• 沒有找到結果。

Development of An After Earthquake Disaster Shelter Evaluation Model

N/A
N/A
Protected

Academic year: 2021

Share "Development of An After Earthquake Disaster Shelter Evaluation Model"

Copied!
6
0
0

加載中.... (立即查看全文)

全文

(1)

DEVELOPMENT OF AN AFTER EARTHQUAKE DISASTER

SHELTER EVALUATION MODEL

Shen-Wen Chien1, Liang-Chun Chen2*, Shin-Yi Chang3, Ging-Hsiang Chiu1, and Ching-Lin Chu3

1

Department of Fire Science and Administration Central Police University

Taoyuan, Taiwan 333, R.O.C. 2

Graduate Institute of Building and Planning National Taiwan University

Taipei, Taiwan 106, R.O.C. 3

Office of the National Science & Technology Program for Hazards Mitigation

Taipei, Taiwan 106, R.O.C.

Key Words: short-term shelter, evacuation inclinations, evacuation

behavior, earthquake disaster

ABSTRACT

The research was carried out in two regions: the Chia-Yi area and Taipei City, investigating the needs of the local populations during earthquake evacuation. Based on real-life earthquake experience and hypothetical settings, with the use of questionnaires, this research probes into characteristics of evacuation behavior of the populace, and the research concentrates, as well, on parameters (age, income, education, rented or self-owned domicile), which influence the popu-lace in selecting a short-term shelter, using estimate modules contained in Haz-Taiwan.

*Correspondence addressee

I. INTRODUCTION

In major earthquakes, the effective distribution and planning of shelters not only can ensure the per-sonal safety of victims while escaping calamity, but also can ease economic pressure on the government in the follow-up recovery phase. In the past, most urban disaster prevention research focused on the gov-ernments’ subjective evaluations of what the govern-ment could supply and how they could supply it and ignored related problems of demand and selection of shelters based on citizens’ evacuation behavior in calamities. Based on the structure of Haz-Taiwan software, this research aims to gain a better under-standing of these characteristics, to obtain the

decisive parameters of evacuation behavior of resi-dents in a model community, and to propose a shel-ter-selecting formula as a base for building up the Haz-Taiwan shelters supply and demand module software.

II. RESEARCH METHODS AND QUESTIONNAIRE STRUCTURE

This research focuses on the greater Chia-Yi area and Taipei City, investigating the needs of local popu-lations during earthquake evacuation. Based on real-life earthquake experience and hypothetical settings, with the use of questionnaires, this research probes into characteristics of evacuation behavior of the

(2)

populations, in the 2 areas. This research concen-trates on the parameters (age, income, education, rented or self-owned domicile), which influence the populace in selecting short-term shelters, using the estimate modules, contained in Haz-Taiwan. The structure of the questionnaire is shown in Fig. 1. III. ANALYSIS OF INVESTIGATION RESULTS

These surveys were conducted within 6 months of the 921 Chi-Chi Earthquake. The numbers of us-able questionnaires from Chia-Yi and from Taipei City were 353 and 180 respectively. The question-naires focused on evacuation behavior of the popu-lace under the following three conditions: ground-shaking, post-quake reaction, and collapse of buildings. Related sections applying to Haz-Taiwan (Sun and Wang, 1999) are discussed as below: 1. The Reactions of People during Ground

Shak-ing

During an earthquake, the percentages of the population’s evacuation behavior are as follows: stay-ing in the original place and waitstay-ing for the endstay-ing of the earthquake (27%), running into living room or other open spaces in the house (18%), running toward a pillar or other more solid places (16%), hiding be-side desks, beds or other more rigid objects (13%), or hiding under desks or beds (9%).

Distinguishing behavior patterns at the moment of an earthquake, three categories could emerge as follows: active evacuation behavior, passive evacua-tion behavior and collective evacuaevacua-tion behavior. As a result of this research, we found about 50% of the populace will actively evacuate.

2. The Reactions of People, Post-Quake

People’s evacuation reactions include leaving for other places together with family (36%), going to turn off the gas and electricity (19%), running to open fields or streets (19%), staying in the original place and continuing to do the same thing as usual (11%), calling relatives (7%), listening to the radio or tele-vision (6%), and perceiving no earthquake or others (3%).

3. The Inclination to Go to a Sanctuary when Domicile has been Damaged during Earthquake When domicile has been damaged during earthquake, the percentages going to various kinds of sanctuaries for temporary shelter are as follows: sleeping out or making tents in nearby open fields, parks or school playgrounds (35%), seeking support from relatives and friends (25%), and proceeding to various edifices such as community centers, schools, district offices, temples or churches for temporary shelter (20%), and others (4%).

