Assessment the earthquake vulnerability of the ...

Caspian Journal of Applied Sciences Research 3(2), pp. 48-59, 2014 Journal Homepage: www.cjasr.com ISSN: 2251-9114

Assessment the earthquake vulnerability of the Tehran core through the application of factor analysis / linear regression model and its implementation in GIS environment kyoumars habibi 1,*, Kayoumars Irandost 1, Behzad Shahmoradi2, Reza Piroozi3, MitraSadat Hashemi4 1

Department of Urban Planning, University of Kurdistan, Sanandaj, Kurdistan, Iran

2

Kurdistan Environmental Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran

3

Faculty of Urban Planning and Architecture, IUST (Iran University of Science and Technology), Tehran, Iran

4

Faculty of Urban Planning and Architecture, Shahid Beheshti University, Tehran, Iran

Human casualties and financial losses in the urban texture resulted from the buildings demolitions are among the most important impacts of earthquake in cities. The concept of location vulnerability can be investigated in different categories factors; natural, artificial factors and the combination of both natural factors and human interventions are examples of this type. In order to enhance the power of the decision-making, management, and supervision bodies, the concept of urban vulnerability can be assessed for better governance of the city in future and preparedness for more intelligently operation at the time of crisis. In this study, 10 physical vulnerability criteria of urban texture and data of each criterion for six municipality zones of Tehran metropolitan were collected and were classified into four main factors using functional analysis method. After drawing the zoned vulnerability maps in GIS, using linear regression and with reference to the functional scores, a new ordering of criteria was achieved for prioritization of the programs dealing with the zone vulnerability conditions against earthquake. The final prioritization revealed that the main criteria of building establishment is very important followed by the criteria addressing the rules of the building size and form. © 2013 Caspian Journal of Applied Sciences Research. All rights reserved.

Keywords: earthquake; urban texture vulnerability; functional analysis; GIS, Tehran.

1. Introduction Evidence shows that while almost 28 percent in 1950 and now more than 50 percent of world population living in cities, the world is still faced with the growing trend of urbanization So that by 2020 more than 66 percent of world population will live in urban communities (Morgan T, 2003, 7). In addition to the inharmonious and irregular

growth of many of urban communities, increasing natural disasters has resulted in increasing the vulnerability of urban areas and is thus an increase in victims. It is estimated that about 95 percent of the world's developing countries are victims of natural disasters and losses caused by such accidents in these countries are 20 times higher than in the

*

Corresponding address: Department of Urban Planning, University of Kurdistan, Sanandaj, Kurdistan, Iran E-mail address: [email protected] (Kiumars Habibi)

© 2013 Caspian Journal of Applied Sciences Research; www.cjasr.com. All rights reserved.

Kiumars Habibi;Kayoumars Irandost;Behzad Shahmoradi;Reza Piroozi;Mitra-Sadat Hashemi / Assessment the earthquake vulnerability of the Tehran core through the application of factor analysis / linear regression model and its implementation in GIS environment 2(8), pp. vFIRS#-vLAST#, vYEAR#

developed countries (Kreimer et al., 2003, 2). Urban areas, especially in developing countries, have become vulnerable to earthquake due to poor planning, poor design of buildings, carelessness in principle implementation of the civic construction projects, lack of adequate care in repairmen and maintenance, settlement in areas at risk, etc. Quick

look at the history of casualties hit by the recent devastating earthquakes throughout the world is expressing the fact that 80 percent of the casualties of these earthquakes have been in the 6 countries of China, Iran, Peru, the former Soviet Union, Guatemala, and Turkey.

