Assessing social vulnerability to earthquake disaster using rough analytic hierarchy process method: A case study of Hanzhong City, China

Safety Science 125 (2020) 104625

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Assessing social vulnerability to earthquake disaster using rough analytic hierarchy process method: A case study of Hanzhong City, China Xuesong Guoa, Naim Kapucub, a b

T

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School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an, Shaanxi, PR China School of Public Administration, University of Central Florida, Orlando, FL, USA

A R T I C LE I N FO

A B S T R A C T

Keywords: Rough analytic hierarchy process Social vulnerability Vulnerability assessment Earthquake Disaster

This study assesses the local social vulnerability to earthquake disasters at a county (urban district) level, with a particular focus on Hanzhong City. A Rough Analytic Hierarchy Process based model was proposed for social vulnerability assessment. Based on criteria derived from the literature, the researchers determined the criteria weights using Rough Analytic Hierarchy Process method, and assessed social vulnerability for each county based on census data. furthermore, the most important factors contributing to social vulnerability were highlighted based on the results of sensitivity and robustness analysis. This research provides some implications for disaster mitigation and risk reduction strategies as well.

1. Introduction Earthquakes, as natural disasters, have very strong destructive power, widespread range, and severe threat to human safety and security (Chong et al., 2010; Guo and Kapucu, 2018; Zhang, 2010). China, one of the most seismically active areas in the world, has experienced many destructive earthquakes (Comfort, 2019; Xu et al., 2017). From 2003 to 2008, 265 earthquakes greater than magnitude 5, 59 earthquakes greater than magnitude 6, and 6 earthquakes greater than magnitude 7 happened in China. Among these earthquakes, the 2008 earthquake in Wenchuan, Sichuan Province, resulted in 69,227 deaths, 17,923 people missing, 373,583 people injured and caused a direct loss of 852.3 billion China Yuan (Xu et al., 2010; Nie et al., 2012; Yin et al., 2009). Vulnerability, a core element of reducing disaster risk, has been identified as the most significant prerequisite for resilience under degrees of exposure to disasters. Although definitions and applications of the term vulnerability vary (Cutter, 2003; Weichselgartner and Juergen, 2001), common elements within the natural hazard’s literature include concepts of exposure, sensitivity, and resilience (Cutter et al., 2006; Cutter, 2003; Hewitt, 1997). Several study conducted on social vulnerability to reduce the damage caused by earthquakes (Cutter, 2003; Cutter and Scott, 2000; Hewitt, 1997; Xu et al., 2017). According to Cutter et al. (2003, p.254), social vulnerability can be assessed using Social Vulnerability Index Score (SoVI). Component scores were equally weighted within this additive model. In this way,

⁎

Corresponding author. E-mail address: [email protected] (N. Kapucu).

https://doi.org/10.1016/j.ssci.2020.104625 Received 4 July 2018; Accepted 26 December 2019 0925-7535/ © 2020 Published by Elsevier Ltd.

each component was viewed as having an equal contribution to overall vulnerability due to absence of a defensible method for assigning weights. Hence, the accuracy of SoVI can be improved through introducing method to assign weights for factors contributing to overall social vulnerability to disasters. On the other hand, comprehensive social vulnerability assessment requires involvement of various stakeholders’ apprehensions and their active participation for incorporating indigenous knowledge into vulnerability assessment (Birkmann et al., 2012; Engle and Lemos, 2010). Morever, long-term earthquake risk management policy formulation necessitates involvement of a wide range of stakeholders. Participatory social vulnerability assessment method provide an opportunity to utilize the stakeholders’ indigenous knowledge to address this need. However, multiple stakeholders often result in multiple and even conflict preferences, and rational assessment process is essentially based on multiple criteria (Khamespanah et al., 2016). Incorporating multiple conflicting preferences into the social vulnerability assessment is posed as an important challenge for future work. Therefore, the critical issue is how to manipulate the subjective stakeholders’ perceptions on weights of factors. Assigning weights of factors is a vague and subjective task because it is difficult to describe the importance of each factor in different contexts. As a consequence, the task involves subjective judgments, instead of numerical expressions and objective decisions. In this respect, the weights of evaluation criteria for SoVI calculation are seldom available and have to be determined subjectively by multiple stakeholders and experts.

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only rely on an adequate understanding of the disaster itself, but should also explore the sense of exposure and vulnerability in a society (Guo and Kapucu, 2018; Rygel et al., 2006). Social vulnerability analyses aim to understand which population groups may be the most vulnerable to the impacts of natural hazards and identify the key factors that affect social vulnerability (Hao et al., 2010). The results of social vulnerability study, can be used for risk management decisions, including risk reduction strategies and disaster mitigation (Birkmann, 2007). The increasing importance of social vulnerability assessment has triggered some attempts to provide solutions in recent years. Several attempts were made to study social vulnerability in different nations, such as the US (Cutter and Finch, 2008; Eric, 2013), Italy (Kuhlicke et al., 2011), and Australia (Granger et al., 1999). Because of its complexity and context dependability, scholars have different definitions on social vulnerability. Clark et al. (1998) discussed social vulnerability in references to the extent of damage caused by a disaster. According to Wisner and Uitto (2009), social vulnerability is ‘‘the characteristics of a person or a group that affect their capacities to anticipate, cope with, resist and recover from the impacts of a disaster’’ (p. 215). It is a preexisting condition or an inherent property of existing communities, independent of the hazard type or threat source (Cutter et al., 2015). Koks et al. (2014) consider social vulnerability as a core element of dealing with disasters. Chen et al. (2013) suggested that social vulnerability influences people's ability to make full pre-disaster preparation under the preexisting conditions, and to recover from post-disaster reconstruction. Furthermore, methodologies on social vulnerability assessment were discussed extensively. Koks et al. (2015) combined disaster, exposure, and social vulnerability to explain the social vulnerability to flooding disasters in Rotterdam, the Netherlands. They analyzed six indicators extracted from the previous literature: total population, single parent family, non-European immigrants, population under 14 years old and above 65 years old, average monthly income, and average construction age. The same weight is assigned to each indicator to obtain comparative results for each social vulnerability variable. Box et al. (2016) utilized a mixed-methods approach including a quantitative questionnaire and qualitative interviews to analyze flood risk responses in Brisbane, Australia, and understand what factors helped or hindered the response and adaptation to flood risk. Zebardast (2013) evaluated the social vulnerability to earthquake disasters in Iran using Analytic Network Process model. They applied spatial data analysis to reveal the spatial patterns in Iran to provide the theoretical basis for earthquake disaster risk management. Arias et al. (2016) highlighted the necessity of developing a vulnerability-centered risk management framework based on social cohesion and integration principles. Zeng et al. (2012), focusing on Luogang District in Guangzhou, China, proposed a new social vulnerability assessment method for natural disasters based on remote sensing technology. Rufat et al. (2015) indicated that a significant challenge in measuring social vulnerability to hazards is identifying influencing factors. They identified demographic characteristics, socioeconomic status, and health as the leading empirical drivers of social vulnerability. The most commonly used methods for the social vulnerability analysis are the Analytical Hierarchy Process (AHP), Principal Component Analysis (PCA), and Geographic Information System (GIS), which help managing, identifying, and visualizing the social vulnerability index of a specific area (Fatemi et al., 2016). Although there has been real progress in the theoretical underpinnings of social vulnerability over the past two decades, this advancement in developing methods for measuring social vulnerability is needed. Social vulnerability is a multidimensional construct which cannot be captured with a single variable easily (Cutter and Finch, 2008). There has been some progress in social vulnerability assessment strategies and methods (Arias et al., 2016; Rufat et al., 2015; Zebardast, 2013; Zeng et al., 2012), but challenges in method development still exist (Cutter et al., 2009). Especially, criteria weights determination is

