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HUMAN VULNERABILITY INDEX FOR EVALUATING
TSUNAMI EVACUATION CAPABILITY OF
COMMUNITIES
Yozo GOTO1 and Tadashi NAKASU2
1 Member of JAEE, Senior Engineering Adviser, Kaihatsu-Toranomon Consultant Co., Ltd,
Tokyo, Japan, gotoyozo@mti.biglobe.ne.jp
2 Member of JAEE, Academic Researcher, Chulalongkom University,
Bangkok, Thailand, Tadashi.N@chula.ac.th
ABSTRACT: The rate of fatalities caused by tsunamis vary from community to
community depending on geographical and socio-psychological features peculiar to each.
If the relationship between fatalities rate and geographical and socio-psychological features
can be quantitatively formulated, this can be a concrete means for evaluating a community’s
vulnerability with regard to evacuation (hereafter, evacuation vulnerability) and developing
effective measures that can reduce loss of human life. Therefore, the authors of this paper
proposed to apply a Human Vulnerability Index (HVI), defined as fatality rate divided by
rate of incidence of washed-out buildings, to evaluate the evacuation vulnerability of
municipalities. Using reliable public databases, the authors analyzed the HVIs of twenty
municipalities that were heavily damaged by the tsunami of the 2011 Great East Japan
Earthquake. Then they applied a multiple-regression analysis using the following four
factors as explanatory variables: 1) time allowance for evacuation; 2) preparedness; 3) road
serviceability; and 4) warning effect. They thus extracted a reliable formula (R=0.904),
which enabled them to quantify the effects of these factors on the HVI. Future tasks are to
generalize the formula through application to other tsunami disasters and to establish a
numerical evaluation of geographical and socio-psychological features to enable estimation
of the tsunami evacuation capability of a municipality and the effect of tsunami
countermeasures before a tsunami occurs.
Key Words: Tsunami evacuation, Human Vulnerability Index, The Great East Japan
Earthquake, Geographical feature, Socio-psychological feature
1. INTRODUCTION
1.1 Objectives of this study
Many people lost their lives because they could not effectively evacuate from the tsunami of the 2011
Great East Japan Earthquake (GEJE). People’s vulnerability to a tsunami, i.e., their lack of ability to
escape from areas that are impacted by a tsunami, must essentially be correlated with the geographical
and socio-psychological features of each area. Therefore, if it becomes possible to establish a numerical
- 1 -
Journal of Japan Association for Earthquake Engineering, Vol. 18, No. 6, 2018
model of such a correlation and then measure the vulnerability of the tsunami-impacted area, it will
advance knowledge regarding tsunami-induced human loss and make it possible to evaluate the area's
effort to improve its geographical and socio-psychological features. Moreover, generalization of the
model to make it applicable to other areas and other tsunami disasters will produce a tool for measuring
areal vulnerability to future tsunamis and enable municipalities to prioritize the order of their
countermeasures. Hence, the authors have proposed a Human Vulnerability Index (HVI), which is
defined as the fatality rate divided by the rate of incidence of buildings damaged, in order to measure
the probability of human loss of an area. Furthermore, they have analyzed the correlation between HVI
and geographical and socio-psychological features.
The objective of this paper is to verify the hypothesis that HVI is correlated with major geographical
and socio-psychological features by establishing a numerical correlation model, and to demonstrate that
factors of tsunami evacuation vulnerability can be macro-assessed by this model.
1.2 Literature review
Studies examining features of earthquakes and vulnerability of buildings using the relationship between
number of fatalities and number of collapsed buildings have a history of one hundred years. Imamura1)
used the ratio of the number of damaged buildings to the number of fatalities to denote the features of
an earthquake and presented his findings in a paper for Earthquake Prevention Study Committee Report
No.77 published in 1913. Coburn, et al.2) defined the ratio of the number of fatalities to the number of
residents in collapsed buildings as a Lethality Ratio, and analyzed the causes of deaths. Ohta, et al.3)
improved Kawasumi’s equation, D = 0.01H1.3 (D: number of fatalities, H: number of collapsed
buildings), and proposed a regression equation for D using data from 35 earthquakes from 1872 to 1978.
Miyano and Ro4), using data from 1950 and later earthquakes in which most of the human damage was
caused by collapsed buildings, proposed a relational expression among hypocentral distance, human
damage and building damage. Murakami5) analyzed Lethality Ratios of single-family houses,
multifamily houses and non-wooden multifamily houses by applying multiple regression analyses to
zone-by-zone numbers of collapsed buildings and fatalities in Ashiya due to the 1995 Great Kobe
Earthquake. Matsuda6) defined the number of destroyed houses (collapsed by shaking, burning and being
washed away by tsunami) per death as an HD value, and applied this to characterize the features of each
earthquake disaster since the Meiji era. Moroi and Takemura7) defined the D/S value as the number of
fatalities divided by the number of occupants of collapsed buildings, and applied this to analysis of the
features of damage due to the 1995 Kobe Earthquake. Takemura8) analyzed the ratios of the number of
destroyed houses (collapsed by shaking, burning, being washed away by tsunami, being buried, and so
on) divided by the number of fatalities from major earthquakes since the 1872 Hamada Earthquake, and
reported that inland near-field earthquakes caused about one fatality per ten collapsed buildings.
The above studies mainly analyzed the relationship between the shaking of earthquakes and number
of fatalities. For the study on tsunami disasters, Kuwasawa, et al.9) surveyed the reactions of people in
Owase (one of the tsunami risk-cities in Mie Prefecture) at the time of the 2004 Kii Peninsula Southeast
Offshore Earthquake. This earthquake did not cause a destructive tsunami, but the shaking prompted
some people to evacuate from the coast. Therefore, Kuwasawa, et al. focused on the consciousness of
evacuation and proposed a decision-making model for tsunami evacuation through a multiple regression
analysis using five explanatory variables: earthquake intensity, tsunami risk awareness, prejudice of
normalization, risk degree of dwellings and preparedness.
After the GEJE, Suzuki and Hayashi10) tried to derive a relationship between human damage and
tsunami hazard of the coastal area. They discussed for each local municipality (hereafter, LM) the
correlation between fatalities rate, tsunami intensity, geographical features, population exposure,
disaster awareness of people and assumed tsunami height. Koyama, et al.11), using 500-meter mesh
population data, extracted age and gender distributions of both daytime population and night-time
population in the inundated area and in the building washed-out area. They pointed out that the fatality
rate rose according to aging of population in LMs and that the missing person rate rose where building-
washed-out areas spread over most of the inundated area. Tanishita and Asada12) analyzed the fatalities
rates of 59 regions of Minami-sanriku, Miyagi Prefecture, using the tsunami inundation depth,
- 2 -
experience from 1960 Chilean Tsunami, viewability of the sea and distance to high land as explanatory
variables. They reported that the fatalities rates were high in areas that the 1960 Chilean Tsunami had
not inundated and the sea could not be seen.
Goto13) proposed a Victim Index, defined as number of fatalities divided by number of damaged
buildings, and applied this to six villages in Yamada, Iwate Prefecture. He reported that the rate of
tsunami-experienced persons in the village, cognition rate of tsunami warnings, degree of traffic jams,
rates of participation in tsunami drills, rate of people who helped others and length of evacuation routes
could be related to the Index of each village. Nakasu, et al.14) 15) proposed an HVI, defined as fatalities
rate divided by collapsed-house rate, as a longitudinal analysis tool. And they applied their HVI to the
major municipalities in the Sanriku ria coast of Japan based on their records of damage due to the 1896
Meiji Sanriku Tsunami, the 1933 Showa Sanriku Tsunami and the 2011 Great East Japan Earthquake
and Tsunami (GEJET) disaster, and they analyzed the historical shift of vulnerabilities.