4. Factors Influencing when People Select Shelters Deducting the 25% of the evacuee population choosing to live with relatives or friends, 35% of the evacuee population chooses to sleep out or make tents in open fields, parks or school playgrounds. The re-sults of this investigation provide the following explanations: being anxious about following earth-quakes and convinced that staying in the house is unsafe (45%), being familiar with the surroundings (25%), and knowing the nearby residents and it First-time Reaction/Follow-up/Evacuation Reaction

(3)

being easier to seek help (21%).

20% of the evacuee population chooses to settle down in edifices; among them, 84% would go to pub-lic shelters, and the rest 15% prefer private loci.

The rest, 16% of the evacuee population, choose to sleep out or put up tents in alleys or streets near domiciles. The following factors will help explain why a specific location is chosen: being familiar with the surroundings (48%), being anxious about after-shocks (33%), knowing the nearby residents (15%), concern about their property in the domiciles (2%) and others (2%).

Comparing the two behavior patterns, evacua-tion to the nearby streets and to the open fields, three main factors could be identified: being worried about following earthquakes and the unwillingness to stay in edifices, being familiar with nearby surroundings, and knowing the nearby residents. As to choosing evacuation behavior, the main difference is the fa-miliarity of the surroundings. This is the reason why t h e r e s i d e n t s w i l l c h o o s e t h e s e a r e a s d u r i n g earthquake. Consequently, whether the residents know where the nearest park is, whether there is any large-scale open area that can provide shelters, and whether the residents are aware of the correct disas-ter response, all are confusing variables that will in-fluence people in choosing the way to evacuate to large-scale open fields or to nearby streets. As a result, any quick conclusion concerning the evacua-tion behavior pattern of Taiwanese people should not be made. Further studies should be conducted to find out the potential reasons that can explain why these people choose to evacuate to nearby streets.

5. The Season’s Influence on Populaces’ Evacua-tion Behavior

In northern Taiwan the winter weather is wet and cold, it often rains and cold fronts pass t h r o u g h . H e n c e , t h e p o p u l a c e s ’ e v a c u a t i o n behaviors inclination may be affected by the weather. Therefore, this study focuses on the climate factor to further understand whether there is any difference due to weather condition.

According to our the results, the percentages of the populaces’ evacuation behaviors under conditions of bad weather and damaged domicile are as follows: making a temporary stay in community centers, schools, churches, temples or other buildings (38%), seeking help from relatives or friends (38%), choos-ing to stay in nearby parks, school play grounds, other open fields or making a tent (17%), living on alleys, streets or putting a tent near domicile (4%) or others (3%).

Comparing these two different evacuation be-havior patterns under normal weather conditions and

under wet and freezing weather conditions, our sta-tistics show that in normal conditions, the percent-age proceeding to shelters is 20%, but in bad weather the percentage will increase to 38%. Furthermore the population sleeping out on streets or in open fields will decrease from 35% to 17% when the weather changes from good to bad. As for the population who seek help from relatives or friends for shelter, the percentage increases to 38% in bad weather.

The above data show that evacuation patterns are not stable constants; instead, they vary with the outside environment. In planning a public shelter field in a certain area, a space with the capacity to contain 17% to 35% of the evacuee population, can then be predicted as sufficient for disaster evacua-tion in all seasons.

IV. SUGGESTIONS TO AMEND THE SHELTER MODULE STATISTICS In the Haz-US shelter module software, estimat-ing the demand for shelters requires, in advance, the percentage of people proceeding to shelters in differ-ent circumstances (population structure, income struc-t u r e a n d d o m i c i l e s struc-t r u c struc-t u r e ) . B a s e d o n o u r investigation, the following factors influence the per-centages of people going to shelters: structure of family, amount of income and ownership or non-own-ership of pre-quake domicile. These figures can be consolidated into tables to estimate the demand for shelters.

1. Relation between Family Structure and Evacu-ation Behavior Patterns

The different sample structures of evacuation behaviors are shown in Table 1. Based on models of evacuation shelters and given family structures to estimate the volume of shelters, we acquire the percentages of people’s evacuation behaviors at different ages. We find that the samples focus on family structure, not different ages; therefore, we could not divide these samples categories by age. Why? Because most families contain members in all age categories, we could not divide them into sepa-rate age categories, related to behavior categories. In order to acquire all ages for our samples, we must investigate in shelters immediately after an earth-quake to acquire these data. Finally, inferring, based on the present research, we need to consolidate the investigation results.

