Journal of Natural Disaster Science, Volume 33, Number 1, 2012, pp37-48

Prediction of Refugee Population in School Districts
Chunhao SHAO*
Takeyoshi TANAKA***Graduate School of Engineering, Kyoto University**Disaster Prevention Research Institute, Kyoto University(Received May 31, 2011 Accepted December 10, 2011)
ABSTRACTThis study developed a numerical formula based on statistical data on collapsed, burnt
down buildings, and refugees taking shelter in schools during the Great Hanshin Earthquakeof 1995. The data on building damage used in the study were 2004 municipalities, classified
into 46 junior high school districts. The independent variables were collapsed buildings,
burnt down buildings, and average population of each building. The refugee population
was regarded as a dependent variable in this study. The formula effectiveness in the juniorhigh school district was 63% and 98% in administrative wards. When predicting the refugee
population in schools in Kobe City using the formula, the school refugee population was
80% of all refugees in Kobe City predicted by the Hyogo Prefectural Government. Thisoutcome was the same percentage of the population in school refuges in the Great Hanshin
Earthquake. Predicting the refugee population who took shelter in other places besides
school refuges and controlling other effective intermediary variables to fit different local
areas are issues worth pursuing in advanced research.
Keyword: School refuge, School district, Refugee prediction, Collapsed building, Burnt
down building

1. INTRODUCTION

who took shelter in school refuges composed 60% of
all refugees in Kobe City during the Great Hanshin
Earthquake. Based on the phenomenon, it is important to grasp the population in school refuges in city
planning for disaster prevention in urban areas. By
correctly predicting the population in school refuges,
relief supplies and rescue actions taken by official
institutions responsible and NPOs could become
more effective and appropriate. Therefore, this study
correctly predicts the refugee population in school
refuges by collapsed and burnt down buildings, andthe population of collapsed and burnt down buildings,
to provide a positive suggestion in predicting refugee
population as a reference for policy-making in disas-

The Great Hanshin Earthquake, with a seismic
intensity of seven, occurred on January 17, 1995
at 5:46 AM. The range of the earthquake included
Kobe City and Awaji Island. Over 50% of the zones
destroyed were urban areas with high-density populations, and people in large numbers therefore took
shelter in streets and buildings. As a result, mostpeople went to refuges designated by the local government, and most refuges in Kobe City were nearby
school refuges including elementary schools, highschools, etc. According to the investigation outcome
conducted by Yokota (1998), the refugee population
37

C. SHAO, T. TANAKA

Here, PE is the total refugee population, PEW is the
refugee population caused by damaged wooden buildings, PER is the refugee population caused by damaged
non-wooden buildings, and PEL is the refugee population caused by lifeline damage.
(4) Kawasaki and Nagahashi (1996) utilized the damage rate of wooden buildings to predict the rate ofrefuge. The seismic intensity of an earthquake wascontrolled to two types: the M5 and the M7 level; therefugee population was counted by the 3-m2 unit ofspace occupied by each person in a refuge. The study
only considered damage to wooden buildings, and did
not include other possible variables in the prediction.
The equation for the rate of taking shelter is as expressed below.

ter prevention and future urban planning.
2. LITERATURE REVIEW
Studies related to predicting population in school
refuges are few. Several important studies cited here
describe research conducted around this issue.
(1) Kimura and Hayashi et al., in the report, “Prediction of Earthquake in Kyoto City for the Third Time”(2009) and “Development of a Method of Estimating
the Number of People at Evacuation Centers in Caseof Urban Earthquake Disaster” (2004), predicted
population change at refuges over ten hours by the independent variable of seismic intensity. The formula
was expressed as:
y10 = (0.0863x2 ‒ 0.7935x + 1.8108) × population. (1)RP = (BC + 0.5

Here, y10 is rate of refugees taking shelter at the 10thhour after the earthquake, and x is seismic intensity.
The study was conducted for the entire range of
Kyoto City, and regarded seismic intensity as an independent variable to predict the entire refugee population in the city.
(2) Sakata (1999) used the data of every administrative ward during a 10-day period to 50 days after theoccurrence of the Great Hanshin Earthquake, andregarded the decreased population in school refuges
as an independent variable for building a model to explain the change in the school refugee population. The
formula was expressed as:
Y = a · EXP (bx).

BP) ÷ BW.