On another front, the City Bureau, Ministry of Land, Infrastructure, Transport and Tourism of Japan
(MLIT) interviewed 10,603 refugees from the coastal LMs of six prefectures from Aomori to Chiba and
asked about their emergency actions16). The City Bureau analyzed the effects of geographical and social
features by grouping the LMs into four zones: urban area close to hills; farming-fishing village close to
hills; urban area in the plain; and farming-fishing village in the plain. Additionally, effects of traffic
jams, risk of human and car mixed evacuations and the effect of steep roads in each LM were analyzed
using trip data of evacuees17).
1.3 Characteristics and Meanings of this Study
This study is intended to:
(1) redefine Nakasu’s HVI by adding the rate of people at home at the time of an earthquake;
(2) measure HVIs of twenty municipalities in Iwate, Miyagi and Fukushima Prefectures numerically
using data of the FSC archive (introduced in 2.2.3 and Appendix 1);
(3) verify HVI as an index for measuring tsunami evacuation vulnerability of LMs; and
(4) present a prediction equation for HVI through a multiple regression analysis that uses four
characteristic values of geographical and socio-psychological features as explanatory variables.
Expansibility of this study is as follows. If HVI can be generalized to make it adaptable to areas
other than the GEJE region, it will be possible to foresee factors causing human damage in risk areas of
coming tsunamis, and to numerically evaluate a way to improve evacuation vulnerability.
The uniqueness of this study is the development of a numerical model of actual evacuation
vulnerabilities of LMs suffered by the large tsunami of GEJE using site-specific geographical and socio-
psychological features. The preceding study by Kuwasawa, et al.9) modeled individual decision-making
for evacuation using data of an earthquake not accompanied by a destructive tsunami. The study by
Suzuki and Hayashi10) analyzed damage caused by GEJE with respect to each LM, but only discussed
qualitatively the effects of tsunami height, exposed population and assumed scenario tsunami height.
On the other hand, the tsunami evacuation simulation using a multi-agent model can be utilized for
disaster education as it can show evacuation action through animation. However, because the effects of
geographical and socio-psychological features are included in the simulation, factors affecting
evacuation vulnerability cannot be explicitly analyzed.
1.4 Structure of this paper
The second chapter describes the analyzed areas, data and their usages. The third chapter describes the
formulation of HVI and the calculation procedure, and verifies the hypothesis that HVI, i.e., the quotient
of risk and hazard, can measure vulnerability. The fourth chapter verifies the hypothesis that HVI can
be defined by geographical and socio-psychological features through multiple regression analysis on the
relationship between HVIs and those features. It also validates HVI through sensitivity analysis on the
effects of geographical and socio-psychological features to the number of fatalities using the regression
equation of HVI. The fifth chapter discusses the availability of some additional explanatory variables
- 3 -
and the method of quantification of the geographical and socio-psychological features when HVI is
utilized as a prediction tool. The sixth chapter lists conclusions of this study.
The contents of this paper originated from the author’s papers for the 2016 JAEE annual conference
and the 16th WCEE18). However, the formulation of HVI has been revised and discussions added.
2. STUDIED AREAS AND USED DATA
2.1 Analyzed Municipalities
Fig. 1 shows LMs that experienced 78 or more fatalities (including missing persons) due to GEJE,
excluding the neighborhood of the Fukushima nuclear power plants. Of these, 20 LMs, from A to T,
were selected for this analysis. The LM labeled U in the figure suffered 188 fatalities, but was left out
of the analysis because: 1) only a small part of the boundary is facing the sea; 2) almost all of the
inundated area was covered by factories and warehouses; 3) 35% of the fatalities were daytime visitors
Drawing from digital map data of Geospatial Information Authority of Japan,
and Japan municipality boundary data of ESRI Japan
Fig. 1 Municipalities to be analyzed
U
Iwate Pref.
Miyagi Pref.
Fukushima Pref.
50km
A
B
C
D
E
F
G
H
I
J
K
L
M
V
N
O
W P
Q
R
X
S
T
Tokyo
Sendai
- 4 -
from other LMs; and 4) the rate of damaged residential houses was low: 4.7% (the rate for the 20 LMs
was 70.1% on average). Additionally, LMs V and W were not included in the target because of the large
deviation in age of the limited samples. And LM X was left out because data of people who had
undertaken tsunami risk preparedness was estimated unnaturally and was extremely small (these
numerical values can be found in footnotes 2) and 3) of Appendix 2).
Another approach would have been to split each LM into areas, such as beach-by-beach or bay-by-
bay, so that features of the areas could be analyzed more clearly. However, such splitting would
unavoidably reduce the number of available data to less than thirty, and there would have been a loss of
significance of the statistical analysis. Therefore, this study did not employ such splitting of LMs.
2.2 Data
2.2.1 Number of fatalities
To obtain the number of fatalities, data from the local governments of Iwate19), Miyagi20) and
Fukushima21) Prefectures were utilized. The number of fatalities is defined as the number of dead
persons found in the LM area not including indirect deaths, and missing persons counted by the LM.
Dead visitors from other LMs were included in the number of fatalities because, in order to analyze
evacuation vulnerability of an LM in the daytime, the number of fatalities including visitors should be
used. 16 LMs published the number of fatalities of their own citizens. The numbers only differed by
1.9% in total from the number of fatalities defined above and the standard deviation of the difference
was 7.8%.
2.2.2 Population and number of houses
The 2010 Census was used to obtain for populations and numbers of houses. As the census does not
include the number of houses of LMs I, J, K and R, these numbers were estimated by the method denoted
in footnote*3 of Table 2.
2.2.3 Number of damaged houses and inundation depth
Data of the Fukkou-Shien-Chousa archive22) (hereafter, the FSC archive) was used to obtain the number
of damaged houses and inundation depth data. The FSC archive, outlined in Appendix 1, is a GIS
database compiling data from interview surveys conducted by the City Bureau of MLIT.
15) All the
damaged house statistics including inundation depth are in one folder for each LM of the archive. In this
study, the number of houses that were washed out by the tsunami was used as the number of damaged
houses. For the inundation depth, the average of inundation depths evaluated at the locations of washed-
out houses was applied.
There might be some biased counting in the data having fuzzy meaning because many people had
to participate to compile the data for different prefectures and there are many LMs in them. Therefore,
cumulative addition curves of numbers of washed-out houses were calculated for each LM taking
building area as a parameter. The curves were averaged with respect to three prefectures: Iwate (six
Fig. 3 Cumulative addition curves of washed-
out houses vs. building area > 30m2
Building area (
m
2)
Miyagi
Fukushima
Iwate
Rate of cumulative addition
(number in area 500m2 as 1)
Fig. 2 Cumulative addition curves of washed-
out houses vs. building area
Building area (
m
2)
Miyagi Fukushima
Iwate
Rate of cumulative addition
(number in area 500m2 as 1)
- 5 -
LMs), Miyagi (eleven LMs) and Fukushima (three LMs). Then, the three cumulative additional curves
were drawn, as seen in Fig. 2, taking building area as the horizontal axis and rate of cumulative addition
as the vertical axis. Non-negligible discrepancy is seen among the three curves. The counting rules
concerning attached small houses, such as barns and garages, seemed to differ among the three
prefectures. Therefore, houses of less than 30m2 in building area were omitted from the cumulative
addition and the three curves were re-drawn as shown on Fig. 3. By omitting small houses, the curves
of Iwate and Miyagi are closely aligned. Although the curve of Fukushima does not align well, this
study decided to use the number of washed-out houses greater than 30m2 in the building area, taking
into account the small number of target LMs in Fukushima.
2.2.4 Sufferers for analysis, rate of people at home and evacuation trip data
Attributes of people who suffered and their evacuation trips were retrieved from the individual
evacuation method folder of the FSC archive. The archive compiled individual attributes, evacuation
trips and answers to questionnaires covering 10,603 sufferers in the coastal LMs of six prefectures, from
Aomori to Chiba. However, in order to focus on the behavior of the persons who might die if they would
not evacuate, this study selected the data of people who were at home at the time of the earthquake or
returned home before the tsunami arrived, and concurrently whose houses had completely collapsed due
to the tsunami. Hereafter, this study denotes these persons as sufferers for analysis.
