
32,731(28,287)
846
(390)
1,119

Kii Peninsula, Awaji Island,part of Osaka Pref.
Core area of the Kinki region
Southern Shikoku region, coastal
area of the Chubu region
Coast of Kyushu and Chugoku,
northern Shikoku, inner land of
Chubu, partial capital region

1) Population: 1,000 persons
2) Seismicity: M4.5~5.4 refers to scale 5 (Japan Meteorological Agency, JMA); M 5.5~ refers to scale 6 and 7 (JMA).
3) Population Density: persons/ 1-km2mesh

29

H. CHEN, N.MAKI, H.HAYASHI

Also, there are 32,781 meshes exposed to 2 scenarios with a maximum intensity of M4.5~5.4. And
846 meshes are only exposed to the TTNN Earthquake with an intensity of M4.5~5.4. These wide
areas are spread along the coastline of the Kyushu
region and the Chugoku region, the northern Shikoku
region, inner land of the Chubu region, and part of thecapital region.

meshes are found in this area. Generally speaking,severe direct damage of casualties or physical damage
to buildings is rarely of concern. Yet, as the secondary
major metropolitan area, indirect damage is expected
when the TTN and the N Earthquake occur individually with a time delay. Thus, business continuity planning (BCP) is very important in relieving the impact
and adjusting effectively in recovery.
Unlike the capital region, the coastal areas of
Kyushu and Chugoku, Northern Shikoku, and the inner land of Chubu Region are mostly covered by sustainable- and dependent-patterned meshes. Vast areas
are exposed to either the N or the TTN Earthquakewith seismicity of M4.5~5.4. Excluding certain sustainable-patterned meshes in prefectural cities, many
local areas (around 15,000 km2, 30% of the affectedareas) are covered by independent transition-patterned
meshes. This represents the necessity of reinforcing
the capability of local government.

Integrative place evaluation
To integrate the above 5 regional exposure characteristics with the 3 demographic transition patterns
associated with self-sufficiency, fig. 8 and fig. 9 aremapped in order to conduct a place assessment. Instead of a complicated legend, we collect mesh numbers classified into patterns (sustainable, independent,
and marginal; uninhabited and combined meshes) as
well as the exposure characteristics (scenario numberswith maximum intensity) in table 5.
It is found that more than 500 km 2 (around19,000 inhabitants) spread over the Kii Peninsula areexposed to a severe intensity of every scenario, but
are constructed by the marginal-patterned and combined meshes. Based on their demographic characteristics and the exposure, these meshes are considered
to be the most vulnerable regions. More human resource and service support from outside is necessary
in disaster recovery.
In the Chubu region, the vast industrial district
along the coast is mostly classified as having a sustainable pattern. Yet these areas are also exposed tostrong intensity in the TTN and TTNN Earthquake.
Although sufficient social services and a large working-age population are highly cumulative and beneficial to recovery, devastating damage is also expected.
In this sense, a damage evaluation is suggested.
Southern Shikoku shares similar characteristics
of seismicity exposure to the Chubu region. But few
sustainable-patterned meshes are found in this region.
Apart from Kochi City, dependent- and marginalpatterned meshes occupy the long coastal areas andthe mountainous inner land. Exposed to the strong intensity of the N and TTNN Earthquakes, it is believed
that, as an issue affecting the Kii Peninsula, wide-areamanagement is necessary in disaster recovery.
In the Kinki region, the core areas and suburban
areas are both exposed to all scenarios with seismicity of M4.5~5.4. Also, large sustainable-patterned

VI. Conclusion
In this study, we aimed to build a self-sufficiency
index based on demographic transition patterns in order to examine the impact of demographic transitionin the context of the Tokai-Tonankai-Nankai Earthquake, in contrast to previous concerns regarding exposed populations.
This study observed the past 15 years of demographic transition and analyzed a great amountof mesh-based population census data of 1990-2005
to develop 3 demographic transition patterns basedon cluster analysis and a series of discrimination
analyses. And the transition of each pattern reveals
the capability of social services and human resources
concerning the self-sufficiency. Applying the patterns developed to entire affected areas, an integrative
place evaluation with scenario setting is presented toreinforce disaster management in the Tokai-TonankaiNankai Earthquake. The findings are as follows.
1) Sustainable pattern: This was constructed by thebaby boom and baby boom junior generation. A
constant young working population and stablefertility of the next generation result in wellequipped social services and sufficient humanresources that are beneficial to disaster recovery.
2) Dependent pattern: Ongoing population aging
and the emigration of young working people are30

EVALUATING THE IMPACT OF DEMOGRAPHIC TRANSITION IN THE CONTEXT OF THE TOKAI-TONANKAI-NANKAI EARTHQUAKE, JAPAN

3)

4)

5)

6)

7)

8)

expected to accelerate. Unsatisfactory advanced
services and fewer human resources will likely
weaken region capability.
Marginal pattern: Severe natural and social
decrease in population result in a highly aging
population pyramid. In contrast to the dependent
pattern, even basic services are barely maintained. More support from outside is required.
Kii Peninsula: The fewer human resources andpoor social services are unable to cope with disaster recovery in the severely and most multiexposed district.
Chubu region: Vast sustainable-patterned areas
imply stably working immigrants in recovery.
Yet the devastating damage from the TTN and
TTNN Earthquake reveals the necessity of damage evaluation.
Southern Shikoku region: Under the strong intensity of the N and the TTNN Earthquake, outside the only prefectural capital city, the distribution of vast dependent - and marginal-patterned
meshes stresses the importance of wide-areamanagement.
Kinki region: This area is exposed to all scenarios but with a seismicity scale under M5.5.
Severe physical damage and casualties are notexpected, yet BCP is suggested to relieve theimpact while in the time-delay scenario and for
effective adjustment.
Coastal areas of the Kyushu region and Chugoku
region, northern Shikoku region, and inner landof the Chubu region: These areas are exposed toeither the N or the TTN Earthquake with seismicity of M4.5~5.4. However, vast independenttransition-patterned meshes imply the necessityof reinforcing the capability of local government.

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