Various approaches have been used to examine the multiple preferences in the weights of factors contributing social vulnerability. Multicriteria techniques are considered as a promising framework for evaluation since they have the potential to explicitly take into account conflictual, multidimensional, incommensurable and uncertain effects of decisions. The most widely used multicriteria methods include the Analytic Hierarchy Process (AHP), multiattribute utility theory, outranking theory and goal programming. Amon the ones listed above, AHP has been widely applied for preference analysis in complex, multiattribute problems (Ghobadi et al., 2017; Lin and Pussella, 2017; Russo and Camanho, 2015). The fuzzy group AHP has received the most attention because of the abilities in handling the subjective human ideas and modeling Multicriteria Decision Making (MCDM) problems (Fatemi et al., 2017; Yi et al., 2017). However, the fuzzy group AHP suffers from the limitations that stem from pre-determined fuzzy membership function. First, the way of selecting the membership function has not yet been thoroughly established. It relies on the subjective and heuristic decisions of domain experts, and affects the performance of vulnerability assessment. Some systematic approaches such as neural network can be utilized to tune the membership function, but they are not feasible in social vulnerability assessment due to complexity and small sample size. Second, the boundary interval that denotes the degree of subjectivity is fixed with respect to the types of membership functions. This is not be true in reality, because the subjective perceptions vary across decision makers (Zhai et al., 2009). As a potential remedy, this study proposes a systematic approach for social vulnerability using AHP and Rough Set Theory (RST). AHP is one of the most widely adopted MCDM methods that is effective in structuring group decisions and manipulating the qualitative and quantitative criteria, but ineffective when applied to an ambiguous problem (Lin et al., 2009). The RST is a mathematical tool capable of dealing with the imprecise and subjective judgments of domain experts by overcoming the previously noted membership function-related limitations of fuzzy set theory. It only relies on the original judgments without any assumptions on membership function and auxiliary information, and can be utilized even with small samples (Pawlak, 1982). By integrating the strength of RST in handling the subjectivity and the merit of AHP in modeling MCDM problems, the suggested Rough AHP measures the weights of factors contributing social vulnerability to incorporate multiple stakeholders’ preferences. We believe that the suggested approach can promote consensus building in the field of social vulnerability assessment in dealing with vulnerability and disaster risk reduction. Hanzhong city, located in Southwest of Shaanxi Province, has the highest probability for earthquakes and related economic losses. It was one of hardest-hit areas in Wenchuan earthquake. Wenchuan Earthquake caused 36 deaths, 499 injuries, and a direct loss of 520 million China Yuan, with more than 1000, 500 people evacuated in Hanzhong (Wang et al., 2010; Yin et al., 2009). Although scholars have conducted relevant and useful researches on social vulnerability assessment in the context of Wenchuan Earthquake (Xu et al., 2017), few studies have assessed social vulnerability of Hanzhong city to earthquake disaster until recently. This study assessed local social vulnerability to earthquake disaster using an evaluation index system, based on Hanzhong city case. Methodologically, this research proposed Rough AHP Method as a remedy for factor weights determination method. 2. Literature and background Since it is difficult to predict earthquakes accurately with the currently available technologies, social vulnerability assessment for earthquake risk reduction is very critical (Xu et al., 2017). Social vulnerability assessment is important for effective disaster mitigation and resilience (Kapucu and Ozerdem, 2013; Rygel et al., 2006; Xu et al., 2017). Timely and effective disaster risk management strategies not 2

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Set (RS) with conventional Analytic Hierarchy Process (AHP). And the procedures of this method are shown as follows: First, we conducted surveys to obtain a group decision matrix, based on the proposed hierarchical structure of criteria. And an expert team was invited to make pairwise comparisons on criteria prioritization to obtain the weights evaluation data matrix. The kth expert pairwise comparison matrix Dk can be expressed as Eq. (9), where rijk is the kth expert’s judgement value for the ith criterion importance compared with the jth criterion.

related to the Multiple Criteria Decision Making (MCDM) problem involving multiple decision makers (Opricovic and Tzeng, 2004) and consensus formation have not been addressed yet (Sakawa and Kato, 2015). In practical terms, problems, such as lack of prior information, subjective and vague judgments (Liu et al., 2012), make it difficult to evaluate social vulnerability accurately (Tate, 2012). Thus, a method which can capture subjective judgments by multiple decision makers to reach consensus on criteria weights can be useful for social vulnerability assessment. The proposed method in the paper for social vulnerability assessment is explicitly linked to multiple stakeholders’ preferences. Two key features distinguish our research from previous work. First, we modified the assumption that each factor can be viewed as having equal contribution to overall vulnerability (Cutter et al., 2003). Second, we suggest that the weight of each factor can be determined through integrating multiple stakeholders’ preferences. Therefore, the data used for the research include not only from census data, but also the subjective judgement of multiple stakeholders/experts, while original SoVI is calculated merely based on census data. Our method promotes consensus building in social vulnerability assessment through integrating the strength of RST in handling the subjectivity and the merit of AHP in modeling MCDM problems. It is also expected that our method can be employed in various ambiguous problem in the filed of vulnerability and/or risk assessment.

k ⎡ 1 r12 ⎢ rk 1 Dk = ⎢ 21 ⎢⋮ ⋮ ⎢r k r k ⎣ m1 m 2

3.1. Criteria weights determination using Rough Analytic Hierarchy Process method

¯ (Ci ) = U {Y ∈ U / R (Y ) ≤ Ci} Upper approximation: Apr

(1)

(11)

5⎤ 4⎥ 3⎥ 1⎥ ⎦

(12)

4 3 3 ⎤ 1 1/2 1/3 ⎥ 2 1 1/2 ⎥ 3 2 1 ⎥ ⎦

(13)

(3)

1, 1, ⎡ ⎢1/5, 1/5, D=⎢ 1/3, 1/3, ⎢ ⎣1/4, 1/2,

(5)

The arithmetic operations of interval analysis can be applied to rough numbers. If RN1 = (L1,U1) and RN2 = (L2,U2) are two rough numbers and k is a nonzero constant, then the arithmetic operations are given by Eqs. (6), (7) and (8).

RN1 + RN2 = (L1,U1) + (L2,U2) = (L1 + L2,U1 + U2)

(6)

RN1 × k = (L1, U1) × k = (kL1, kU1)

(7)

RN1 × RN2 = (L1,U1) × (L2,U2) = (L1 × L2,U1 × U2)

(8)

⋯ r1m ⎤ ⋯ r2m ⎥ ⋱ ⋮⎥ ⎥ ⋯ 1⎦

(14)

Then, we can get the group decision matrix by combining the above four pair-wise matrixes shown in Equation (10)-(13) together, as shown in Eq. (15).