The rate of people at home was defined as the number of sufferers for analysis divided by the total
number of people whose houses were completely destroyed. The values are listed in Table 2 and are
consistent with the values of a preceding study23). It should be noted here that the individual attribute
data of the FSC archive defined “completely collapsed houses” to include “washed-out houses”.
Evacuation routes and elapsed time for this study were extracted from the trip data of the FSC
archive. However, as this was based on interview surveys and not on instrumental observation, it should
be recognized that the accuracy is somewhat limited.
2.2.5 Data correction with age
Distribution of age data on individuals in the FSC archive deviated from that of the population.
Therefore, age distribution was compared with the census of small segments24) which had an average of
137 segments per LM, and correction coefficients with age were defined extracting a group of small
segments which covered tsunami inundation area.
Then, the data on individuals in the FSC archive were weighted by the correction coefficient so that
the age data distribution came close to that of the census when summing up the data for each LM (for
details, see Goto’s past paper25)). Table 1 shows the average and standard deviation of the correction
coefficients, which were divided into three age ranges: 20-49, 50-69 and over 70. It might have been
desirable to divide them into six ranges by adding a sex range. However, such detailed division was not
applied because there were too few data.
2.2.6 Discussion on use of survivors' data
While it is more favorable to use data including fatalities for the analysis of evacuation vulnerability, it
was impossible to get such data with the same accuracy as for survivors. Therefore, this study used only
the data of survivors, as mentioned in 2.2.4.
The ratio of fatalities to sufferers for analysis (defined at 2.2.4), however, needs to be checked. The
total number of sufferers for analysis was estimated as 127,000. This was based on the rate of people at
home (75%) and the total number of completely destroyed houses in the object region (70,800 houses)
with 2.4 people living in one house (2.66 persons per a household in Iwate, Miyagi and Fukushima
according to the 2010 Census, with vacant houses estimated to be 10%). The total number of fatalities
Table 1 Average and standard deviation of correction coefficients with age of twenty LMs
Age 20~49 50~69 Over 70
Average 1.209 0.9 1.044
Standard deviation 0.357 0.137 0.185
- 6 -
in the subject region was 16,900. Mikami26) reported that 80% of the fatalities were estimated to have
been at home or in the process of evacuation. Therefore, the number of fatalities among sufferers for
analysis was 13,000, that is, 80% of 16,900. Consequently, the ratio of fatalities to the sufferers for
analysis is estimated at around 10%.
If the data concerning fatalities could be added, some aspects of evacuation vulnerability might be
emphasized; for example, evacuation roads could be longer, the rate of preparation of emergency carry-
out bags would be lower, and so on. Consequently, analysis without data of fatalities might lack
sharpness. However, it is not such a simple on-off phenomenon such that evacuation roads longer than
a certain length automatically result in evacuation failure. Success or failure of tsunami evacuation
depends on many widely dispersed factors. As the data of fatalities were 10% of that of sufferers for
analysis, the authors considered the analysis that lacked data of fatalities to not cause a fatal error.
This study might be misunderstood as having developed an evaluation method of the number of
fatalities by using survivor data. However, the main subjects of this study are a regression analysis of
HVI and its validation. Although evaluation of the number of fatalities is introduced in Chapter 5 of this
paper, it is aimed at validating the availability of the regression HVI. Therefore, the validity of using
data from survivors in the regression analysis for HVI should be discussed, and this study considered it
possible, as mentioned above.
3. FORMULATION OF HVI
3.1 Definition of HVI
This study defined HVI as an index for measuring the evacuation vulnerability of people in an area. It
is hypothesized that the number of fatalities (= risk) could be computed by multiplying the exposed
population by HVI (= vulnerability) and by inundation depth of a tsunami (= hazard), as described by
Eq. (1). For simplification, hazard is expressed by inundation depth, although the flow velocity may
affect it.
Number of fatalities Exposed population × HVI × Inundation depth (1)
HVI is formulated by Eq. (2).
Number of fatalities caused by a tsunami 1
Population in area Rate of people at home
HVI = × 100 (2)
Number of washed-out houses
Number of residential houses in area
Both the number of fatalities and the number of washed-out houses monotonically increase with
increased depth of tsunami inundation. However, taking their ratio, HVI becomes independent of
inundation depth, as shown by Eq. (2). In addition, the denominator and numerator of Eq. (2) are divided
by the number of residential houses and the population in the object area, respectively, enabling HVI to
be non-dimensional. Residential houses and population in a common area have to be counted. Therefore,
this study used each municipality as the common area, considering data accessibility and versatility.
Exceptionally, Ishinomaki was divided into rias coast area and flatland area, and Sendai was divided
into administrative wards because of its broadness.
The definition of completely collapsed houses includes washed-out houses. It might have been worth
considering applying the number of completely collapsed houses instead of the number of washed-out
houses in Eq. (2) in order to fit the definition of "sufferers for analysis" mentioned in subsection 2.2.4.
However, as the "number of washed-out houses" of Eq. (2) is used only to evaluate hazard, it is not
necessary to use the same definition as in 2.2.4. Therefore, this study used the number of washed-out
houses for the reason mentioned in section 3.3.
Multiplication of 100 in Eq. (2) is for improved readability of the HVI value.
×
- 7 -
3.2 HVI of each LM (Local Municipality)
The HVIs of LMs from A to T of Fig. 1 are listed in Table 2, together with their calculation parameters.
The calculated HVIs are scattered from 3 to 33. Chapters 4 and 5 will verify that this scattering means
the difference among evacuation vulnerabilities of the LMs.
3.3 Verification of independence of HVI of inundation depth
HVI and inundation depth must be independent of each other in order to verify the hypothesis that HVI
represents vulnerability. The right end column of Table 2 lists the average inundation depths evaluated
at the locations of washed-out houses in each LM. Fig. 4 is a plot of the HVI of each LM with the
averaged inundation depth on the horizontal axis. The dotted line in the figure is the linear regression
Table 2 Parameters and HVI
LM Fatalities
*1 Population*2 Residential
houses*3
Rate of people
at home*4
Washed-out
houses*5 HVI Inundation
depth*6
A 514 59,430 25,010 0.718 1,453 20.7 5.48 (m)
B 752 18,617 7,950 0.729 1,990 22.1 5.40
C 1229 15,276 6,130 0.658 2,989 25.1 7.98
D 1040 39,574 18,420 0.724 2,303 29.0 7.45
E 419 40,737 16,580 0.698 2,397 10.2 7.06
F 1,763 23,300 8,550 0.803 4,210 19.1 11.04
G 1,326 73,489 25,670 0.741 5,817 10.7 7.23
H 812 17,429 5,540 0.752 3,836 8.9 10.71
I 850 10,051 3,450 0.762 2,268 16.9 13.41
J 1,106 23,611 8,105 0.881 3,974 10.8 8.19
K 2,597 137,215 56,765 0.823 4,163 31.4 4.54
L 1,086 42,903 15,450 0.835 2,862 16.4 3.80
M 78 20,416 6,650 0.895 829 3.4 4.54
N 345 132,306 70,640 0.561 1,407 23.3 4.28
O 950 73,134 25,820 0.623 1,888 28.5 4.68
P 270 34,845 11,520 0.819 1,089 10.0 3.37
Q 698 16,704 5,310 0.756 2,061 14.2 5.99
R 99 8,224 3,068 0.827 440 10.1 7.58
S 636 70,878 25,050 0.631 1,083 32.9 5.28
T 330 342,249 147,740 0.733 914 21.3 3.31
*1 Number of dead and missing persons, except related deaths, found in each LM. Iwate, Miyagi and
Fukushima Prefectures compiled and published them on the web.
*2 Extracted from 2010 Census data.
*3 Extracted from 2010 Census data; however, the numbers for I and R were not published and for J
and K, only sum of J + K was published. Therefore, the numbers for I, R and K were evaluated
using the rate of number of residential houses to that of population of neighboring LMs. J was
evaluated by subtracting K from the sum.
*4 Rate of people who were in their houses at the time of the earthquake or returned to their houses
before the tsunami, and whose houses completely collapsed. Extracted from the FSC archive.