Investigation samples can be divided into students, senior citizens living in solitude, and non-local population. Under the assumption that the fam-ily is the evacuation behavior unit for students; se-nior citizens living in solitude are weak minorities;

(4)

non-local populations living in solitude will return to hometowns after disasters. These three popula-tions are expected to possess comparatively different evacuation behavior patterns, and therefore can be referred to as indexes in predicting the percentage of demand for short-term shelters. Transforming and analyzing the investigation data can result in the following:

(1) The ratio of population under the age of 16 which goes to shelters is 0.22.

(2) The ratio of population between the age of 16 and 65 which goes to shelters is 0.18.

(3) The ratio of population above the age of 65 which goes to shelters is 0.24.

The calculation above is based on the structure ratio of evacuee population. Revised statistics deter-mined are shown as Table 2.

2. Relation between Income and Evacuation Be-havior Patterns

The evacuation behaviors of different income groups are shown as Table 3. Please note that in the questionnaire the classifying standard is family

monthly income, whereas in the Haz-US module the classifying standard is family annual income. Based on different income levels of people proceeding to shelters, we could cite Table 3 directly to deduce the percentage of evacuee populace with different in-comes going to public shelters shown as in Table 4. 3. Relation between Homeownership and

Evacua-tion Behavior Patterns

Different living conditions linked with differ-ent evacuation behaviors are shown as Table 5. As a b a s e f o r e s t i m a t i n g d e m a n d f o r s h e l t e r s , homeownership can be divided into 4 general types: self-owned domicile, rented domicile, dormitory, and domicile borrowed from relatives or friends. (Those who choose the “other” option are queried for more detail.) By weighted grade calculation, the amended suggested values are determined as shown in Table 6.

V. CONCLUSION

The result of this research can help revise the Table 1 Choosing temporary shelters because of damage to domicile during earthquake

Senior Children Junior &

Sample citizens Non-local Percentage

under 7 years Senior High School

Item living in population

old Students

solitude Putting tents on

streets near the 20.8% 14.3% 24.5% 12.8% 16.6%

domiciles

Putting tents in the school playgrounds,

31.3% 42.9% 15.1% 20.5% 34.4%

parks or farmlands near the domicile Going to a

22.9% 20.2% 26.4% 12.8% 20.7%

government shelter Seeking support from

18.8% 20.7% 22.6% 48.7% 23.9%

relatives and friends

Others 6.3% 2.0% 11.3% 5.1% 4.4%

Subtotal 100% 100% 100% 100% 100%

Table 2 Revised suggested parameter values of age structure for proceeding to temporary shelters

HazUs Haz-Taiwan

Subject Age

Default Value Suggestion Value

AM1 Population under the age of 16 0.4 0.22

AM2 Population between the age of 16 and 65 0.4 0.18

(5)

Table 3 Choosing temporary shelters because of damage to domicile during earthquake Family Below NT$32,000 NT$80,000 NT$160,000 Above Monthly NT$ ~ ~ ~ NT$ Percentage Income 32,000 NT$79,000 NT$159,000 NT$290,000 290,000 Item Putting tents on

streets near the 22.1% 12.4% 14.7% − − 16.4%

domiciles Putting tents in school playgrounds,

28.7% 40.0% 26.5% 25.0% 50.0% 33.7%

parks or farmlands near the domicile Going to a

16.9% 22.1% 26.5% 25.0% 25.0% 20.4%

government shelter Seeking support

from relatives and 25.7% 23.4% 26.5% 50.0% 25.0% 25.1% friends

Others 6.6% 2.1% 5.9% − − 4.3%

Subtotal 100% 100% 100% 100% 100% 100%

Table 4 Revised suggested parameter values of income for proceeding to temporary shelters Haz-US Haz-Taiwan

Subject Income

Fault Values Suggestion Values IM1 Family annual income < NT$400,000 0.62 0.17 IM2 NT$400,000 ≤ Family annual income<NT$1,000,000 0.42 0.22 IM3 NT$1,000,00 ≤ Family annual income<NT$2,000,000 0.29 0.27 IM4 NT$2,000,000 ≤ Family annual income<NT$3,500,000 0.22 0.25 IM5 NT$3,500,000 ≤ Family annual income 0.13 0.25

Table 5 Choosing temporary shelters because of damage to domicile during earthquake Ownership of Domicile Self-owned