Table 1: Research Process (Source: Authors) No Step 1 2

Statement of the problem and purpose of study Finding the Research Theory

3 4 5 6 7

Doing a literature review Area of study introduction Gathering intended criteria for measuring urban vulnerability Documentation (together with consultation with the experts in this field) Implementation of factor analysis and new classification of criteria in extracted factors (4 factor)

8

Implementation of linear regression analysis among factors obtained and vulnerability score

9

criteria ranking for priority determining the vulnerability studies

10

Summary, conclusions and alternative suggestions

Locating on the earthquake belt of the Alpine Himalayan during past centuries, Iran has experienced 130 earthquakes of magnitude 7.5 Richter or more (Ghafory-Ashtiany, 1999, 4). Earthquakes occurred along with huge financial and criminal damages like Buin Zahra (1962), Rudbar (1990) and Bass (2003) are witness for this issue. Moreover, according to the research works conducted by the Department of Housing and Urban Development in the form of the national physical plan, Iran has been classified into the areas with a very high risk, fair, moderately low, and low indicating that 50% of the urban population are living in areas of high, very high, and moderately high risk (Kavab Consulting Engineers, 1990, 52). In other word, preliminary zoning map of the earthquake relative risk in Iran indicates that a significant part of the state residential areas are located in the high relative risk and almost all lands in relative moderate to high risk limits (Ziari, 1999, 283). However, studies performed on Tehran Earthquake, suggests that the possibility of an earthquake of high intensity is very high in Tehran. According to the experts opinion, Tehran is the only city, where may be heavily damaged (70% demolition) due to an earthquake at engineering scale, "moderate earthquake" (Najafi, 1992, 4), because according to the earthquakes records, Tehran has experienced several high magnitude earthquakes with 150 years return period and this city has not experienced any catastrophic

earthquake since 1830 (JICA, 2001). Moreover, it has been located exactly on a fault. Considering the high potential of the country's vulnerability, the fact of Tehran range being rich of seismology, unpredictability the time and place of earthquake, and consequently resulted increasing the losses, damages, and injuries make the vulnerability to seismic events undeniable and studying and assessing its dimensions become essential. Because the vulnerability requires a holistic planning and design, it seems that it is possible to minimize the earthquake vulnerability through planning, intervening, and changing some of the effective factors in vulnerability (ZangiAbadi et al., 2006). Following understanding this necessity, this research work embodies a picture of the relative vulnerability of the central tissue of Tehran and specifically in the zone 6, Tehran municipality division and creates this possibility that to predict the vulnerability capacity of this zone against earthquake through identifying the vulnerability criteria, their logical prioritization, and providing vulnerability map at fractures level. In addition, providing solutions and giving necessary measures to reduce vulnerability can facilitate and expedite dealing with the status of urban areas vulnerability. Thus, enhancing ability of the decision-making authorities, management and supervision can provide better city leading conditions in future and preparedness for smarter functionality at the time of crisis. The oldest activities on vulnerability to 49


earthquake are dated back in 1972, when nonlinear models were proposed to identify the behavior of buildings against earthquakes (Zahrae et al., 2002). Many studies of global and domestic vulnerability to earthquake phenomena have been conducted in different years. Rashed (2003) has studied role of GIS and remote sensing and their integration in modeling and predicting the urban vulnerability against earthquake. Habibi et al (2009b) used fuzzy logic and GIS for identifying unstable zoning using selective vulnerability indices. Studying environmental (geological) or physical factors, Zangi Abadi et al (2008) have considered the impact of other factors like social and economic factor in increasing earthquake damages. All studies conducted indicate that although numerous studies and models for assessing and reducing earthquake vulnerability of urban areas have been done, but the issue of programs prioritization in the vulnerable areas has been paid less attention. Thus, in the present study, it was attempted to first study the urban areas vulnerability in the study area using functional analysis / linear regression model and then to prioritize the areas programs and to give suggestions and alternatives to reduce vulnerability.