(4)

Here, RP is the rate of people who should take shelterin refuge, BC is the number of completely collapsedbuildings, BH is the number of half-collapsed buildings, BP is the number of partly collapsed buildings,
and BW is the number of undamaged whole buildings.
The literature does not include any studies focusing on prediction of the refugee population in school
institutions in each school district. The independent
variables used to predict the refugee population were
the seismic intensity of the earthquake, collapse rate,
rate of damage to lifelines, and change in the refugee
population. The prediction objects ranged from administrative wards to the entire city or special areas.
Compared to past studies, this study chose the basic
unit of “school district” in city disaster prevention
as the predicted object of official school refuges. To
grasp the relationship between residents of damagedbuildings and the refugee population, the populationsof collapsed and burnt down buildings were regarded
as independent variables. The purpose of this study
was not only to predict the refugee population of aschool refuge in a broad area of each ward or the entire city, but also to grasp information on small areas
such as one school district.

(2)

Here, a is a constant, b is rate of decrease in the refugee population, Y is the change in refugee population,and X is the number of days after the earthquake.
(3) In the Mieno study (1999), every administrative ward in Fukuoka City was taken as the research
object, and the status of refugee house damage wasclassified into three types: collapse of wooden buildings, collapse of non-wooden buildings, and damage
to the water supply system. The independent variables
were the rate of completely collapsed buildings, fire
spread, and damage to the water supply system. The
outcome still did not approach the area of school
districts. The equation used in the study is expressed
below:PE = PEW + PER + PEL.

BH + 0.1

3. METHODOLOGY
The methods used in this study are based on theliterature and statistic analysis. The former composes
the main database, and the latter is the analysis process. These methods are explained in detail below.

(3)38

PREDICTION OF REFUGEE POPULATION IN SCHOOL DISTRICTS

3.1 Literature analysis
This study used quantification of literature
analysis as a research method. The building damage
data used in this study were based on the document,“Final Report on the South Earthquake in Hyogo
County,” published by the Building Research Instituteof the Ministry of Construction in 1998. Statistics on
refugee population at the peak time in each school
refuge on January 25, 1995 are presented in a reportpublished by the Education Committee in Kobe Cityand in the research of Kashihara (1998). Household
and population statistics are provided in the city census published by the General Affairs Planning Department of Kobe City Hall (1995). The data mentioned
above were collected for the statistical analysis.
The data process steps are described below.
(1) First, building collapse and destruction by fire
were sorted for every municipality in the school
district. The 2004 municipalities were classified
into 46 junior high school districts, which also
included elementary school districts. School district classification was based on information provided by the Kobe Education Committee. School
district distribution was according to damaged
municipalities, as shown in Fig. 1. The number
in each municipality indicates the junior highschools belonging to that municipality.
(2) To understand the status of damaged buildings
and total refugee population in each school
district, statistics on various types of damagedbuildings and peak population on January 25,
1995 in every school were calculated according
to the school district. Table 1 lists the manipulated data.
3.2 Process of statistical analysis
The method utilized in this study arranges roughdata through “regression analysis.” This method waschosen because regression built for prediction takes
into account the percentage of factors chosen as independent variables (population of damaged buildings) and explains the dependant variable (refugee
population). The prediction quantity can predict relief
supplies and other rescue actions needed when making policies related to disaster prevention and urban
planning. In this study, the dependent variable was the
refugee population in school refuges in junior highschool districts, and the independent variables were
the number of completely collapsed buildings, number of burnt down buildings, and average populationof buildings in the school district.

Fig. 1. Distribution of school districts in damaged municipality units
39

C. SHAO, T. TANAKA

Table 1. Data on damaged buildings and refugee population in school districts

Note:
CCB: Number of completely collapsed buildings in the school district
HCB: Number of half-collapsed buildings in the school district
BB: Number of burnt down buildings in the school district
BB Mid-R: Report on middle by the Construction Ministry
M.H-rise: Middle- and high-rise buildings

40

PREDICTION OF REFUGEE POPULATION IN SCHOOL DISTRICTS

Table 2. Pearson product moment correlation among variables in the study

Variable
Pearson
Product-Moment
Correlation(r coefficient)

PopulationofRefugeesin School
Refuges(yg)

Number ofCompletely
Collapsed
Buildings(xc)

Numberof Burnt
Down
Buildings(xf)

Numberof
Buildings(xb)