Rented-Dormitory Others Percentage

Item domicile domicile

Putting tents on streets near

13.1% 18.2% 11.1% 64.3% 16.7% the domiciles

Putting tents in school

playgrounds, parks or 37.4% 30.3% 33.3% 14.3% 34.2% farmlands near the domicile

Going to a government

21.5% 20.2% 11.1% 7.1% 20.2%

shelter Seeking support from

24.3% 25.3% 44.4% 7.1% 24.4%

relatives and friends

Others 3.7% 6.1% − 7.1% 4.5%

Subtotal 100% 100% 100% 100% 100%

parameters or numbers fed into the evacuation mod-ule of HAZ-Taiwan, so that we can come to a more precise prediction as to the numbers of people

engaging in different evacuation behaviors with the use of the HAZ-Taiwan software. Furthermore, through the evacuation behavior research in greater

(6)

Chia-Yi and Taipei City, we have added to our knowl-edge of Taiwanese’s evacuation behaviors. Finally, we have shown the effectiveness of the parameters used to evaluate the need for constructing evacuation shelters that are required.

In the present HAZ-Taiwan software, the infer-ences and the calculation procedures of most mod-ules depend on the original design of Haz-US. However, after two years of module research, inves-tigation and parameter amendment, researchers real-ize that some of the inference procedures or the in-fluential parameters are not ideal predictive variables, nor do they fit the real pattern for this Island. For instance, among those variables, the percentage of people who proceed to shelters does not include higher incomes, which reveals that income is not an ideal and effective predictive variable. In addition, a large number of research units have gone deep into the disaster zone to collect information after the 921 Chi-Chi Earthquakes in the year of 1999. Utilizing these accumulated research results to guide or to

amend to a more suitable calculation module of casualties and demands for shelters for Taiwan is a promising topic for further research.

REFERENCE

1. National Institute of Building Science, 1997, “Earthquake Loss Estimation Methodology -HazUs User’s Manual,” Federal Emergency

Man-agement Agency, pp. 9-41~ 9-49.

2. Sun, C. H., and Wang, S. M., 1999, “The Collec-tion of Data for the Earthquake Loss EstimaCollec-tion Methodology in Taiwan,” Workshop on Disaster Prevention/Management & Green Technology, Na-tional Science Council, (ROC), CA, USA, pp. 171-177.

Manuscript Received: Aug. 28, 2001 Revision Received: May 14, 2002 and Accepted: June 03, 2002

 !"#$%&'()*+,$%-./01

 1 !2 !3 !1 !3 1 !"#$%&#'() 2 !"#$%&'()*+ 3 !"#$%&'()   !"#$%&'()*+(,-./0123456789:;<  !"#$%&'()*+,-./0123456789$:;<  !"#$%&'() Haz-Taiwan  !"#$%&'(")*+,

 !"#!$%&'()&'* !"#$%&'()*+,

 !"#$%&'$%()'$%*+',-./0

Table 6 Revised suggested parameter values of domicile ownership for proceeding to temporary shelters

Haz-US Haz-Taiwan

Subject Ownership of domicile

Fault Values Suggested Values

OM1 Self-owned domicile 0.4 0.22

數據

Fig. 1  Questionnaire Structure
Table 2  Revised suggested parameter values of age structure for proceeding to temporary shelters
Table 5  Choosing temporary shelters because of damage to domicile during earthquake Ownership of Domicile Self-owned
Table 6 Revised suggested parameter values of domicile ownership for proceeding to temporary shelters

參考文獻

相關文件

In this way, the philosophy of tea giving with life cultivation of his personal characteristics, with fl esh and blood, and with wisdom and sadness and the course of Buddhist

• elearning pilot scheme (Four True Light Schools): WIFI construction, iPad procurement, elearning school visit and teacher training, English starts the elearning lesson.. 2012 •

(Another example of close harmony is the four-bar unaccompanied vocal introduction to “Paperback Writer”, a somewhat later Beatles song.) Overall, Lennon’s and McCartney’s

Microphone and 600 ohm line conduits shall be mechanically and electrically connected to receptacle boxes and electrically grounded to the audio system ground point.. Lines in

This study combines the Technology Acceptance Model and Theory of Planned Behavior as its research foundation, added with dimension of perceived value as

The core of this research is focusing on Service Innovation, discussing on the composite factors of manufacturing servitization through literatures and questionnaires from

development of AutoLISP programming language for building and into Knowledge Engineering (Knowledge Based Engineering, KBE) technology, the ball screw and linear guideway the use

The campus of an existed elementary school was this object of research which was evaluated according to evaluation tables of biodiversity, greenery and on-site water