*5 Number of washed-out houses with building areas greater than 30m2. Extracted from the FSC
archive.
*6 Average of inundation depths evaluated at the locations of washed-out house. Extracted from the
FSC archive.
- 8 -
line, which indicates a somewhat decreasing HVI with increased inundation depth. The correlation
coefficient of HVI and the average inundation depth was −0.184. However, as the significance level p
was 0.437, correlation between HVI and inundation depth was denied from a statistical point of view.
As an alternative, other HVIs were calculated using the number of completely collapsed houses
including washed-out houses, instead of the number of washed-out houses only, and analyzed
correlation to the average inundation depths evaluated at locations of completely collapsed houses. The
correlation coefficient was 0.123 and p was 0.605. Therefore, the independence of HVI would be
somewhat improved if the number of completely collapsed houses were used. However, as Takemura8)
pointed out, the definition of completely collapsed house has altered historically and the recent
administrative definition seemed to depart from the structural definition to some extent. In addition,
considering utilization of overseas data, such a clear definition as washed-out house should be applied.
4. MULTIPLE REGRESSION ANALYSIS ON HVI AND VALIDATION
4.1 Explanatory variables
Many multiple regression analyses on HVIs were executed, taking many different combinations of
geographical and socio-psychological features as explanatory variables, as shown in Appendix 2. The
following four explanatory variables were extracted as the best combination. The clarity of geographical
and socio-psychological meanings of the variables were emphasized in the extraction:
Allowance period A: Tsunami arrival time / Distance to a safe place
Preparedness P: Rate of people who had prepared emergency carry-out bags
Road serviceability R: Rate of car-using evacuees × Car speed
Warning effect We: Tsunami warning height × Cognition rate
4.1.1 Allowance period A
Allowance period A is defined as tsunami arrival time divided by distance to a safe place. Tsunami
arrival time means the time for the tsunami to arrive at each LM on the coast after the earthquake and is
evaluated as 35 minutes for the rias coast area and 53 - 65 minutes for the flatland area, as shown in
Table 3, by referring to previous studies17), 23). Distance to a safe place is the average moving distance of
persons in an LM who evacuated their completely collapsed houses, without detour, to high land or
inland non-inundation area or vertical evacuation facilities. The individual moving distance was
calculated from the individual's evacuation trip data of the FSC archive, using the same method as Goto’s
preceding study25).
When analyzing the average moving distances, it should be noted that in many LMs the number of
car-using evacuees was roughly equal to that of walking evacuees. Therefore, the following equivalent
Fig. 4 HVI vs. Average inundation depth
HVI
HVI = 22.0 - 0.569D
p value: 0.437
Ave ra
g
e inundation de
p
th
(
m
)
- 9 -
evacuation distance, which converted the evacuation distance of a car to that of a pedestrian, was used
(Table 3).
ed(i) = W(i) × rw(i) + D(i) × rc(i) × 0.244 (3)
where, ed(i): Equivalent evacuation distance of LM i
W(i): Average walking evacuation distances of LM i (average of the distances from house
to a safe place)
rw(i): Rate of walking evacuees of LM i
D(i): Average car-driving-evacuation distances of LM i (average of the distances from
their house to a safe place)
rc(i): Rate of car-using evacuees of LM i
0.244 = ΣW(i) / ΣD(i): Conversion factor of car-driving-evacuation distance into walking
evacuation distance (average of ratios of all target LMs)
Fig. 5 shows the correlation between HVI and allowance period A. The plots are scattered considerably,
but HVI tended to increase when the allowance period decreased.
4.1.2 Preparedness P
Preparedness P is defined as the rate of sufferers for analysis who had prepared emergency carry-out
bags beforehand, and was extracted from personal interview data of the FSC archive (refer Table 3). The
Table 3 HVI and element parameters for explanatory variables
LM HVI
Tsunami
arrival
time
Equivalent
evacuation
distance
Car
velocity
Rate of car-
using-
evacuees
Number o f
data ed, rc
*1
Tsunami
warning
height
Cognition
rate of
warning
Number o f
data cr
*2
t (minute) ed (m) v (km/h) rc h(m) cr
A 20.7 35 184 8.6 0.504 114 3 0.513 184
B 22.1 35 157 6.4 0.439 35*3 3 0.366 167
C 25.1 35 247 6.4 0.453 90 3 0.368 151
D 29.0 35 175 6.1 0.253 134 3 0.505 209
E 10.2 35 126 8.4 0.586 181 3 0.422 257
F 19.1 35 166 11.1 0.445 145 3 0.566 308
G 10.7 35 247 7.1 0.503 357 6 0.620 490
H 8.9 35 186 7.6 0.551 179 6 0.702 275
I 16.9 35 159 6.2 0.387 94 6 0.589 120
J 10.8 35 135 8.8 0.465 156 6 0.563 236
K 31.4 60 433 6.9 0.487 460 6 0.576 789
L 16.4 60 478 7.7 0.516 68 6 0.430 151
M 3.4 65 166 9.5 0.736 42 6 0.675 57
N
23.3 65 510 13 0.956 31 6 0.501 57
O 28.5 65 501 8.9 0.601 88 6 0.535 155
P 10.0 65 551 18.5 0.703 73 6 0.644 149
Q 14.2 60 425 17.9 0.922 96 6 0.437 134
R 10.1 65 293 13.2 0.721 43 3 0.520 74
S 32.9 65 348 14.1 0.925 97 3 0.301 193
T 21.3 53 190 8.1 0.552 78 3 0.320 110
*1
N
umber of evacuees who evacuated their houses to a safe place without detou
r
, and whose houses completely
collapsed.
*2 Number of sufferers-for-analysis, namely, people who were at their houses at the time of the earthquake or
returned to their houses before the tsunami, and whose houses completely collapsed.
*3 A part of the data is missing.
- 10 -
Table 4 Explanatory variables and Regressed HVI
LM HVI
Allowance
period Preparedness Road
serviceability
Warning
effect Number of
data
*1
Regression
HVI
*2
A
= t / ed
(
minute/m
)
P R = v × rc
(
km/h
)
We = h × c
r
(
m
)
A 20.7 0.190 0.498 4.34 1.539 184 14.9
B 22.1 0.222 0.406 2.81 1.098 167 20.7
C 25.1 0.142 0.429 2.90 1.104 151 32.1
D 29.0 0.200 0.342 1.54 1.515 209 30.1
E 10.2 0.278 0.480 4.92 1.266 257 10.3
F 19.1 0.211 0.369 4.94 1.698 308 15.5
G 10.7 0.141 0.403 3.57 3.720 490 18.2
H 8.9 0.188 0.469 4.19 4.212 275 10.3
I 16.9 0.220 0.376 2.40 3.534 120 14.2
J 10.8 0.259 0.361 4.09 3.378 236 10.0
K 31.4 0.139 0.418 3.36 3.456 789 19.1
L 16.4 0.126 0.434 3.97 2.580 151 21.9
M 3.4 0.392 0.410 6.99 4.050 57 4.2
N
23.3 0.127 0.272 12.43 3.006 57 18.5
O 28.5 0.130 0.289 5.35 3.210 155 23.9
P 10.0 0.118 0.412 13.01 3.864 149 12.4
Q 14.2 0.141 0.351 16.51 2.622 134 12.5
R 10.1 0.221 0.414 9.52 1.560 74 10.4
S 32.9 0.187 0.133 13.04 0.903 193 37.0
T 21.3 0.279 0.225 4.47 0.960 110 23.1
*1 Number of evacuees who evacuated directly to a safe place without detour and whose
houses completely collapsed.
*2 Regression HVI is discussed in section 4.2.
correlation between HVI and preparedness P is shown in Fig. 6. To prepare an emergency carry-out bag
beforehand is evidence of consciousness that emergency evacuation might be needed. Considerable
correlation was observed as the significance level p was 0.019.
The rates of beforehand-executing of "talking about evacuation method, communication tool,
designated place, and so on among family", "checking tsunami hazard map" and "participating in
tsunami evacuation drill arranged by the community" were also extracted and were analyzed to
determine their correlations with HVI. However, the p values were 0.545, 0.474 and 0.286, respectively,
and no significant correlations with HVI were seen.