against one or more risk factors and the disaster is measured based on the risk impact. It fluctuates from scale of zero (no vandalism) to one (full sabotage) for a special disaster (Ghotbi, 2008). Against earthquake risk vulnerability is classified into tangible and intangible elements. Tangible elements include unstable buildings and their poor residents, equipment and machinery, infrastructure, people property, and unstable and unstructured elements inside the buildings and intangible elements include social solidarity, community structure, dependencies, and cultural products of the community (Coburn et al., 1994). Urban vulnerability is the amount of damage imposed on a city and its elements and components in case of disaster occurrence. In this category, vulnerability involves the inherent value of the related elements and their functional value in helping protection and accelerating pre-disaster conditions in general, and emergency response to a disaster and recovery and other specific cases. Urban vulnerability is a broad phenomenon that encompasses all the factors in a city and due to the dependence of urban elements and components together; it covers all the factors in a city and develops quickly. Urban vulnerability against natural hazards such as earthquake is a function of the human behaviors, which indicates the degree of influence or ability to stand up to economic - social entities and urban physical assets versus natural hazard (Rashed and Week, 2003; Ahadnejad et al., 2010). In this case PGA (Peak Ground Acceleration) measure is an important one and what is experienced by a particle on the ground. PGA is a natural simple design parameter since it can be related to a force and for simple design one can design a building to resist a certain horizontal force (Supriatna Semedi et al., 2010).

2. Materials 2.1. Research theory Among the fundamental issues of vulnerability, the terms earthquake and the urban vulnerability could be implied. Earthquake is the seismic energy transmitted to the surface from deep underground and usually creates shear, tensile or compressive (horizontal, vertical, circular) failures, with seismic high voltage in the Earth's crust. The mentioned vibrations cause damage and destruction of buildings, facilities and killing and injuring people (Habibi, 2009, 50). ‘Vulnerability’ literally means to be damage’ (Merriam-Webmaster Dictionary). The word has come from Latin, vulnerabilis [vulnero þ bilis], which means ‘liable to be wounded’ (Oxford Latin Dictionary). The state of a soldier lying wounded on the battlefield has been explained in the Romans (Moral et al., 2013). Vulnerability is the tendency of various elements to be demolished due to event occurrence. In other words, vulnerability is a degree of damage imposed to an element or a set of elements because of a natural phenomenon. In engineering, vulnerability is seen as a mathematical function in which the amount of damages is placed

2.2. Study area District VI of Tehran Municipality was selected as the study area for this research work (Fig 1). The area of this district is 2,144 ha having a population of 232,583persons under coverage. Having gross population density of 108 people per hectare and with a surface area equivalent to 3 percent of Tehran, District VI as one of the most important Tehran urban districts has been a prestigious place in the Tehran urban developments. The first step in the formation of the area was taken between the years 1931-1941 by destruction of the Nasseri ramparts along the north ditch of Tehran and construction of Enghelab Ave. with 5 km in length. Setting up and placing most of the governmental, 50


educational, and cultural elements as a result of the area development had been hospitalizing the political and social decision-makers groups; hence, it has been the origin of the fundamental effects in the subsequent developments and Tehran structure. As Fig 1 indicates, District VI has been located in the almost geographical center of Tehran and adjacent to the city old gavial center, i.e. Bazar and Arg Circle. Moreover, under influence of the Pahlavi I measurements regarding to the Tehran development, and gradual transferring and movement of the Tehran centrality towards the north and west north, it got the activity-spatial centrality from 1950s. Meanwhile, constructing Ministry of Agriculture, and several official buildings and new urban centers at more limited operational scales along the paths within District VI

resulted in doubling the area body centrality so that the operational and spatial present conditions of the main part of the district has got Capital of Iran name (Habibi, 2009). In addition, a significant part of the district plays as the skeleton of Tehran and new C.B.D. gravitational center. For example, locating 10 ministries and 142 sister departments, 49 Universities and UG institutes, 66 hospitals and healthcare centers, 26 embassies and international organizations,… hundreds of the main official functions and governmental and private economical-financial (centralization of most of the commercial activities in central or southern areas of Tehran that have less residential usage (Eshghabadi et al., 2013)) founds in this district are among the main land uses (Naghshe Jahan-Pars Consulting Engineers, 2007).