Populationin theSchoolDistrict
(xp)

yg

1.000

.830

.317

.497

.090

xc

.830

1.000

.420

.540

.085

xf

.317

.420

1.000

.107

-.219

xb

.497

.540

.107

1.000

.681

xp

.090

.085

-.219

.681

1.000

To prove the relationship between the independent and dependent variables, the Pearson productmoment correlation (r coefficient) was used to understand the basic relationship among variables in the
study. Table 2 shows a positive relationship between
the most independent and dependent variables. The
strength of relationship from high to low is the number of collapsed buildings, the number of buildings inschool districts, the number of burnt down buildings,
and the school district populations. The r coefficientamong independent variables is low and middling(<.5). When performing regression analysis, the correlation among independent variables is expected to
be low and that between the independent and dependent variables is expected to be higher and therefore
better.
The regression analysis function is expressed asa simple numerical formula as:b=f (x) = y^ = ax+b,(5)n Σ xy ‒ Σ x Σ y, andn Σ x2 ‒ ( Σ x ) 2(6)Σy ‒ bΣx
.
n

(7)

a=

when using x as the independent variable, should betaken into account. The R2 formula is expressed as:

2

R=

(

Σ xy
Σ x2 Σ y 2

)

2

.

(8)

When R2 is higher, the regression effectiveness isbetter. The following introduces the formulas used forprediction in this study.
(1) Numerical formula for population prediction of
refugees in school refuges
The formula of equation (9) listed below is afunction of population prediction for refugees inschool refuges in school districts.

(

)

(

)

| yg | = f xc, xf, xp = 695 + 1.7xc + 0.9xf xp .
‒
‒
b
b

(9)
Here,
yg is the population of refugees in school refuges inschool districts.
xc is the number of collapsed buildings in school
districts.
xf is the number of burnt down buildings in school
districts.
xp/b is the average population of buildings in school
districts.
The scatter plot shown in Fig. 2 is the distribution of samples in the quadrant constructed by threevariables and the plane standards for the regression
equation. The direction of plot distribution is thesame for the plane and shows a positive relationship.
The coefficient of determination (R2) in the formula
is 0.654, which means that the independent variables
can explain 65% of the dependent variables. To confirm that no interaction exists among independent

In the basic regression formula, y is the dependent variable, x is the independent variable with a,which is the interception of y, and b is the slope of theregression line.
When considering regression effectiveness, R2or the so-called coefficient of determination, which isthe square of the Pearson product-moment correlation
coefficient (r) and shows the ratio of explanation to y
41

C. SHAO, T. TANAKA

Fig. 2. Scatter plot for yg, xxp/b, and xfxp/b
played an important role in the study. All school districts were divided into two types, one being districts
with no burnt down buildings, and the other being
districts with burnt down buildings. A comparison of
the effectiveness of prediction for the refugee population by the number of collapsed buildings is shown inthe scatter plot of Fig. 3.
In Fig. 3, the number of genuinely collapsedbuildings has a positive correlation with school district. This relationship is manifest in school districts
with both burnt down buildings and no burnt downbuildings. R2 in the former is 0.628, and in the latter,
it is 0.605. Therefore, the prediction of “collapsedbuildings” in burnt down districts is a little higher
than in non-burnt down districts. In this study, theprediction effectiveness (R2) with the contribution ofburnt down buildings is near to 0.0539, and this value
is close to the difference in R2 between collapsedbuildings in these two kinds of school districts shownin Fig. 3. In other words, the “number of collapsedbuildings” has a strong statistical predictive power forboth kinds of school district, and the “number of burnt
down buildings” can boost the prediction effectiveness of collapsed buildings in places of conflagration
occurrence.

variables, which is also a multicollinearity problem,
the VIF value should be less than 10. The VIF value
for the number of collapsed buildings was 1.058, forthe number of burnt down buildings, it was 1.215, andfor the average population of buildings, it was 1.087.
In the equation, there was no multicollinearity problem among the three variables.
In addition to the equation for school districts, tomatch data based on administration ward, a formulafor administration wards was also developed:
ya =

n

Σy.
i =1

g

(10)

Here,
ya is the population of refugees in school refuges ineach ward.
yg is the population of refugees in school refuges injunior high school districts.
n is the number of school districts in each ward.
R2 of regression for administrational wards is70%, which means that the independent variables can
explain 70% of the dependent variables. The formula
will be used in the fourth part of this article to compare the population prediction of refugees in entire
Kobe City provided by Hyogo Prefecture.
(2) Influence of burnt down buildings on refugee
population prediction
From R2 stated above, we find that the populationof collapsed buildings has higher prediction effectiveness for the refugee population than the burnt downbuilding population. However, burnt down buildings
42