(4) _

5 3 2 ⎤ 1 1/3 1/2 ⎥ 3 1 1/2 ⎥ 2 2 1 ⎥ ⎦

⎡ 1 r12 ⎢r 1 D = ⎢ 21 ⋮ ⋮ ⎢ ⎣ rm1 rm2

¯ (Ci )) . So, the human idea and interval of boundary region can limit(Lim be expressed by Eqs. (4) and (5).

¯ (Ci ) − Lim (Ci ) Interval of boundary region: IBR (Ci ) = Lim

⎡ 1 1/5 D2 = ⎢ ⎢1/3 ⎢1/2 ⎣

After the consistency test (Saaty, 1977), the group decision matrix D can be built following Eq. (14), where rij = {rij1, rij2, ⋯, rijm} .

_

_

(10)

(2)

Thus, the class, Ci, can be represented by a rough number. And rough number can be defined by its lower limit(Lim (Ci )) and upper

¯ (Ci ))] Rough number: RN (Ci ) = [(Lim (Ci )), (Lim

5 3 4 ⎤ 1 1/3 1/2 ⎥ 3 1 3 ⎥ 2 1/3 1 ⎥ ⎦

⎡ 1 1/4 D4 = ⎢ ⎢1/3 ⎢1/3 ⎣

Boundary region: BR = U {Y ∈ U /R (Y ) ≠ Ci} = {Y ∈ U / R (Y ) < Ci } U {Y ∈ U / R (Y ) > Ci}

⎡ 1 1/5 D1 = ⎢ ⎢1/3 ⎢1/4 ⎣

3 6 ⎡ 1 1/3 1 2 ⎢ D3 = ⎢ 1/6 1/2 1 ⎢1/5 1/4 1/3 ⎣

Rough Set (Pawlak, 1982) is a mathematical tool capable of dealing with subjective and imprecise concepts (Greco et al., 2001). Assume that there is a set of n classes of human ideas, R = {C1, C2, ⋯, Cn} , ordered in the manner of C1 < C2 < , ⋯ < Cn and Y is an arbitrary objects of U, then the lower approximation of Ci, upper approximation of Ci, and boundary region are defined as Eqs. (1), (2) and (3). _

(9)

To illustrate the process of criteria weights determination using Rough AHP, an example on on four criteria was introduced (This is only an example for method demonstration, and the results are not used for social vulnerability assessment in the research). And four experts’ comparison matrixes are shown as follows.

3. Method and data

Lower approximation: Apr (Ci ) = U {Y ∈ U / R (Y ) ≤ Ci}

⋯ r1km ⎤ ⋯ r2km ⎥ ⎥ ⋱ ⋮ ⎥ ⋯ 1 ⎥ ⎦

1, 1 5, 5, 3, 4 3, 3, 6, 3 4, 2, 5, 3 ⎤ 1/3, 1/4 1, 1, 1, 1 1/3, 1/3, 2, 1/2 1/2, 1/2, 4, 1/3⎥ 1/6, 1/3 3, 3, 1/2, 2 1, 1, 1, 1 3, 1/2, 3, 1/2 ⎥ ⎥ 1/5, 1/3 2, 2, 1/4, 3 1/3, 2, 1/3, 2 1, 1, 1, 1 ⎦ (15)

Second, we transformed the element rij in group decision matrix D into rough number form to obtain rough group decision-making matrix R. And rough number form RN (rij ) of rij can be get using Equations (1)(8), where rijkL and rijkU are the lower limit and upper limit of rough number RN (rijk ) in the kth pairwise comparison matrix, as shown in Eq. (16).

RN (rij ) = [rijkL, rijkU ]

However, rough number cannot effectively deal with group decisions, because it lacks an effective evaluation framework to cope with alternatives in relation to multiple criteria. Therefore, we proposed Rough Analytic Hierarchy Process (Rough AHP) by combining Rough

(16)

Thus, we can get rough sequence RN (rij ) , as shown in Equation (17).

RN (rij ) = {[rij1L, rij1U ], [rij2L, rij2U ], ⋯, [rijmL, rijmU ]} 3

(17)

Safety Science 125 (2020) 104625

X. Guo and N. Kapucu

Decision Maker

Comparison Matrixes

1

Decision Maker

D1

…

2

Decision Maker

…

D2

n

Dn

~ D

Group Comparision Matrix

~

W

Criteria Weights

Fig. 1. Factor Weights Determination Using Rough AHP.

And the average rough interval RN¯(rij ) can be obtained by using rough computation principles, as shown in Eqs. (18)-(20).

RN¯(rij ) = [rijL, rijU ]

rijL =

rijU =

Wi = [ m

(rij1L + rij2L+⋯+rijmL ) (19)

(rij1U + rij2U +⋯+rijmU ) (20)

m

Then, rough group decision matrix R can be get, as shown in Equation (21). U [r12L , r12 ] ⎡ [1, 1] ⎢ [r L , r U ] [1, 1] R = ⎢ 21 21 ⋮ ⋮ ⎢ ⎢[r L , r U ] [r L , r U ] m 1 m 1 m 2 m2 ⎣

⋯ [r1Lm, r1Um ]⎤ ⋯ [r2Lm, r2Um ]⎥ ⎥ ⋱ ⋮ ⎥ ⋯ [1, 1] ⎥ ⎦

¯ (Result )) + (1 − α )(Lim (Result )), where0 ≤ α ≤ 1 α (Lim _

(21)

_

1 1 (5 + 3 + 4 + 5) = 4.25Lim (4) = (4 + 3) = 3.5 _ 4 2

¯ (4) = 1 (5 + 4 + 5) = 4.67Lim (5) = 1 (3 + 4 + 5 + 5) = 4.25 Lim _ 3 4

¯ (5) = 1 (5 + 5) = 5 Lim 2 k k ). Thus, r12 can be expressed in the rough number form of RN (r12 1 2 RN (r12 ) = RN (r12 ) = RN (5) = [5.25, 5]

3 RN (r12 ) = RN (3) = [3, 4.25] 4 RN (r12 ) = RN (4) = [3.5, 4.67]

(24)

3.2. Data processing and social vulnerability scores calculation

According to Eqs. (18)–(20), the average rough interval of RN (r12) = [3.85, 4.73] can be obtained. Similarly, we can get rough number forms and average rough intervals for other elements in the group decision matrix. Therefore, the rough group comparison matrix can be get based on Eq. (15), as shown in Eq. (22).