4.1.3 Road serviceability R
Ideally, road serviceability R should be defined by the road traffic capacity, namely, multiplication of
the available number of cars and their velocity, in each LM area. However, such data could not be
obtained easily. Therefore, data on the actual performance of the tsunami evacuation was used. The
better the R, the greater the actual number of car-using evacuees and the higher the car velocity are
assumed to be. The car velocity v and the rate of car-using evacuees rc were extracted from the
evacuation trip data of the FSC archive, and their multiplication is applied as R (Table 3).
Fig. 7 shows the relationship between R and HVI. As there are outliers N, Q and S and significance
level p is 0.653, no correlation is seen. However, if the outliers are skipped, p becomes 0.039 and
correlation is improved, as shown by the red dotted line. As LMs N, Q and S are flatland and low
population areas, cars could be driven at their natural velocity. Therefore, the HVIs of these LMs must
have been affected strongly by other factors. For example, in LM S, preparedness P is 13% (the average
of 20 LMs was 37%), and the evacuation rate is 54% (the average was 77%). Hence, many people in
LM S could have lacked wariness over tsunamis, and lost their lives without attempting evacuation.
- 11 -
4.1.4 Warning effect We
The first announcement of a large-tsunami warning forecast tsunami heights as three meters for the
Iwate and Fukushima coasts and six meters for the Miyagi coast. Multiplication of announced tsunami
height h and cognition rate of warning cr is defined as Warning effect We. Fig. 8 shows the relationship
between We and HVI. Although a certain level of fluctuation is seen, such correlation as significance
level p being 0.045 is confirmed.
4.2 Correlation among explanatory variables, and comprehensive influence of geographical features
Correlation coefficients of the four explanatory variables are listed in Table 5. While it is desirable for
the correlation coefficients to be low, preparedness P was to some extent related to road serviceability
R and warning effect We . Correlation between P and the five element variables v, rc, h, cr and ed were
analyzed and the results are listed in Table 6. P indicates a negative correlation to the rate of car-using
evacuees rc. Therefore, many of the people who prepared emergency carry-out bags beforehand seemed
to have intended to evacuate on foot. P indicates a positive correlation to cognition rate of warning cr.
Persons who prepared for evacuation were maintaining a wariness over tsunamis and attention to
tsunami warnings. When the sensitivity analysis on the explanatory variables is conducted, these
correlations should be considered.
On the other hand, geographical features could have a strong influence. Therefore, a dummy variable
that defined the rias coast region (north of Oshika peninsula) as 0 and the flat coast region (southwest
of Oshika peninsula) as 1, was introduced and its correlation with the element parameters (Table 3) and
the explanatory variables (Table 4) was analyzed.
The results are shown in Table 7. Geographical features are distinctly correlated to tsunami arrival
time t and evacuation distance ed, but does not affect allowance period A because the correlations are
compensated for through calculation of the ratio between t and ed. Correlation to the rate of car-using
evacuees rc is clear. The number of car-using evacuees rc might have increased because the evacuation
Fig. 5 HVI vs. Allowance period A
Allowance period
A
(min./
m
)
H
V
I
HVI = 28.4 - 51.7
A
p value: 0.062
HVI = 36.7 - 49.2
P
p value: 0.019
Fig. 6 HVI vs. Preparedness
P
HVI
Preparedness P
HVI = 24.55 - 1.487
R
p value : 0.039
Fig. 7 HVI vs. Road serviceability
R
Road serviceability R (km/h)
HVI HVI = 19.58 - 0.211
R
p value : 0.653
Fig. 8 HVI vs. Warning effec
t
We
HVI = 26.29 - 3.258
I
e
p
value: 0.045
Warning effect We (m)
HVI
- 12 -
distance was long and many roads were easy to drive on in the flat region. However, in conclusion, the
geographical features do not correlate with HVI, as p is 0.649 and the correlation coefficient is 0.109.
The reasons are inferred as follows. LMs M, R and T are located in the flat coast region, but hills are
near the coast, which shortens evacuation distances. While, in LMs K and O, HVIs are pushed up due
to traffic jams lowering car velocities.
4.3 Formulation of regression equation and result of multiple regression analysis
A regression equation is formulated as shown Eq. (4). To prevent HVI from being negative, a monomial
of exponential terms is applied.
HVI = eα × Aβ × Pγ × RΔ × Weε (4)
Taking the natural logarithm of both sides of Eq. (4), Eq. (5) is obtained.
ln(HVI) = α + β×ln(A) + γ×ln(P) + Δ×ln(R) + ε×ln(We) (5)
Applying the explanatory variables listed in Table 4, regression coefficients α, β, γ, Δ and ε for Eq.
(5) were evaluated by linear multiple regression analysis (IBM SPSS Statistics was used), and the
following regression equation for HVI was obtained.
Regression HVI = 2.886 × A-1.117 × P-0.852 × R-0.423 × We-0.441 (6)
The evaluated regression coefficients, their significance probabilities p and standardized coefficients are
listed in Table 8. As each p value is less than 0.02, good fit of the analysis is confirmed. The standardized
coefficient of β was maximum and that of Δ was second. This means that allowance period A is most
effective on ln(HVI) and road serviceability R is second. VIF, Variance Inflation Factor, is the value that
increases if correlation between the explanatory variables becomes higher, and for a VIF of more than
10, the multiple regression analysis becomes unstable because of multicollinearity. As the VIF of each
explanatory variable is less than 10, the solution of the multiple regression analysis is confirmed to be
stable27).
Table 9 lists evaluation values of the regression equation, with a multiple correlation coefficient R
of 0.904 and an adjusted determination coefficient R2 of 0.768 was achieved. The values of the
regression HVIs are calculated from the Eq. (6) and are listed in the right end column of Table 4.
Additionally, the plot of the relationship between HVI and the regression HVI is shown in Fig. 9. A
trend of similarity is recognized from the plots, even though the standard error is 4.66 and the deviation
from the one-to-one line is 12 at maximum.
Table 5 Correlation factors between explanatory
variables
A P R
We
Allowance period : A 1 0.040 -0.214 -0.105
Preparedness : P 0.040 1 -0.380 0.238
Road Serviceability : R -0.214 -0.380 1 0.058
Warning effect : We -0.105 0.238 0.058 1
Table 6 Correlation facto
r
s of preparedness
P to element variables
P
Car velocity : v -0.287
Rate of car-using-evacuees : rc -0.415
Height of warned tsunami : h 0.087
Cognition rate of warning : cr 0.454
Evacuation distance : ed 0.250
Table 7 Correlation factors between geographical aspect vs. element and explanatory variables
t
ed A=t / ed
P
v
rc
R=v×rc
h
cr We=h×cr HVI
Rias or flat 0.981 0.744 －0.143 －0.447 0.563 0.690 0.638 0.302 －0.123 0.138 0.109
- 13 -
4.4 Validation of HVI through sensitivity analysis
In order to estimate the number of fatalities using the regression HVI, Eq. (2) is deformed to Eq. (7).
The regression HVI of Table 4 is applied to Eq. (7), and the results are listed in Table 10. The deviation
of the estimated number from the actual number is 0.5%.
Risk Exposed population
Number of fatalities = Population × Rate of people at home
× Regression HVI × (7)
Vulnerability Hazard
In addition, one of the four explanatory variables was multiplied by the rate of increase of standard
deviation 1 σ, and regression HVI was calculated keeping the other three variables at their original
values. Then, the number of fatalities was evaluated from Eq. (7), and is listed in rows (1) - (4) of Table
11. If the same cost were required to increase each explanatory variable by the rate of 1 σ, increase of
allowance period A would be the most effective, as already presumed through the comparison of the
standardized coefficients of Table 8. If all explanatory variables were increased by the rate of 1 σ, the
number of fatalities would decrease by 41%, as shown in row (5) of Table 11.