PREDICTION OF REFUGEE POPULATION IN SCHOOL DISTRICTS

Fig. 3. Distribution of collapsed buildings and refugee population in two types of areas
4. COMPARING PREDICTION OUTCOMESBETWEEN THE STUDY AND OFFICIAL DATA

ings in each ward were distributed by the percentage
in each ward during the Great Hanshin Earthquake.
The average building population was also calculated
in the same way. Figure 4 shows a comparison of the
refugee population between this study and the official
data in each ward.
On the other hand, when making prediction
based on the formula in this study, the population of
refugees in school refuges in school districts of Kobe
City was 104,375. Compared to population of refugees in entire Kobe City predicted by Hyogo Prefecture, there was a difference of 23,861 people. As described above, the prediction given by the formula inthis study was 104,375 people, which is 80% of Hyogo Prefecture’s assumption, 128,236 people. The reason is that the population of refugees in this study was
focused on people staying in school refuges. In otherwords, people who take shelter in other places, suchas parks, homes of relatives, and community centers,
were not included when making the prediction. However, the percentage of refugee population in school
refuges during the Great Hanshin Earthquake wasover 60% of the refugee population in entire Kobe
City. The percentage is close to the prediction in thisstudy when compared to the refugee population inentire Kobe City predicted by the Hyogo Prefectural
Government.

This section makes a prediction based on theformula in this study for current-day Kobe City, andthen compares this prediction with the prediction by
the Hyogo Prefectural Government based on the assumption that the Rokko-Awajishima fault caused theearthquake. The numerical formula used in this section is the prediction for administrative wards, such as
equation (10) given in the third part of the article.
ya = Σ ni = 1 yg .
(10)
Similar to the information shown in Table 3, according to the prediction by the Hyogo Prefectural
Government on the assumption that the Rokko-Awajishima fault caused the earthquake, the number ofcollapsed buildings might be 60,322, and the numberof burnt down buildings might be 21,455 during thetime interval from 4:00 AM to 5:00 AM during thewinter season. The potential refugee population in
Kobe City might be 128,236. Because the Hyogo Prefectural Government did not state the potential refugee population or building damage based on each administrative ward, the refugee population, number ofcollapsed buildings, and number of burnt down build43

C. SHAO, T. TANAKA

When comparing each administrative ward, there
is a large difference between this study and prediction
offices of each ward for Nagata and Tarumi Wards.
The difference reflects the fact that the conflagrationin these two wards would have influenced the predic-

tion in this study, because burnt down buildings havea negative relationship with the refugee population inthis study. For this reason, the prediction made in thisstudy is lower for both Nagata and Tarumi Wards.

Table 3. Prediction of collapsed buildings, burnt down buildings, and refugee population in
every ward of Kobe City
Ward

C.B

B.B

P.B

P.R

P.R (H.P)

Higashinada

12,246

1,007

1.43

15,75035,043Nada

11,414

1,432

1.06

16,173

20,206

Hyogo

8,529

2,896

1.11

17,615

15,183

Nagata13,887

14,6601.3

51,31820,356

Suma

6,886

1,254

0.68

12,217

12,162

Tarumi

1,052

3

2.83

4,553

3,598

Kita

242

3

5.92

3,925

1,356

Chuo

5,676

200

1.35

8,631

19,305

Nishi

390

0

1.88

4,032

1,027

Kobe City

60,322

21,455

1.95

104,375

128,236

Note.
C.B: Number of collapsed buildings in the prediction
B.B: Number of burnt down buildings in the prediction
P. B: Population of damaged buildings on average in the prediction
P. R: Population of refugees in the prediction of this study
P. R (H.P): Population of refugees in the prediction based on the data of Hyogo Prefecture

Fig. 4. Comparison of refugee populations in wards
44

PREDICTION OF REFUGEE POPULATION IN SCHOOL DISTRICTS

tion in the same district. However, in the Great Hanshin Earthquake, refugees who took shelter in schools
are not only from the same district; some of them arefrom other school districts and wards. For this reason, when prediction of whole Kobe City conductedby Hyogo Prefecture is multiplied by the percentage(27%) of the refugee population in Higashinada in
the Great Hanshin Earthquake, the predicted refugee
population will be much larger than that predicted bythis study. The difference of 19,293 refugees implies
that they are from other wards or those taking shelter
not because of collapsed or burnt down houses but
because, for example, of damage to the water supply
system or emotional factors.

To confirm whether the difference between thisstudy’s prediction and that of Hyogo Prefecture isstatistically significant or not, a t-test was conducted.
The t-test formula is expressed as:ta =ya ‒ μa
sa
na
.