[3.85, 4.73] [3.31, 4.31] [2.75, 4.25]⎤ ⎡ [1, 1] ⎢ [0.21, 0.29] [1, 1] [0.47, 1.19] [0.64, 2.52]⎥ R=⎢ ⎥ [0.26, 0.32] [1.52, 2.69] [1, 1] [1.29, 2.41]⎥ ⎢ ⎢[0, 27, 0.39] [1.21, 2.46] [0.76, 1.56] [1, 1] ⎥ ⎦ ⎣

(23)

If decision makers are optimistic, they can select α with a bigger value (α > 0.5). If they are pessimistic, a smaller value for α (α < 0.5) can be selected. If decision makers keep a realistic and moderate attitude, in other words, neither optimistic nor very pessimistic, they can select α as 0.5. Then, the value of the criteria weights can be transformed into crisp value. Take the criteria weights for example, w1 can be calculated as 0.5 × 0.8 + (1 − 0.5) × 1 = 0.9. So, w ' ={[0.8,1],[0.16,0.32],[0.28,0.39],[0.23,0.36]} can be transformed into crisp value w¨ '={0.9,0.24,0.34,0.3}. And its normalization form can be shown as w ' ={0.51,0.13,0.19,0.17}. Overall, based on the hierarchical structure, established by decomposing general decision objective into factors (Saaty, 1996), criteria weights can be determined using Rough AHP. Compared with conventional AHP, all decision makers’ preferences can be aggregated into group preferences using Rough AHP method, as shown in Fig. 1.

Take the element in r12 = [5, 5, 3, 4] as example to illustrate the rough number transformation process.

¯ (3) = Lim (3) = 3Lim

m

According to the Eq. (23), we can get the criteria weightw = {[2.43, 3.05], [0.5, 0.97], [0.84, 1.2], [0.71, 1.11]} . Then normalize w to obtain its normalization form w ' ={[0.8,1],[0.16,0.32], [0.28,0.39],[0.23,0.36]}. And each criterion’s overall weight can be calculated using the multiplication synthesis method from the top level to the bottom level. However, the weights are rough numbers containing the minimum and maximum values, leading to the difficulties in social vulnerability assessment. Based on Hurwicz principle (Arnold et al., 2002), we introduced the optimistic index α (0 ≤ α ≤ 1), which indicates experts’ risk propensities on the results in the research, to transform the rough weights into crisp value, as shown in Eq. (24).

(18)

m

m

∏i =1 rijL , m ∏i =1 rijU ], i = 1, 2, ⋯m

Based on the case of Hanzhong City, procedures for data processing and social vulnerability scores calculation in practice is presented as follows: Step 1: Considering data availability and regional characteristics, a hierarchy structure for social vulnerability assessment based on selected factors is developed by literature review. Step 2: We determined the criteria weights using Rough AHP. The researchers interviewed the advisory team including 8 local public mangers, 12 local emergency management officials, 23 local resident representatives and 8 earthquake experts. The criteria

(22)

Third, the rough weight Wi of each criterion in different hierarchies can be calculated using Eq. (23). 4

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4. Results and discussion

weights can be calculated using Rough AHP (see details in section Criteria Weights Determination Using Rough Analytic Hierarchy Process Method). Step 3: We calculated Social Vulnerability Scores (SVS) to distinguish different degrees of social vulnerability among counties (urban districts) in Hanzhong City. And data were collected from local census in 2017 (Hanzhong Statistical Bureau, 2017) on each county (urban district) according to criteria determined in Step 1.

4.1. Factors selection and hierarchy structure establishment As highlighted in the literature review, several factors contribute to social vulnerability, including rapid population growth, gender inequality, poor education, and type/density of infrastructure and lifelines (Cutter et al., 2015). In the research, we presented the factors including Socioeconomic Vulnerability, Population and Household Vulnerability, and Building Vulnerability. Sub-factors including annual income and capital assets (Cutter et al., 2015; Morrow, 1999), education (Cutter et al., 2003; Morrow, 1999) and employment (Mileti, 1999; Morrow, 1999; Redwood-Campbell and Abrahams, 2011) are introduced to measure socioeconomic vulnerability. Using sub-factors including age and family structure (Cutter et al., 2003; Ngo, 2001; Sanders et al., 2004) and disadvantaged populations (Cutter et al., 2003), we measure population and household vulnerability. Based on sub-factors including average age and height of buildings (Cutter et al., 2003), density of built environment, building vulnerability can be assessed. Some indicators, such as Average Value of Housing Units, are positively related to social vulnerability. That is, higher values of the indicators will improve social vulnerability. The other indicators, such as Per Capita Disposable Income, are negatively related to social vulnerability. And higher values of the indicators will decrease social vulnerability. In the research, “-” indicates that the indicator is negatively related to social vulnerability, while “+” indicates that the indicator is positively related to social vulnerability. We developed hierarchical structure of criteria, as shown in Table 1.

Each criterion was normalized to obtain a relatively uniform dimension. Criteria positively related to social vulnerability are transformed by Eq. (25), while the ones negatively related to social vulnerability are transformed by Eq. (26).

Si =

Xi − Xi − min Xi − max − Xi − min

(25)

Si =

Xi − max − Xi Xi − max − Xi − min

(26)

And Social Vulnerability Scores (SVS) on each county (urban district) can be calculated using Eq. (27), where Di is value of index for county (urban district) i, w¯j is the overall weight of criterion, and Mij is the standardized value of criterion j in county (urban district) i m

SVSi =

∑ w¯j Mij (27)

j=1

Step 4: We also executed sensitivity analysis to explore the changes of SVS and criteria weights under different optimistic index (α) (see details in (24)), with some implications and suggestions proposed.

4.2. Criteria weights determined using rough AHP As suggested by the expert team, we set α value as 0.5, and ask every expert to express his opinions though comparison matrix (see details in

Overall, the employed method applied in practice is shown in Fig. 2.

Hierarchy Structure Establishment

Aggregate Individual Preferences

Create Pairwise Comparison Matrix For Each Expert

Consistency Check

CI<0.1

Adjust Values

No

Yes Data from Local Census

Calculate the Factor Weights Using Rough AHP

Calculate Social Vulnerability Scores

Final Ranking and Decision Making

Sensitivity Analysis on Different Uncertainties Fig. 2. Method Employed in Practical Social Vulnerability Assessment. 5

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Table 1 Criteria for Social Vulnerability Assessment. Factor

Sub-factor

Indicator

Sign

Socioeconomic Vulnerability (V1)

Annual Income and Capital Assets (V11)

Per Capita Disposable Income (V111) Per Capita Capital Assets (V112) Percent of Households Earning Less Than 30,000 China Yuan (V113) Average Value of Housing Units (V114) Percent of Population Over 18 Years Old with No Middle School Diploma (V121) Percent of Population Having Completed at Least Four Years of Higher Education (V122) Rate of Unemployment (V131) Percent of Population Employed in Low-skilled Service Sector (V132) Percent of Population Employed in Primary Extractive Industry (V133) Percent of Population Employed in Health Care and Social Sectors (V134) Percent of Population over 60 Years Old (V211) Percent of Population under 6 Years Old (V212) Percent of Single-parent Headed Households (V213) Percent of the Population Living in Nursing Houses (V221) Percent of the Population Receiving Disability Pension (V222) Average Age of Buildings (V311) Average Height of Buildings (V312) Number of Housing Units Per Square Kilometers (V321) Number of Manufacturing Establishments Per Square Kilometers (V322) Number of Commercial Establishments Per Square Kilometers (V323) Value of Property and Agricultural Facilities Per Square Kilometers (V324)

– – + + +

Educational Level (V12)

Employment (V13)

Population and Household Vulnerability (V2)

Age and Family Structure (V21)

Disadvantaged Populations (V22) Building Vulnerability (V3)