Table 8 Regression coefficients and evaluation values
α β γ Δ ε
Regression coefficient 1.060 －1.117 －0.852 －0.423 －0.441
Significance probability p 0.017 0.000 0.003 0.002 0.004
Standardized coefficient ----- －0.655 －0.465 －0.477 －0.424
VIF 1.102 1.387 1.252 1.287
Table 9 Evaluation value of
r
egression equation
Correlation coefficient R 0.904
Determination coefficient R2 0.817
Adjusted R2 0.768
Standard deviation σ 0.270
Significance probability p 0.000
N
umber of washed-out houses 1
Number of residential houses 100
×
Fig. 9 HVI vs. Regression HVI
Regression HVI
HVI
- 14 -
As shown in Table 7, a positive correlation exists between preparedness P and warning cognition
rate cr, which is the element variable of Warning effect We. Therefore, the number of deaths should be
calculated when P and cr are increased together. The result is shown in row (6) of Table 11 and proved
that the increase of P + cr is as effective as the increase of allowance period A.
Negative correlation existed between preparedness P and the rate of car-using evacuees rc, which
was the element variable of road serviceability R. Therefore, the case in which P and cr are increased
and rc is decreased by the rate of 1 σ was analyzed. The calculated result is shown in row (7) of Table
11. The decrease of rc has the effect of compensating the increases of P and cr.
For the actual evacuation during the GEJET, P and rc in the LMs studied were in negative correlation.
However, if improvements of roads for evacuation, optimization of car usage and sophistication of
disaster education were simultaneously implemented, P and rc would not be in negative correlation and
their synergistic effect would be expected to develop.
5. DISCUSSION
5.1 Selection of explanatory variables and their combination
The hypotheses that HVI is independent of inundation depth and can be an index for evaluating
evacuation vulnerability according to geographical and socio-psychological features are verified
through the analyses described in section 3.3 and chapter 4. An inevitable dispersion exists in the data
from the interview surveys. Therefore, it can be emphasized that a set of explanatory variables that
achieves such high-accuracy regression as correlation coefficient 0.904 was developed. However, this
set of explanatory variables does not comply with the sufficient condition, so additional explanatory
variables are discussed.
5.1.1 Earthquake intensity
The rates of people of the LMs who immediately thought that a tsunami would come because of the
large shaking of GEJE were 49.8% on average, with a standard deviation of 0.173. However, the
correlation coefficient between this rate and HVI was as low as −0.188, and multiple regression analysis,
including this rate as one of the explanatory variables, did not improve regression accuracy. The JMA
seismic intensities of LMs studied were from 5-upper to 6-upper, and the durations of the shaking were
similarly long. Therefore, the earthquake intensity that had the effect of prompting people to evacuate
did not differ much among the LMs and the differences among the HVIs of the LMs in this study were
estimated to be determined by factors other than earthquake intensity.
However, if HVI is applied to a tsunami disaster that is accompanied by an earthquake of different
intensity, the effect of earthquake intensity should obviously be added to the multiple regression analysis
as one of the explanatory variables.
Table 11 Sensitivity analysis on explanatory variables
Modified explanatory variable Number of fatalities
(1) Allowance period A +1σ 12,080
(2) Road serviceability R +1σ 13,540
(3) Preparedness P +1σ 14,090
(4) Warning effect We +1σ 14,220
(5) All explanatory variables +1σ 6,890
(6) P +1σ and warning cognition rate cr +1σ 12,900
(7) P +1σ, warning cognition rate cr +1σ
and rate of car-using evacuees rc –1σ 15,130
Table 10 Estimated number of
fatalities vs. actual number
N
umber of
fatalities
Actual number 16,900
Estimated number
by Eq. (7) 16,819
- 15 -
5.1.2 Aging rate
A high damage rate of aged people is a well-known feature11), 12). Therefore, HVI is presumed to be high
in the area of high aging rate. However, the correlation coefficient between the aging rate, which is
defined by the ratio of the population over 70 years old to that over 20 years old, and HVI is as low as
−0.094. The accuracy of the multiple regression analysis, which was added to the aging rate as one of
the explanatory variables, was not improved. Here, the average of the aging rate of 20 LMs was 0.284
and the standard deviation was 0.0256. Therefore, the variation of the aging rates was generally low.
Moreover, LM I and LM J, which had relatively higher aging rates, were on the rias coast and had
experienced tsunamis frequently. And as reported by the interview survey of Goto and others22), the
experience seemed to have transmitted from one generation to the next. In such LMs, awareness of
tsunamis was shared among the people and there were fewer fatalities. Thus, the HVI might not become
high, even if the aging rate is high.
5.1.3 Visibility of sea
Tanishita12), 28) reported a tendency in which the number of fatalities increased in areas where there was
no direct view of the sea coast. This study could not take into account the visibility of the sea coast
because the minimum resolution of the area was the size of each LM. If a finer areal resolution were
applied, the visibility of the sea coast would be evaluated by utilizing 3-dimensional GIS and such, and
its effect could be studied by adding this as one of the explanatory variables.
5.1.4 Coastal levee
The structural effect of coastal levees is automatically reflected in the hazard term of Eq. (7), because
the presence or absence of coastal levees affects the inundation depth and rate of washed-out houses. As
for the spiritual effect, people in the area without effective coastal levees might have heightened
wariness over tsunamis and evacuate quickly. These effects are reflected in preparedness P.
On another front, taking coastal levees as one of the explanatory variables, it could be possible to
analyze the following effects: The presence of coastal levees might reassure people living near-by and
induce them to stay in their houses, and they might conceal the tsunami and cause people to delay their
evacuation; conversely, it might delay the tsunami inundation. However, these effects of coastal levees
could not be analyzed in this study, because no data that covered the location and height of all coast
levees of 20 LMs were found.
5.2 Evaluations of key parameters to analyze the evacuation vulnerability of a tsunami-anticipated
area using HVI
In order to estimate HVIs for tsunami-anticipated areas and to evaluate evacuation vulnerability, or to
analyze the factors that affect the number of fatalities using Eq. (7), the number of washed-out houses
and the values of the four explanatory variables must be established in advance.
5.2.1 Number of washed-out houses
The number of washed-out houses can be calculated through the following steps:
(1) Obtain the inundation depth distribution from the results of tsunami inundation simulations that the
central or prefecture governments provide to the LMs, and
(2) Calculate the number of washed-out houses using a fragility curve. The fragility curve indicates the
relationship between inundation depth and rate of washed-out houses, as proposed by Koshimura,
et al.29) and others.
However, death by tsunami can happen even if the number of washed-out houses is zero. For these
cases, it is necessary to develop another type of explanatory variable that expresses the tsunami hazard.
This is a future research issue.
5.2.2 Allowance period A (tsunami arrival time/ evacuation distance)
The tsunami arrival time can be estimated from tsunami simulation conducted by central or prefecture
governments. The evacuation distance can be calculated by GIS-aided search of roads that connect
- 16 -
starting points with safe places. The former is people's locations when the earthquake strikes, like home,
and the latter is the outer area of the assumed inundation area or the vertical evacuation facility.
5.2.3 Preparedness P (rate of persons who had prepared emergency carry-out-bags beforehand)
Data from recent existing surveys can be utilized. In LMs that have not completed such a survey, the
data can be collected by questionnaire.
Evaluating the preparation rate of emergency carry-out bags helps to evaluate the level of people's
self-directive risk awareness. It should be noted that disaster education such as only urging people to
prepare emergency carry-out bags does not directly reduce the number of fatalities.
5.2.4 Road serviceability R (car velocity x rate of car-using evacuees)
R is obtained by multiplying car velocity by the rate of car-using evacuees. There are several ways to
estimate car velocity: analyzing the road network capacity against car use demand, using a traffic flow
simulator to analyze the effect of traffic jams, measuring car velocity at a car use evacuation drill, and
so on. The rate of car-using evacuees must be a conceivable rate, not the target rate of a disaster
prevention plan.