(12)

Here,
ta: is the t value of the refugee population in school
refuges in wards.
ya : is the mean refugee population in school refuges
in wards.
μa: is the mean refugee population in Kobe City.
Sa is the standard deviation of refugee population inschool refuges in wards.
na is the number of wards.
When conducting the t-test, the null hypothesis is
set with no difference between the prediction by thisstudy and that by prediction offices. The t-value is
4.07 with a probability of 0.004 if the null hypothesis
is rejected by mistake. Because the significance level
is less than 0.01, the prediction in this study showeda significant difference from the entire populationof refugees in Kobe City, and a random error with aprobability of only 0.4% could hardly result in overestimation. The probability of error is so small thatthe 80% prediction of refugee population in school
refuges in school districts of all refugees in Kobe City
can be supported by the confidence interval.
Furthermore, the prediction of refugee population in three wards shows a large difference (morethan twice) from the prediction of Hyogo Prefecture.
These wards are Higashinada, Nagata, and Chuo
Wards. In order to explain the reason for the difference, we will give an illustration of these three wards
from the viewpoint of data used for prediction.

(2) Nagata Ward
In Nagata Ward, the refugee population predictedby this study is 51,318 people, with 20,356 people
predicted by Hyogo Prefecture in entire Kobe City,
which is a multiplication of 16%; this is the percentage of the refugee population in Nagata Ward in the
Great Hanshin Earthquake. The difference between
these two predictions is 30,962 people, and the prediction of this study is larger than Hyogo Prefecture’s.
The reason is that the number of burnt down buildings (6,814) in Nagata Ward in the Great Hanshin
Earthquake was greater than in other wards, andmany residents died or were injured by conflagrationof their houses. In the study, we use the number ofcollapsed buildings and number of burnt down buildings to predict the refugee population; the greater thenumber of damaged buildings, the greater the refugee
population. However, deceased residents and people
who cannot evacuate are not calculated in equation(9). As a result, when the prediction of entire Kobe
City conducted by Hyogo Prefecture is multiplied by
16% in the Great Hanshin Earthquake, the prediction
was much lower than the prediction of this study. The
percentage shows the living refugee population, but
the prediction of this study only shows the people
who might take the shelter because their houses haveburnt down or collapsed. Furthermore, the statistics
on damaged buildings in Nagata Ward may have suffered double counting of burnt down and collapsedbuildings. The data on building damage in Nagata
Ward should be revised again based on other reliable
resources, besides the data of the Building ResearchInstitute.

(1) Higashinada Ward
In Higashinada Ward, the potential refugee
population predicted by this study is 15,750 people,
with a refugee population of 35,043 as predicted by
Hyogo Prefecture in entire Kobe City, which is amultiplication of 27% the percentage of the refugee
population in Higashinada Ward in the Great Hanshin
Earthquake. The difference between these two predictions is 19,293 people. The reason for the differenceis that we only use data on collapsed buildings andburnt down buildings to predict the refugee popula45

C. SHAO, T. TANAKA(3) Chuo Ward
The refugee population predicted by this study
is 8,631 people, with 19,305 refugees predicted by
Hyogo Prefecture in entire Kobe City, which is a multiplication of 15.8%, or the percentage of the refugee
population in Chuo Ward in the Great Hanshin Earthquake. The case of Chuo Ward is like Higashinada
Ward, the extreme difference being a result of refugees from other school districts or wards. Moreover,there are refugees who evacuate for other reasons, besides collapsed and burnt down houses. In particular,
there were many downtown refuge sites in Chuo Wardduring the period of the Great Hanshin Earthquake
accommodating many refugees from other wards.
The difference of 10,674 people shows the refugee
population from other wards and evacuation for otherreasons than collapsed and burnt down houses.