Average Age and Height of Buildings (V31) Density of Built Environment (V32)

– + + + – + + + + + + + + + + +

Note: “−” indicates that the indicator is negatively related to social vulnerability, while “+” indicates that the indicator is positively related to social vulnerability. Table 2 Criteria Weights. Factor

V1

V2

V3

Rough Weight

[0.1737, 0.6297]

[0.2174, 0.4274]

[0.2158, 0.3360]

Sub-factor

Rough Weight

V11

[0.4865, 0.5173]

V12

[0.1899, 0.2585]

V13

[0.2413, 0.3101]

V21

[0.4401, 0.5601]

V22

[0.3566, 0.6432]

V31

[0.2201, 0.3329]

V32

[0.6126, 0.8344]

Indicator

V111 V112 V113 V114 V121 V122 V131 V132 V133 V134 V211 V212 V213 V221 V222 V311 V312 V321 V322 V323 V324

Rough Weight

[0.0047, [0.2071, [0.2272, [0.0300, [0.2900, [0.3932, [0.1217, [0.2878, [0.1465, [0.1004, [0.2080, [0.2247, [0.2573, [0.1873, [0.5169, [0.3748, [0.0358, [0.4259, [0.1059, [0.0098, [0.0250,

0.4124] 0.7029] 0.3283] 0.0769] 1.0000] 1.0000] 0.2371] 0.7635] 0.4485] 0.3531] 0.4240] 0.4432] 0.4536] 0.2128] 1.0000] 0.8118] 0.3782] 0.7307] 0.0578] 0.2044] 0.3249]

Overall Weight Rough Number Form

Crip Value (α = 0.5)

[0.0004, 0.1334] [0.0594, 0.0670] [0.0192, 0.1062] [0.0065, 0.0097] [0.0332, 0.0472] [0.0356, 0.0640] [0.0051, 0.0463] [0.0320,0.0562] [0.0188, 0.0286] [0.0148,0.0196] [0.0199, 0.1015] [0.0538, 0.0424] [0.0434, 0.0616] [0.0165, 0.0515] [0.1123, 0.1421] [0.0908,0.0178] [0.0017, 0.0423] [0.0966, 0.1194] [0.0162, 0.0140] [0.0013, 0.0573] [0.0033, 0.0911]

0.0669 0.0632 0.0627 0.0081 0.0402 0.0498 0.0257 0.0441 0.0237 0.0172 0.0607 0.0481 0.0525 0.0340 0.1272 0.0543 0.0220 0.1080 0.0151 0.0293 0.0472

Note: we introduced the optimistic index α which indicates experts’ risk propensities on the results in the research, to transform the rough weights into crisp value using Eq. (24) to facilitate data processing and social vulnerability scores calculation.

Equation (9)). Using Rough AHP, we integrated the preferences of all the experts and determined the criteria weights, as shown in Table 2.

Table 3 Social Vulnerability Value of Each County (Urban District).

4.3. Social vulnerability score calculation Values of indicators shown in Table 1 can be obtained from census data, e.g., technical criteria (age of buildings, infrastructure). And SVS of each county (urban district) can be calculated according to Eq. (27), with the results shown in Table 3. Fig. 3 displays social vulnerability indexes as a five-rank choropleth map, revealing SVS of different counties (urban districts). Furthermore, the top indicators contributing to social vulnerability were identified according to their weights, as shown in Table 4. 6

County (Urban District)

Social Vulnerability

Rank

Han-Tai District Nan-Zheng District Cheng-Gu County Yang County Xi-Xiang County Mian County Ning-Qiang County Lue-Yang County Zhen-Ba County Liu-Ba County Fo-Ping County

0.3328 0.3982 0.7353 0.8058 0.7898 0.9163 0.9712 0.9638 0.7516 0.6878 0.7493

11 10 8 4 5 3 1 2 6 9 7

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X. Guo and N. Kapucu

North 20 40 60 80km N

Fig. 3. Spatial Distribution of SVS at County (Urban District) level.

The results shown in Table 8 indicate that Ning-Qiang County, Mian County, and Lue-Yang County are the three counties (urban districts) which are most vulnerable to earthquake disasters no matter how α changes. This indicates that they should receive high priority for assistance. On the other hand, Han-Tai District and Nan-Zheng District are the least vulnerable to earthquake disasters. The reason might be that it is located in urban area, which is farther from risk prone area and has better anti-seismic infrastructure and facilities. The results also imply that an Urban-Rural Gap exists in the current Chinese earthquake disaster prevention system. Besides residing much better location (e.g., being farther from risk prone area), urban areas usually receive more attentions from authorities and get more resources for disaster mitigation or preparedness. But, it’s more difficult for some remote areas, especially mountainous areas, to get adequate resource for disaster management. According to results presented in Table 8, the top 10 criteria remain their top positions no matter how α changes, indicating the most important factors contributing to social vulnerability. The sub-factors can be divided into three categories, including Aging and Density of Buildings (V311, V321), Age and Family Structure (V211, V212, V213), Disadvantaged Populations (V222) and Socioeconomic Status (V111, V112, V113, V122), as shown in Table 9. Since many counties (urban districts) are located in mountainous area, Aging and Density of Buildings (V311, V321) is a main factor contributing to social vulnerability. That is, buildings located in mountainous area are much more susceptible to earthquake, causing more deaths or injuries. And social vulnerability is aggravated by this factor, although Hanzhong city is farther from seismic belt than other ones located in Sichuan province. On the other hand, Age and Family Structure (V211, V212, V213), Disadvantaged Populations (V222) are also identified as main factors impacting social vulnerability. The factor of age and family structure reveals significant difference among people in different age groups, especially facing earthquake risks. The aged in rural area of China always stay within their hometowns, and are cared for by younger adults. But, younger adults are more tend to immigrate to big cities for job and education opportunities, leaving their spouses, the elderly and children at home in rural areas. Disadvantaged populations are also vulnerable to earthquake disaster, due to their lack of mobility. Therefore, the

Table 4 The Top Indicators/Sub-factors in Each Dimension. Indicator

Weight

Rank

Per Capita Disposable Income (V111) Per Capita Capital Assets (V112) Percent of Households Earning Less Than 30,000 China Yuan (V113) Percent of Population Having Completed at Least Four Years of Higher Education (V122) Percent of Population over 60 Years Old (V211) Percent of Population under 6 Years Old (V212) Percent of Single-parent Headed Households (V213) Percent of the Population Receiving Disability Pension (V222) Average Age of Buildings (V311) Number of Housing Units Per Square Kilometers (V321)

0.0669 0.0632 0.0627

3 4 5

0.0498

9

0.0607 0.0481 0.0525 0.1272 0.0543 0.1080

6 10 8 1 7 2

4.4. Results of sensitivity analysis Sensitivity analysis was executed to analyze the influence of experts’ risk propensities on the assessment results. In the research, we introduced the optimistic index α (0 ≤ α ≤ 1) to transform the rough weights into crisp value, as shown in Eq. (24). If decision makers are optimistic, α will be set as 1. If they are pessimistic, α will be set as 0. If decision makers keep a realistic and moderate attitude, in other words, neither optimistic nor very pessimistic, α will be set as 0.5. The results of sensitivity analysis are shown in Table 5, Fig. 4 and Table 6.