Figure 1: Location of the Zone 6 municipality in Tehran metropolitan and Iran Country

51


aspects, in this issue by applying urban vulnerability indices, once can achieve various aspects of vulnerability at the time of the earthquake.

3. Methodology and Its Application in the Area of Study This study is an applied research with Positivism approach. The research method was descriptive analytical and data collection was based on library studies and literature review, field and observatories, and documentation of the Tehran master and comprehensive plans reports. As for the analysis of any phenomenon, we need to have measures that will assist us in gathering various

Therefore, after reviewing the various types of research, the combination of the main criteria affecting vulnerability to earthquake resulted from the previous studies were selected and after reviewing available information about the area study, 10 major criteria for the study were extracted and documented (Table 2).



building age construction materials















 

building quality 

building stores



construction density









landuse



K. Habibi et al (2009)

 





























Primary database was provided using available maps in GIS environment and through adding data collected at separated pieces level in District VI to the spatial maps in order to document area conditions regarding to each of the criterion. Information of every piece was classified based on the five-level Likert rating from very low to very high vulnerability. According to the city as a complex phenomena, it's so important to find a simple way for modeling it. We assessed a variety of







confinement

access to open spaces





parts area

Ground Horizontal Acceleration (PGA)

A. Faraji et al (2010)

A. Asgari et al (2002)

ZangiAbadi et al (2010)

T.Rashed et al (2003)

M. Servi(2004)

M.C.schmidtlein et al(2003)

Criteria

A.Giacomo et al(2007)

Table 2: Criteria of earthquake vulnerability (previous similar studies)



methods such as fuzzy, AHP, etc. and finally factor analysis was selected for classification. A combination of factor analysis and linear regression methods would have many merits compared to other methods. The main benefits of factor analysis are that the analyst can focus their attention on the unique core elements instead of the redundant attributes, and as a data ‘pre-processor‘ for regression models. Figure 2 shows the diagram of methodology.

52


Figure 2: Diagram of Methodology

After entering the information into the SPSS software, first using factor analysis data matrices were entered in factor analysis using principal component analysis (PCA) in order to form a correlation matrix. According to Table 3 and the Bartlett test components, it was revealed that variables are correlated and could be verified using factor analysis. Moreover, considering sampling adequacy measurement (KMO)> 0.5, it is

determined that the matrix does not match with the phenomenon of single line or multiple lines and data are suitable to continue analysis. After observing primary calculation matrix, it is noticed that the percentage of cumulative variance for the factors determined is about 73 percent, which explaining the issue with this much of the cumulative variance is acceptable in urban studies.

Table 3: Bartlett, KMO Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. Bartlett's Test of Sphericity

Moreover, the specific value of each determined factor greater than 1 was measured and through the notch test, it was revealed that the first four factors have a specific value greater than 1; hence, they are introduced for the earthquake vulnerability study of the study area. according to table 4, variance intervals are more than 10% that shows the suitability of 4 first factors to explain the issue. In order to make the criteria closer to the factor scope and reaching the best arrangement and achieving the criteria having maximum factor load

.657 Approx. Chi-Square

1.001E5

df

45

Sig.

.000

in each factor, we used orthogonal rotation (Varimax). After five times repetitions in the present study, a weight was obtained for each factor versus criteria; the values less than 0.4 were eliminated to facilitate decision-making and we selected those criteria having maximum score in each column versus factors. At this stage, factors were named by evaluating criteria in each category (subset of a factor). Table 4 shows name of factors and determinant criteria for each factor.