5.2.5 Warning effect We (announced tsunami height x cognition rate of the warning)
Warning effect, We, is the equivalent value of the product of announced tsunami height and cognition
rate of warning. In the case of GEJET, it was reported that many people in the area where 3 meters was
announced as the forecast tsunami height by the first warning received this as a sign of safety and some
of them missed the timing of evacuation30). Therefore, 6-meter or 3-meter warning might be a criterion
for people to think about evacuation. However, as the effect of the warning to push people to evacuate
varies with past tsunami experience and announcement history of forecast height in the area, the
effective tsunami height for announcement should be evaluated considering these historical factors.
Cognition rate should not be estimated through analogy of GEJET, because emergency alert emails
and other new IT tools to be issued by governmental agencies to disaster forecast areas have been
introduced. The effect of such tools should be checked on such occasions as disaster drills.
6. CONCLUSIONS
(1) The Human Vulnerability Index (HVI) introduced by this study was verified to be independent of
tsunami height, and to be an index that can express evacuation vulnerability of a studied area being
evaluated by geographical and socio-psychological features.
(2) A multiple regression analysis, which set HVI as a target variable and used four explanatory variables,
allowance period A, preparedness P, road serviceability R and warning effect We, extracted a
regression formula that achieved a multi-correlation coefficient of 0.904 and an adjusted coefficient
of determination of 0.768. The number of fatalities calculated using the regression HVI deviated
from the actual number by only 0.5%.
(3) Sensitivity analysis of the four explanatory variables concerning the number of fatalities indicated
allowance period A as the most effective and road serviceability R as second, if the same variation
rate was applied to one of the four variables. Preparedness P was closely related to wariness over
tsunamis and hence linked with the cognition rate of warnings cr, which is the element variable of
We. Therefore, if these two factors are combined, the effectiveness will be almost the same as that
of A.
(4) Factors indicated by the sensitivities of these explanatory variables have been qualitatively reported
by previous surveys. Nevertheless, by modeling the effects of the variables using HVI, sensitive
factors become clear and the efficiency of countermeasures by improving the factors can be
numerically evaluated.
(5) Issues for the future are to test HVI for other tsunami disasters and improve the reliability of the
regression formula, and to establish a comprehensive evaluation method for explanatory variables
in order to apply HVI to other areas subject to tsunami hazard.
- 17 -
ACKNOWLEDGMENT
The City Bureau of Ministry of Land, Infrastructure, Transportation and Tourism of Japan and the
Center for Spatial Information Science of the University of Tokyo collected significant data regarding
people's evacuation from the Great East Japan Earthquake tsunami and uploaded the data as the Fukkou
Shien Chosa archive. The authors would like to express much thanks and respect for their contributions.
Many appreciations to their effective discussions are given to Professor Hitomi Murakami of
Yamaguchi University, Professor Masayoshi Tanishita of Chuo University, and the members of the
JAEE research committee “Evacuation Research Committee 2012-2016”. Associate Professor Maki
Koyama of Gifu University is appreciated for her kind advice concerning data exploration. The authors
are also grateful to Dr. Raya Muttarak from the International Institute for Applied Systems Analysis
(IIASA) for her kind notification of the importance of HVI’s potential uses.
APPENDIX 1 Outline of Fukkou Shien Chosa archive (FSC archive)
The FSC archive is a GIS database uploaded by the Center for Spatial Information Science of the
University of Tokyo. The original data for the archive is the outcome of the “Survey for Reconstruction
of Damaged Cities suffered by the East Japan Great Tsunami, 2011 (Fukkou Shien Chosa in Japanese)”
conducted by the City Bureau of Ministry of Land, Infrastructure, Transportation and Tourism of Japan.
In line with the contents of the survey report16), the FSC archive web page21), and the paper by Sekimoto,
et al.31), the outline of the archive is described below.
1. The database lists individual persons and business offices in 62 local municipalities (hereafter LMs),
on the coast from Aomori to Chiba Prefectures. The numbers of samples are 10,603 individuals and 985
business offices. In the survey of the individuals, investigators visited shelters, temporary houses and
partially damaged houses, and interviewed people who were affected by the tsunami. The survey term
was from September to December of 2011.
2. The sample rate of the individuals was 1.5% - 3% of over 20 age population in the inundation area of
LMs. The minimum number of samples in an LM was 20, and in reverse, if number exceeded 500, the
sample rate was gradually decreased and the number of samples was limited to around 1,500 at most. In
addition, the affected area was divided into two zones, houses completely collapsed and houses partially
damaged and inundated, and the number of samples was allocated to be proportional to the population
of each area.
For the people in the area where houses had completely collapsed, those in temporary houses were
interviewed, and for the people in the area where houses were partially damaged and inundated, those
in their own houses were interviewed.
The sex and age distribution of samples were targeted to be similar with those of the population in
each LM. However, the actual distribution of samples somewhat deviated from the targets in the LMs
studied. There were fewer 20-39 year old males and females and 40-59 year old males. Conversely,
there were more over 60 year old males and females, and 40-59 year old females.
3. The archive consists of open data and semi-open data, and governmental or research users can access
the latter through a registration procedure. However, for downloading the data, the users are required to
promise to take measures to protect personal information and privacy in future publication of their study
results.
4. The archive contains a GIS data definition document and each LM's past reconstruction plan as well
as each LM's database of many kinds of damage of the 2011 Great East Japan Earthquake. Namely,
inundation area, inundation depth, inundation trace, damage overview, building damage, public
infrastructure damage (river, coast, steep slope, erosion control facility, windbreak storm surge forest,
road, port, sewer, park, and green space), lifeline damage (water and gas), public service damage (bus,
- 18 -
hospital and welfare), cultural asset and educational facility damage, and sufferer and their evacuation
manner data are compiled in the database.
5. Outline of data used in this study:
(1) Inundation area data: Polygon data.
(2) Damaged building data: Polygon data of all buildings in the inundation area with attributes such as
floor area, structure, usage, classification of damage, year build*, residential house or non-residential
house*, adequacy for the use as evacuation points, and inundation depth at building location. (*
means data of some MLs are missing).
(3) Evacuation trip data of individual sufferers: Polyline data of individual evacuation trips with
attributes such as staying time, start time, arrival time, movement method, purpose of trip, trigger of
evacuation start, and tsunami visibility.
(4) Refuge place data of individual sufferers: Point data of refuge place with attributes such as name and
type.
(5) Sufferer data of each administrative area: Polygon data of administrative areas in each municipality
with attributes such as population before disaster, number of households before disaster, number of
deaths, number of missing, number of deceased visitors, and number of decreased households.
However, it should be noted that these data were collected before the end of June of 2011 and a
considerable number of items are missing.
(6) Interview data of individual sufferers: At the top of the evacuation action sub-folder, a table of
interviews of individual sufferers is uploaded. The contents are:
(a) whereabouts of the interviewee at the time of the earthquake,
(b) number of stories of the building where the interviewee was in at the time of the earthquake,
(c) whereabouts of the interviewee's family at the time of the earthquake,
(d) anticipated tsunami coming just after the earthquake or not,
(e) damage of the place where the interviewee was located,
(f) actions the interviewee took after the earthquake,
(g) heard the large tsunami warning or not, heard the height of tsunami forecast or not,
(h) source of the heard warning, impression of warning upon hearing it,
(i) heard evacuation alert from LM or not,
(j) most beneficial source of information in the period from the earthquake to the day's sunset,
(k) intended doing evacuation before arrival of tsunami or not,
(l) evacuation place after the earthquake until sunset of the day, type of evacuation place,
(m) movement method, movement purpose,
(n) trigger for decision to start evacuation,
(o) reason for using a car for evacuation,
(p) problem of road for evacuation,
(q) problem of first evacuation place,
(r) watched hazard map beforehand or not,
(s) saw a board or a sign or a marking that indicated the direction and the place of evacuation, or not,
(t) made preparations for evacuation, such as securing furniture, preparing emergency carry-out bag,
talking with family about tsunami emergency, pre-confirming evacuation place and road, checking
tsunami hazard map, participating in community evacuation drill and so on, or not,
(u) knew the location of the designated place or building for evacuation near the place that was at the
time of the earthquake or not,
(v) was able to go there or not,
(w) sex, age, job of interviewee, number of families living in the same house,
(x) saw the tsunami after the earthquake or not,
(y) damage to interviewee’s house by tsunami or by shaking of the earthquake,
(z) injury to interviewee and his/her family by tsunami or by shaking of the earthquake.