4.5. Robustness analysis and further discussion The results of sensitivity analysis provides results under different experts’ risk propensities. On the other hand, the criteria weights and SVS of each county (urban district) is highly dependent on the value of optimistic index α (see details in Eq. (24)), which is set subjectively by decision makers. And even small changes of α may cause significant changes of criteria weights and SVS. Robustness analysis is required to find out how the criteria weights and SVS change if the optimistic index is changed. To achieve the analysis, we changed the value of α with 0.1 increment, and observed the criteria weights and SVS accordingly. The ranks of criteria weights and SVS are shown in Tables 7 and 8. 7

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Table 5 Social Vulnerability on Each County (Urban District) under Different Risk Propensities. α=0

County (Urban District)

Han-Tai District Nan-Zheng District Cheng-Gu County Yang County Xi-Xiang County Mian County Ning-Qiang County Lue-Yang County Zhen-Ba County Liu-Ba County Fo-Ping County

α = 0.5

Social Vulnerability

Rank

Social Vulnerability

Rank

Social Vulnerability

Rank

0.3806 0.3615 0.7164 0.8057 0.7726 0.9088 0.9614 0.8891 0.7217 0.6927 0.7329

10 11 8 4 5 2 1 3 7 9 6

0.3328 0.3982 0.7353 0.8058 0.7898 0.9163 0.9712 0.9638 0.7516 0.6878 0.7493

11 10 8 4 5 3 1 2 6 9 7

0.3917 0.4201 0.7413 0.7942 0.8103 0.9189 0.9821 0.9752 0.7689 0.7001 0.7809

11 10 8 5 4 3 1 2 7 9 6

Han-Tai District 1 Fo-Ping County

0.8

Į=0

Table 7 Rank of SVS on Each County (Urban District) under Different Optimistic Index (α).

Į=0.5

Nan-Zheng District

Į=1

County (Urban District)

0.6 Liu-Ba County

Cheng-Gu County

0.4 0.2

Yang County

Zhen-Ba County

Lue-Yang County

Xi-Xiang County Ning-Qiang County

Mian County

Fig. 4. Radar Chart for Social Vulnerability of Each County (Urban District) under Different Risk Propensities.

V111 V112 V113 V122 V211 V212 V213 V222 V311 V321

α = 0.5

α = 1.0

Sub-factor/Indicator

Direction of the Weight

Weights

Rank

Weights

Rank

Weights

Rank

0.0616 0.0619 0.0589 0.0488 0.0618 0.0471 0.0327 0.1301 0.0988 0.0987

5 4 6 9 7 8 10 1 2 3

0.0669 0.0632 0.0627 0.0498 0.0607 0.0481 0.0525 0.1272 0.0543 0.1080

3 4 5 9 6 10 8 1 7 2

0.1112 0.0514 0.0419 0.0407 0.0508 0.0461 0.0409 0.1101 0.0418 0.0981

1 4 7 8 5 6 10 2 9 3

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

10 11 8 4 5 2 1 3 7 9 6

10 11 8 4 5 2 1 3 6 9 7

10 11 8 4 5 2 1 3 7 9 6

9 11 8 4 5 3 1 2 6 10 7

11 9 8 4 5 3 1 2 6 10 7

11 10 8 4 5 3 1 2 6 9 7

11 10 8 4 5 3 1 2 6 9 7

11 10 9 5 4 3 1 2 7 8 6

10 11 8 5 4 3 1 2 7 9 6

11 10 9 5 4 3 1 2 7 8 6

11 10 8 5 4 3 1 2 7 9 6

Table 8 Rank of Sub-factor/Indicator Weights under Different Optimistic Index (α).

Table 6 Criteria Weights and Rank under Different Risk Propensities. α=0

Value of Optimistic Index (α)

Han-Tai District Nan-Zheng District Cheng-Gu County Yang County Xi-Xiang County Mian County Ning-Qiang County Lue-Yang County Zhen-Ba County Liu-Ba County Fo-Ping County

0

Sub-factor/ Indicator

α = 1.0

V111 V112 V113 V122 V211 V212 V213 V222 V311 V321

↑ N/A N/A ↑ ↑ N/A N/A ↓ ↓ N/A

Value of Optimistic Index (α) 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

5 4 6 9 7 8 10 1 2 3

5 4 6 9 7 8 10 1 2 3

4 5 6 10 7 8 9 1 3 2

3 5 6 10 4 9 8 1 7 2

4 3 5 9 6 10 8 1 7 2

3 4 5 9 6 10 8 1 7 2

3 4 5 10 7 6 8 1 9 2

1 4 5 10 8 6 9 2 7 3

1 4 5 10 6 7 9 2 8 3

1 4 6 9 5 7 10 2 8 3

1 4 7 8 5 6 10 2 9 3

Note: Due to limited space, we only listed top 10 criteria.

place provides the everyday benefits for subsistence, jobs, and economic opportunities they cannot get in other places easily. Various disaster resettlement program have been implemented during recovery from Wenchuan Earthquake. Government suggests (or even enforce) evacuation or emigration from hazardous places, changing the resettled people’s livelihood modes. Sub-factors including per capita disposable income, per capita capital assets, percent of households earning less than 30,000 China Yuan and percent of population having completed at least four years, which are essential to maintain livelihood after earthquake, are highlighted. Social vulnerability is affected by different factors, which are distributed in some different areas, including local environments, population, and household livelihood. And Wenchuan Earthquake “triggered” a series of events affecting social vulnerability directly or indirectly. Since 2008, when Wenchuan Earthquake occurred, most of

The symbol ↑ indicates that the weight increases with increment of α. The symbol ↓ indicates that the weight decreases with increment of α. Due to limited space, we only listed top 10 criteria.

population living in this area (the aged, children and disadvantaged population) are less mobile and do not have enough capacity to reduce disaster risk or recover from disasters. Thus, “Empty-Nest” elderly, “Stay-at-Home” children and disadvantaged population, are important factors increasing social vulnerability. Socioeconomic Status (V111, V112, V113, V122) was selected as another important factor. Although living in mountainous area, which is vulnerable to earthquake, people “discount” the risk in order to get the day-to-day benefits of livelihood. For them, the value of benefits in hazard area outweighs the potential earthquake risks. Even if they might lose homes or lives in an earthquake prone areas, living in that 8

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Table 9 Main Factors Attributing to Social Vulnerability. Category

Sub-factors

Aging and Density of Buildings

Average Age of Buildings (V311) Number of Housing Units Per Square Kilometers (V321) Percent of Population over 60 Years Old (V211) Percent of Population under 6 Years Old (V212) Percent of Single-parent Headed Households (V213) Percent of the Population Receiving Disability Pension (V222) Per Capita Disposable Income (V111) Per Capita Capital Assets (V112) Percent of Households Earning Less Than 30,000 China Yuan (V113) Percent of Population Having Completed at Least Four Years of Higher Education (V122)

Age and Family Structure

Disadvantaged Populations Socioeconomic Status

the capital have been invested in infrastructure construction, such as disaster resettlement community development, and highway construction to increase coping capacity significantly. On the other hand, some other challenging problems, such as household livelihoods, population/ family structure, are still lack of necessary resources investment and have not been solved effectively. That might be one of the reasons why social vulnerability indexes still remains high in some counties even after 10-year disaster recovery efforts.