53


Table 4: Naming and categorizing factors based on the maximum factor loading of variables Factors

Eigenvalue

RSSL/ Cumulative %

Factors name and determinant criteria for each factor

F1 C2 C1 C3 C8

2.648

24.355

construction and use construction materials building age building quality landuse

F2 C4 C6 C7 F3 C10

2.272

4

C9 F4 C5

1.389

1.033

46.985

61.853

73.3416

Form and Dimensions building stores construction density confinement Location Ground Horizontal Acceleration (PGA) access to open spaces Area parts area

By doing this analysis, for each of the components, a value in each factor is obtained. The maps with five spectra of each of them have been presented below. The criteria of construction materials, building age, building quality, and landuse related to the first factor were used as "construction and use". Then, the maps of the first factor forming criteria and the final map of the factor itself are presented. As it has been indicated using lighter spectrum in the map of construction materials (Fig 2), the materials used in the district

3

2

1 .858 .827 .790 .613

.939 .885 .752 .827 .825 .901

buildings have better conditions and the old buildings endanger physical vulnerability conditions in the central and southern parts of the district. Considering social and economical situations of the area residents, especially in the middle and northern parts, buildings have relatively good quality. Compared with the nonresidential landuses of the southern part, residential landuses in the northern parts of the district increase the vulnerability risk at the time of hazard occurrence.

Figure 3: Categorizing pieces in factor 1 and its forming criteria

54


Figure 3 shows three criteria, namely, building stores, construction density, and confinement related to the second factor, entitled "Form and Dimensions". As Figure 3 indicates, most of the buildings are classified as medium-high vulnerable from the number of stores viewpoint, whereas

considering building density, the vulnerability of the area is at low risk. The confinement factor increases the physical vulnerability of the components located in the inter-texture and farer from the mains.

Figure 4: component classification in factor 2 and its forming criteria

The criteria of the ground horizontal acceleration due to earthquake, access to open spaces, and relief centres included in the third

factor, entitled “Location”, are less vulnerable against earthquake in the north and west northern parts. (Fig 4)

Figure 5: component classification in factor 3 and its forming criteria

The fourth factor, Area, was determined based on the components are classified. The area map

(Fig 5) indicates that vulnerability of buildings in the area is relatively low.

55


Figure 6: component classification in factor 4

After factor analysis, relationship or correlation of each factor with general vulnerability was assessed using final vulnerability score and the four factor achieved and through linear regression analysis. For this purpose, using Durbin-Watson test and confirming lack of self-correlation of the variables, assessing coefficient of the linear

regression analysis was conducted. In this method, the values of earthquake vulnerability, depended variable, and the four independent factors have been considered. At the end, considering tables obtained and “Beta” column in Table 5, dependency of the factors to the urban earthquake vulnerability in the district has been determined.

Table 5: coefficient of the linear regression analysis Model

B

Std. Error

(constant)

6.588

0.026

F1

2.942

0.026

F2

6.151

F3 F4

beta

t

sig

265.363

0.000

0.295

114.488

0.000

0.026

0.617

239.347

0.000

5.353

0.026

0.537

208.307

0.000

2.260

0.026

0.227

87.947

0.000

(Depended variable: value of earthquake vulnerability )

Summing up the scores of different factors and considering dependency of each factor to the general vulnerability gave the final map of the

earthquake vulnerability. Figure 6 presents the individual component vulnerability at five classes from very low to very high.

56


Figure 7: final map of earthquake vulnerability of Tehran District VI

Results of applying this model for Tehran District VI show that the southern parts are more vulnerable to earthquake because of concentrated texture, unsuitable locating condition, building age and quality conditions, and irregular access compared to other parts of the district.

awareness to the concerned decision-making authorities. For this purpose, using multiplying "Beta" entity for each factor and the factor loads for each criterion within specified factor into the average vulnerability in each criterion, the criteria were ranked for handling. In Table 6, ranking criteria was determined according to their importance, their correlation with general vulnerability, and average vulnerability of each criterion. Collecting, evaluation, classification, valuing, and placing criteria in specific classes are the main steps to achieve the perfect way to assess the vulnerability.