- 19 -
APPENDIX 2 Major trials for selecting explanatory variables (tried about 5 times of this table)
Start
time of
evacua-
tion
Tsunami
arrival
time:t
Evacua-
tion
dist ance
(here
after, ED)
Walking
ED
Car-
using
ED
Equiva-
lent
ED:ed
4)
Allow-
ance
period:
t/ed
5)
Rate of
car-
using-
evacuees:
cr
Car
velocity:
v
Road
service-
ability:
v × cr
6)
Rate of
over 70
age
persons
Tsunami
warning
height :
h
7)
Rate of
antici-
pating
tsunami:
R
h
×
RCogni-
tion rate
of
warning:
cr
Warning
effec t
h × cr
8)
Checked
the
hazard
map or
not
Partici-
pated
evacua-
tion drill
or not
Prepared-
ness
9)
Rate of
returnin g
home +
detour-
ing
0.681 0.357 0.057 0.007 0.056
0.684 0.315 0.168 0.758 0.013 0.063
0.681 0.311 0.069 0.984 0.013 0.066
0.704 0.351 0.338 0.365 0.006 0.047
0.716 0.325 0.327 0.397 0.507 0.019 0.047
0.684 0.316 0.142 0.011 0.754 0.079
0.681 0.311 0.084 0.022 0.111 0.940
0.704 0.351 0.338 0.365 0.006 0.047
0.694 0. 334 0.051 0 .449 0.008 0.026
0.719 0.369 0.036 0.213 0.014 0.069
0.666 0.332 0.029 0.010 0.043
0.717 0. 375 0.027 0 .176 0.038 0.029
0.760 0.456 0.026 0.025 0.036 0.016
0.766 0.470 0.040 0.055 0.086 0.009
0.782 0.501 0.025 0.008 0.063 0.005
0.660 0.323 0.109 0.371 0.007
0.814 0.565 0.008 0.004 0.036 0.004
0.730 0.440 0.060 0.006 0.009
0.819 0.544 0.076 0.004 0.575 0.037 0.004
0.825 0.557 0.099 0.004 0.032 0. 408 0.007
1
0) 0.814 0.532 0.971 0.044 0.007 0.059 0.005
0.817 0.539 0.013 0.665 0.083 0.051 0.005
0.760 0.456 0.026 0.025 0.036 0.016
0.797 0.530 0.015 0.004 0.068 0.013
0.644 0.248 0.415 0.049 0.872 0.052
0.730 0.440 0.060 0.006 0.009
0.744 0.426 0.003 0.007 0.176 0.061
0.650 0.306 0.009 0.025 0.122
0.793 0.523 0.003 0.003 0.127 0.031
0.748 0.471 0.008 0.003 0.012
0.744 0.425 0.012 0.038 0.544 0.041
0.804 0.545 0.002 0.003 0.087 0.009
0.748 0.433 0.023 0.015 0.461 0.041
0.842 0.596 0.040 0.011 0.021 0.126 0.003
0.842 0.596 0.139 0.002 0.023 0.125 0.003
0.810 0.523 0.108 0.005 0.062 0.605 0.007
0.805 0.513 0.077 0.005 0.080 0.010 0.950
0.748 0.433 0.011 0.051 0.534 0.048
0.739 0.456 0.011 0.021 0.037
0.799 0.534 0.042 0.008 0.082 0.009
0.831 0.571 0.036 0.023 0.036 0.162 0.006
0.803 0.568 0.013 0.020 0.006
0.822 0.577 0.010 0.065 0.280 0.006
0.838 0.603 0.078 0.010 0.509 0.004
0.831 0.619 0.031 0.008 0.003
0.850 0.631 0.024 0.027 0.258 0.003
0.861 0.656 0.057 0.004 0.148 0.009
0.836 0.599 0.009 0.009 0.004 0.558
0.851 0.673 0.010 0.007 0.000
0.867 0.686 0.006 0.012 0.218 0.000
0.869 0. 691 0.006 0.010 0.189 0.000
0.860 0.669 0.010 0.009 0.380 0.001
0.823 0. 617 0.001 0.002 11) 0.001
0.837 0.621 0.001 0.015 0.064 0.050
0.895 0.747 0.000 0.003 0.002 0.001
0.897 0.753 0.000 0.001 0.008 0.000
0.874 0.700 0.000 0.002 0.034 0.018
0.797 0.566 0.002 0.006 0.131
0.904 0. 768 0.000 0.002 0.004 0.003
0.904 0.752 0.000 0.004 0.886 0.006 0.004
0.904 0. 752 0.000 0.003 0.843 0.009 0.027
1
2) 0.827 0.599 0.001 12) 0.151 0.017 0.044
Sign ific an ce le ve l p of e xp lana to ry va ria ble
(Multip le regres sion a nalysis was ap plied to the s et of va riables of which ce lls a re fille d b y p va lues ).
R
Corre-
lation
coefficient
R2
Adjusted
determi-
nation
coefficien t
1) An alyze d
using the data
of 19 local
mun icipa litie s
(here after LMs)
of Iwate and
Miyagi.
Regression
equation of
po lyn omial
terms was
ap plied .
Th e fo llowin g 2)
and 3), same
type of
po lyn omial
terms was
ap plied .
2) An alyze d
using the data
of 17 LMs of
Iwate and
Miyagi.
3) 20 LMs of
Iwate, Miyagi
and Fukushima.
Analyzed using
the data of 20
LMs o f I wat e,
Miyagi and
Fukus hima.
Monomial of
exponential
terms was
ap plied .
1) The 19 LMs are Miyako, Yamada, Otsuchi, Kamaishi, Ofunato, Rikuzentakata, Kesennuma, Minamisanriku, Onagawa, Ishinomaki (rias coast area),
Ishinomaki (flatland area), Higashimatsushima, Shichigahama, Miyagino-ku of Sendai, Wakabayashi-ku of Sendai, Natori, Iwanuma, Watari and Yamamoto.
2) Miyagino-ku and Iwanuma were omitted because of their large deviation of age distribution from Census. (Data of under 49 age of Miyagino-ku was 19%,
whereas Census in the area was 57%. Data of over 70 age of Iwanuma was 5%, whereas Census was 18%.)
3) Shinchi, Minamisoma and Iwaki of Fukushima were added, whereas Soma of Fukushima was excluded because its preparation rate of emergency carry-out-
bag was unnaturally low. (Preparation rate of emergency carry-out bag of Soma was 5.1%, while the rate of 20 LMs was 37.2% in average.)
4) Equivalent ED = Average walking ED × Rate of walking evacuee+Average car ED × Rate of car-using evacuee×0.244. (Here, ED is the distance of road
from evacuee's home to the boundary of inundation area.)
5) Allowance period = Tsunami arrival time/Equivalent ED
6) Road serviceability = Rate of car-using evacuees × Car velocity (The better the road, the higher the car velocity and the higher the rate of car users.)
7) Tsunami forecast height broadcast by the first tsunami warning (Iwate 3m, Miyagi 6m, Fukushima 3m)
8) Warning effect = Tsunami forecast height × Cognition rate of tsunami warning
9) Preparedness= Rate of persons who prepared emergency take-out bag beforehand
10) Hereafter，HVI was modified by applying the rate of persons in their houses, as denoted by Eq. (2).
11) Hereafter, when calculating the preparedness rate, the denominator was replaced by the number of persons whose houses were completely collapsed to the
number of persons who were at home or returned home before the tsunami’s arrival and concurrently whose houses were completely collapsed.
12) Road serviceability was replaced by a dummy variable that defined the rias coast region (north of Oshika peninsula) as 0 and the flat coast region (southwest
of Oshika peninsula) as 1．
- 20 -
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- 22 -