Birkmann, J., Bach, C., Vollmer, M., 2012. Tools for resilience building and adaptive spatial governance: challenges for spatial and urban planning in dealing with vulnerability. Raumforsch Roumordn 70 (4), 293–308. Chen, W., Cutter, S.L., Emrich, C.T., Shi, P., 2013. Measuring social vulnerability to natural hazards in the Yangtze river delta region, China. Int. J. Disaster Risk Sci. 4 (4), 169–181. Chong, X.U., Dai, F., Beijing, 2010. Wenchuan earthquake-induced landslides: an overview. Geol. Rev., vol. 56, 6, pp. 860–874. Clark, G.E., Moser, S.C., Ratick, S.J., Dow, K., Meyer, W.B., Emani, S., et al., 1998. Assessing the vulnerability of coastal communities to extreme storms: the case of revere, MA. USA. Mitig. Adapt. Strat. Glob. Change 3 (1), 59–82. Comfort, L.K., 2019. The Dynamics of Risk: Changing Technologies and Collective Action in Seismic Events. Princeton University Press, Princeton, NJ. Cutter, S.L., 2003. The vulnerability of science and the science of vulnerability. Ann. Assoc. Am. Geogr. 93 (1), 1–12. Cutter, S.L., Boruff, B.J., Shirley, W.L., 2003. Social vulnerability to environmental hazards. Soc. Sci. Quart. 84 (2), 242–261. Cutter, S., Emrich, C., Mitchell, J., Boruff, B., Gall, M., Schmidtlein, M., Burton, C., Melton, G., 2006. The long road home–race, class, and recovery from Hurricane Katrina. Environment 48 (2), 8–20. Cutter, S.L., Emrich, C.T., Webb, J.J., Morath, D., 2009. Social vulnerability to climate variability hazards: a review of the literature. Final report to Oxfam America < http://adapt.oxfamamerica.org/resources/Literature_Review.pdf > . Retrieved on 10 June 2011. Cutter, S.L., Finch, C., 2008. Temporal and spatial changes in social vulnerability to natural hazards. PNAS 105 (7), 2301–2306. Cutter, S.L., Mitchell, J.T., Scott, M.S., 2015. Revealing the vulnerability of people and places: a case study of Georgetown County, South Carolina. Ann. Assoc. Am. Geogr. 90 (4), 713–737. Cutter, S.L., Scott, M.M.S., 2000. Revealing the vulnerability of people and places: a case study of Georgetown county, South Carolina. Ann. Assoc. Am. Geogr. 90 (4), 713–737. Engle, N.L., Lemos, M.C., 2010. Unpacking governance: building adaptive capacity to climate change of river basins in Brazil. Global Environ. Change 20 (1), 4–13. Eric, T., 2013. Uncertainty analysis for a social vulnerability index. Ann. Assoc. Am. Geogr. 103 (3), 526–543. Fatemi, F., Ardalan, A., Aguirre, B., Mansouri, N., Mohammadfam, I., 2016. Social vulnerability indicators in disasters: findings from a systematic review. Int. J. Disaster Risk Reduct. 22, 219–227. Fatemi, F., Ardalan, A., Aguirre, B.E., Mansouri, N., Mohammadfam, I., 2017. Constructing the indicators of assessing human vulnerability to industrial chemical accidents: a consensus-based fuzzy delphi and fuzzy AHP approach. Plos Curr. 9. Ghobadi, M.H., Taheri, Milad, Taheri, Kamal, 2017. Municipal solid waste landfill siting by using analytical hierarchy process (AHP) and a proposed karst vulnerability index in Ravansar county, west of Iran. Environ. Earth Sci. 76 (2), 68. Granger, K., Jones, T., Leiba, M., Scott, G., 1999. Community risk in cairns: a multihazard risk assessment. Austral. J. Emerg. Manage. 14 (2), 25–26. Greco, S., Matarazzo, B., Slowinski, R., 2001. Rough set theory for multicriteria decision analysis. Eur. J. Oper. Res. 129 (1), 1–47. Guo, X., Kapucu, N., 2018. Social Vulnerability Evaluation for Ankang City, China, using Fuzzy Analytic Hierarchy Process Method. J. Homel. Secur. Emerg. Manage. https:// doi.org/10.1515/jhsem-2016-0037. Hanzhong Statistical Bureau, 2017. Hanzhong Local Statistical Yearbook, 2017. < http:// tjj.hanzhong.gov.cn/ > . Retrieved on 28 April, 2018. Hao, D., Shifei, D., Lihua, H., 2010. Research progress of attribute reduction based on rough set. Comput. Eng. Sci. 6, 92–94. Hewitt, K., 1997. Regions of Risk—a Geographical Introduction to Disasters. Singapore: Longman. Birkmann, Joern, 2007. Risk and vulnerability indicators at different scales: applicability, usefulness and policy implications. Global Environ. Change Part B Environ. Hazards 7 (1), 20–31. Kapucu, N., Ozerdem, A., 2013. Managing Emergencies and Crises. Jones & Bartlett Publishers, Boston, MA. Khamespanah, F., Delavar, M.R., Moradi, M., Sheikhian, H., 2016. A GIS-based multicriteria evaluation framework for uncertainty reduction in earthquake disaster management using granular computing. Geodesy Cartogr. 42 (2), 58–68. Koks, E.E., Jongman, B., Husby, T.G., Botzen, W.J.W., 2015. Combining hazard, exposure

5. Conclusion In this paper, a Rough AHP method for social vulnerability assessment was proposed and applied at county (urban district) level in Hanzhong City, China. Based on the proposed criteria, we determined the criteria weights and calculated social vulnerability indexes using Rough AHP. The results showed the spatial distribution of social vulnerability in Hanzhong city. Moreover, some key factors contributing to local social vulnerability were derived and discussed based on the results of sensitivity and robustness analysis to facilitate informed decision on earthquake disaster prevention. Methodologically, this social vulnerability assessment model provides relevant information for scholars and practitioner of emergency management. The validation process can also contribute to future scholarship in vulnerability assessment. A comparative study, based on the model presented in the research, within China and other country context can also be beneficial. The case study shows advantages of the proposed Rough AHP method and its applicability in social vulnerability assessment. However, there are some limitations of the study. First, the factors contributing to social vulnerability are assumed to be independent (no correlation) based on the Rough AHP method, with discussions on the interdependency among influential factors avoided. Thus, it is clear that additional model refinement is required to better understand the correlations among factors methodologically. The Analytic Network Process, a generalization of AHP with feed-backs to adjust weights, may be a solution. Second, we collected data from open statistical yearbook, and were unable to access all the official information due to the administrative concerns of confidentiality. Acknowledgements This research was sponsored by the National Social Science Fund of China (Grant Number: 16BZZ052). References Arias, P.A., Villegas, J.C., Machado, J., Serna, Ángelica M., Vidal, L.M., Vieira, C., 2016. Reducing social vulnerability to environmental change: building trust through social collaboration on environmental monitoring. Weather Clim. Soc. 8 (1), 57–76. Arnold, B.F., Größl, I., Stahlecker, P., 2002. The minimax, the minimum, and the hurwicz adjustment principle. Theor. Decis. 52 (3), 233–260. Box, P., Bird, D., Haynes, K., King, D., 2016. Shared responsibility and social vulnerability in the 2011 Brisbane flood. Nat. Hazards 81 (3), 1549–1568.