4. Conclusion Finding importance and the criteria relationships and their classification in a research would be valuable when it results in giving

Table 6: criteria ranking for priority determining the vulnerability studies

No

Criteria Name

ranking

loading factor

Beta

Average of vulnerability 4.52

1

Ground Horizontal Acceleration (PGA)

2.007

0.827

0.537

2

access to open spaces

1.814

0.825

0.537

4.1

3

building stores

1.784

0.939

0.617

3.08

4

construction density

1.429

0.885

0.617

2.62

5

confinement

1.395

0.752

0.617

3.01

6

building quality

0.758

0.790

0.295

3.26

7

building age

0.738

0.827

0.295

3.03

8

parts area

0.684

0.901

0.227

3.35

9

landuse

0.608

0.613

0.295

3.37

construction materials

0.491

0.858

0.295

1.94

10

57


Using this procedure, we can measure the earthquake vulnerability in different urban districts and we can plan and run some activities in order to reduce damages resulting from earthquakes by zoning and also by finding main criteria influencing the zone vulnerability. Moreover, the procedure and steps followed in this research could be considered as a pattern for measuring vulnerability of various urban textures and as an approach to rate city limits for urban earthquake vulnerability. Two criteria are determined in the final prioritization, which are very important after the fundamental criteria of the building establishment, dealing with the regulation of the building form and dimensions. In addition, evaluating the built spaces including residential, official, industrial buildings, facilities, etc. and determining an appropriate construction strategy to reduce the physical vulnerability, such as determining the design criteria based on the construction standards, strengthening weak structures against disaster events must be considered by the managers and planners. it can be importent to do some preventing actions like: to reduce construction density and confinement in narrow roads, to improve the transportation ways hierarchy in Vali-e-Asr neighborhood. To enhance the accessibility of Karegar St., prohibit the high-level constructions in Vali-e-Asr Enghelab and IranShahr neighborhoods, to locate some emergency places in Laleh Park, Keshavarz-West, Nosrat neighborhood and Tehran University, according to importance of constructioin and location factors.

compliance with 2800 Code in order to improve the physical conditions; empowering workforce; creating jobs; organizing the functional areas; encouraging investment in the development of economic entities in order to improve economic conditions; establishing relationships and coordination between individuals and decision making institutions; enhancing efficiency of the administrative and technical management; creating partnerships between institutions and active organizations; and identification and establishment of functional relationships between organizations involved in emergency disasters; strengthening the Crisis Management Center in order to improve crisis management; and finally creation of a regional GIS database regularly for Tehran and other cities and regions so that having knowledge of the area vulnerability would assist them in taking the necessary measures to reduce the areas vulnerability. References Ahadnejad M, Gherekhloo M, Ziari K (2010). Modeling the Earthquake Vulnerable Cities Building Process Using a Hierarchical Analysis in GIS Environment, A case Example: The City of Zanjan. Geography and Development Journal, Year VIII, Number 19,171-198. Asgari A, Parhizkar A, Ghadiri A (2002). Application of Methods of Urban Planning (Land Use) to Reduce Vulnerability of Earthquake Risks (With GIS); Case Study: District 17 of Tehran. Geographical Research, No.67.

Based on our results, various strategies and recommendations to prevent or to reduce the irreparable damages resulted from earthquake to at least the core of Tehran are as follow: decreasing density and prevention of tall buildings; avoiding uncontrolled and dense growth on the loosetextured soils; appropriate site location for rescue and fire through developing vast access radius, especially in high density and vulnerable areas; creating a hierarchy of public welfare activities; building a hierarchy of access network proportional to the service level; organizing transportation networks and Infrastructure; renovating old houses; creating local green spaces and open areas in neighborhoods; retrofitting of important buildings such as schools, hospitals, and government centers; repairing and modification of old infrastructures; creating rescue specific routes in the event of earthquake; preventing unauthorized construction; building construction

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GhaforyAshtiany M (1999). Rescue Operation and Reconstructions in Iran. Disaster Prevention and Management, vol.8,N 1,MCB University,ISSN 0965-3562.

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