Earthquake. For this reason, there was an irregulardifference in prediction between this study and that ofthe prediction office in each ward. Continuous exploration of further information about the prediction ofeach ward by other offices or other institutions would
enable a comparison with the prediction of this study
for a more accurate outcome.
Based on the above findings, the contribution ofthis study encompasses several points. First, the prediction of refugee population in this study can include
school districts and administrative wards with significant statistical levels. The equation formula shows
the influence of damaged buildings on the refugee
population, which provides more detail than using theseismic intensity of an earthquake as an independent
variable. Finally, the prediction of school refugee
population enables an accurate calculation of essential
relief supply and the adoption of more effective rescue action as a basic reference for policy-making indisaster prevention and urban planning.
Future research would be to investigate how to
predict the refugee population among those who do
not take shelter in school refuges and to improve theprediction effectiveness. Controlling the influence ofintermediary variables, such as refugees from otherschool districts and fatality count, could apply to predictions made by other cities. In the Great East Japan
Earthquake on March 11, 2011, besides schools, otherrefuge sites also played an important role as refugeeaccommodation and supply distribution.
Based on the survey conducted by the Cabinet
Office of the Japan Government and the Reconstruction Headquarters in Response to the Great East Japan Earthquake on June 2nd and September 22nd, therefugees in schools and public halls in Fukushima,
Miyagi, Iwate, and other prefectures damaged in
the Earthquake were 41,143 people on June 2nd and2,840 people on September 22nd. The refugees taking
shelter in hotels were 28,014 people on June 2nd and4,337 people on September 22nd. Those taking refuge
at relatives’ or friends’ houses were 32,483 on June
2nd and 17,683 on September 22nd. Refugees living in
temporary housing, hospitals, and other prefabricatedhouses were 22,954 people on June 2nd and 2,840 people on September 22nd. From the whole image of thechange in refugee population in different refuge sites,
compared to the Great Hanshin Earthquake, because
many schools and designated refuge sites were de-

CONCLUSION
In this study, the prediction effectiveness forschool districts is 65% and 70% for the administrative
wards. When considering the prediction effectivenessbetween individual variables, the number of collapsedbuildings was a powerful factor in predicting refugee
population, but in school districts with conflagration,
it was powerless to predict the refugee population.
This is because the death of residents caused by conflagration as well there being refugees from otherschool districts influenced the relationship between
the independent and the dependent variables.
Regarding application of the formula, this study
also conducted a comparison of prediction outcomesfrom this study and those from the Hyogo Prefectural
Government. The prediction of refugee populationin this study was 80% (104,375 people) of the refugee population (128,236 people) in entire Kobe City
predicted by Hyogo Prefecture. According to the ttest, the difference approached statistical significance.
Therefore, the prediction in this study was proven,and the percentage was also supported by the dataon the Great Hanshin Earthquake. An irregular difference was found between individual wards in the
refugee population. This was because the prediction
by Hyogo Prefecture was for entire Kobe City, but notfor each ward. The prediction for each ward from theprediction office was based on the percentage of the
refugee population of each ward in the Great Hanshin
46

PREDICTION OF REFUGEE POPULATION IN SCHOOL DISTRICTS

stroyed by the tsunami caused by the earthquake, therefugee population taking shelter in hotels is greaterthan in the Great East Hanshin Earthquake. Moreover,the population in temporary housing, hospitals, andother prefabricated houses continued increasing from
June to September. The phenomenon indicates thatrefugees selected temporary housing and other public
prefabricated houses to live in for middle- and longterm refuge.
The data on the Great East Japan Earthquake
described above should be regarded as an importantreference and adjusted to the equation of the formuladeveloped in the study. Finally, building a prediction
model to fit not only west Japan, but also northeast
Japan in urban areas, rural areas, and harbor villages
and providing suggestions for the planning of disasterprevention in different areas are essential in future research.

homepage, 1995, <www.city.kobe.lg.jp> accessed on
January 15, 2011 (in Japanese).
Hyogo Prefecture Homepage, 2006, <www.pref.hyogo.jp>
accessed on January 15, 2011.
Kashihara, S., Ueno, J., Morita, T. & Yokoda, T., 1998,
Study on Refuges in the Great Hanshin Earthquake,
265-283, Osaka University Press, Osaka (in Japanese).
Kawasaki, J. and Narashino, S., 1996, A study on the Presumption of Refugee Population and Capacity of Refuge Places in the Earthquake Disaster: A Case Study
for the Tsudanuma Area, Nawashino City, Chiba Prefecture, Summary Report of the Architectural Instituteof Japan, 89-90 (in Japanese).
Kimura, R., Hayashi, H., Tatsuki, S. & Tamura, K., 2004,
Development of a Method of Estimating the Numberof People at Evacuation Centers in Case of Urban
Earthquake Disaster, 13th World Conference on Earthquake Engineering, Paper No. 1306.
Kobe Education Committee, 1998, Reconstruction of Dam-

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City for the Third Time, 38-55 (in Japanese).

havior of Refugees, pp. 35-66, Osaka University Press,

General Affairs Planning Department of Kobe City Hall

Osaka (in Japanese).

47