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Safety Science 125 (2020) 104625

X. Guo and N. Kapucu and social vulnerability to provide lessons for flood risk management. Environ. Sci. Policy 47, 42–52. Koks, E.E., Moel, H.D., Aerts, J.C.J.H., Bouwer, L.M., 2014. Effect of spatial adaptation measures on flood risk: study of coastal floods in Belgium. Reg. Environ. Change 14 (1), 413–425. Kuhlicke, C., Tapsell, S., Steinführer, A., Marchi, B.D., 2011. Contextualizing social vulnerability: findings from case studies across europe. Nat. Hazards 58 (2), 789–810. Lin, C.T., Lee, C., Chen, W.Y., 2009. Using fuzzy analytic hierarchy process to evaluate service performance of a travel intermediary. Serv. Ind. J. 29 (3), 281–296. Lin, L., Pussella, Pgrni, 2017. Assessment of vulnerability for coastal erosion with GIS and AHP techniques case study: southern coastline of Sri Lanka. Nat. Resour. Model. 30 (4), 1–12. Liu, H.C., Liu, L., Liu, N., Mao, L.X., 2012. Risk evaluation in failure mode and effects analysis with extended VIKOR method under fuzzy environment. Exp. Syst. Appl. 39 (17), 12926–12934. Mileti, D., 1999. Disasters by Design: A Reassessment of Natural Hazards in the United States. Joseph Henry Press, Washington, DC. Morrow, B.H., 1999. Identifying and mapping community vulnerability. Disasters 23 (1), 1–18. Ngo, E.B., 2001. When disasters and age collide: reviewing vulnerability of the elderly. Nat. Hazard. Rev. 2 (2), 80–89. Nie, C., Yang, L., Li, H., 2012. Macro assessment of seismic population vulnerability in China. Prog. Geogr. 31 (3), 375–382. Opricovic, S., Tzeng, G.H., 2004. Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur. J. Oper. Res. 156 (2), 445–455. Pawlak, Z., 1982. Rough sets. Int. J. Comput. Inform. Sci. 11 (5), 341–356. Redwood-Campbell, L., Abrahams, J., 2011. Primary health care and disasters-the current state of the literature: what we know, gaps and next steps. Prehospital Disaster Med. 26 (3), 184. Rufat, S., Tate, E., Burton, C.G., Maroof, A.S., 2015. Social vulnerability to floods: review of case studies and implications for measurement. Int. J. Disaster Risk Reduct. 14, 470–486. Russo, R.D.F.S.M., Camanho, R., 2015. Criteria in AHP: a systematic review of literature. Proc. Comput. Sci. 55, 1123–1132. Rygel, L., O’Sullivan, D., Yarnal, B., 2006. A method for constructing a social vulnerability index: an application to hurricane storm surges in a developed country. Mitig. Adapt. Strat. Glob. Change 11 (3), 741–764. Saaty, T.L., 1977. A scaling method for priorities in hierarchical structures. J. Math. Psychol. 15 (3), 234–281. Saaty, T.L., 1996. The Analytic Network Process: Decision Making with Dependence and Feedback. RWS Publications, Pittsburgh. Sakawa, M., Kato, K., 2015. A consensus model for group decision-making problems with subjective linguistic preference relations. Fuzzy Sets Syst. 97 (1), 19–31. Sanders, S., Boy, S.L., Bowie, Y.D., 2004. Lessons learned on forced relocation of older adults: the impact of Hurricane Andrew on health, mental health, and social support of public housing residents. J. Gerontol. Soc. Work 40 (4), 23–35. Tate, E., 2012. Social vulnerability indices: a comparative assessment using uncertainty

and sensitivity analysis. Nat. Hazards 63 (2), 325–347. Wang, Y., Zhang, B., Wang, L.Y., Cheng, L.H., 2010. Earthquake damage analysis of rural buildings in Hanzhong by the Wenchuan great earthquake. J. Shaanxi Univ. Technol. 32 (6), 116–118 in Chinese. Weichselgartner, Juergen, 2001. Disaster mitigation: the concept of vulnerability revisited. Disaster Prevent. Manage., vol. 10, 2, pp. 85–95. Wisner, B., Uitto, J., 2009. Life on the Edge: Urban Social Vulnerability and Decentralized, Citizen-Based Disaster Risk Reduction in Four Large Cities of the Pacific Rim. Facing Global Environmental Change. Berlin: Springer Berlin Heidelberg. Xu, C., Dai, F.C., Xu, X.W., 2010. Wenchuan earthquake-induced landslides: an overview. Geol. Rev. 56 (6), 860–874. Xu, X., Zhang, W., Chen, X., 2017. Social vulnerability assessment of earthquake disaster based on the catastrophe progression method: a Sichuan province case study. Int. J. Disaster Risk Reduct. 24, 361–372. Yi, L., Ke, C., Cao, X., Sun, Y., Cheng, X., Ye, H., 2017. Analysis of social vulnerability of residential community to hazards in Tianjin, China. Nat. Hazards 87 (2), 1223–1243. Yin, Y.P., Wang, F., Ping, S., 2009. Landslide hazards triggered by the 2008 Wenchuan earthquake, Sichuan, China. Landslides 6 (2), 139–152. Zebardast, E., 2013. Constructing a social vulnerability index to earthquake hazards using a hybrid factor analysis and analytic network process (F’ANP) model. Nat. Hazards 65 (3), 1331–1359. Zeng, J., Zhu, Z.Y., Zhang, J.L., Ouyang, T.P., Qiu, S.F., Zou, Y., et al., 2012. Social vulnerability assessment of natural hazards on county-scale using high spatial resolution satellite imagery: a case study in the Luogang district of Guangzhou, South China. Environ. Earth Sci. 65 (1), 173–182. Zhai, L.Y., Khoo, L.P., Zhong, Z.W., 2009. A rough set based QFD approach to the management of imprecise design information in product development. Adv. Eng. Inf. 23 (2), 222–228. Zhang, J.S., 2010. The study on assessment index of urban social vulnerability to earthquake disaster. Guide Sci-tech Magaz. 36, 12–14. Xuesong Guo, Ph.D., is professor of School of Public Policy and Administration, Xi’an Jiaotong University, China. His main research area is emergency management and risk analysis. He teaches operation research, emergency management, and quantitative research methods. He can be reached at [email protected]. Naim Kapucu, Ph.D., is Pegasus professor of public administration and policy and director of the School of Public Administration at the University of Central Florida (UCF). His main research interests are network governance, emergency and crisis management, leadership, decision-making in complex environments, social inquiry and public policy. His work has been published in Public Administration Review, Administration & Society, Journal of Public Administration Research and Theory, the American Review of Public Administration, and Public Management Review, among others. He teaches network governance, leadership in public service, and methodology courses. He can be reached at [email protected].

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