Improved Tools for Disaster Risk Mitigation in

ITERATE

Deliverable F.4

Social vulnerability model results and map

ECHO/SUB/2016/740181/PREV23 – ITERATE – Improved Tools for Disaster Risk Mitigation in Algeria Project co-funded by ECHO – Humanitarian Aid and Civil Protection

Author IUSS Pavia Al Mouayed Bellah Nafeh Andres Abarca Ricardo Monteiro Mario Martina Date December 2018

Review FEUP Smail Kechidi Jose Miguel Castro Date April 2018

ECHO/SUB/2016/740181/PREV23 – ITERATE – Improved Tools for Disaster Risk Mitigation in Algeria Project co-funded by ECHO – Humanitarian Aid and Civil Protection

Table of Contents 1. INTRODUCTION ...... 1 2. Methodology ...... 2 2.1. Social Vulnerability Index...... 2 2.2. SCORECARD Approach ...... 2 2.3. Hybrid Approach: Composite Social Vulnerability Index ...... 2 3. ASSESSMENT ...... 3 3.1. Social Vulnerability Index (SoVI) ...... 3 3.1.1. Population...... 3 3.1.2. Education ...... 4 3.1.3. Economy ...... 4 3.1.4. Health ...... 5 3.1.5. Infrastructure...... 5 3.1.6. Habitat ...... 6 3.1.7. Governance and Institutional Capacity ...... 6 3.1.8. Summary of SoVI Analysis...... 6 3.2. SCORECARD-Based Assessment ...... 7 4. Average vulnerability score ...... 11 5. CONCLUSION ...... 14 6. Bibliography ...... 14

ECHO/SUB/2016/740181/PREV23 – ITERATE – Improved Tools for Disaster Risk Mitigation in Algeria Project co-funded by ECHO – Humanitarian Aid and Civil Protection

1. INTRODUCTION

The overall seismic risk of a population depends on three factors: hazard, exposure of assets and communities and vulnerability: physical and social. Hazard and exposure are harder to act upon, but vulnerability can be significantly reduced if properly characterized. On the other hand, the social effects are usually largelly ignored when performing seismic risk assesment, mainly because of the difficulty to quantify the human dimensions within a hazard zone and the scarcity of readility available census data. Quantification of the human dimension (through census data) within a hazard zone aids decision-makers and the exposed society itself to cope with the results of a disastrous event. Social vulnerability assessment is the evaluation of the differential impacts that several communities may present under similar levels of hazard, based on the conditions of each community’s social fabric, which includes pre-existing socioeconomic characteristics related to the capacity of groups to prepare for, respond to, and recover from damaging events (Burton & Silva, 2016).

In order to quantify these effects objectivelly, a methodology has been defined in Deliverable F.2, merging concepts from two previously defined techniches, which is based on the construction of a composite index using a comparative analysis of census based data (Deliverable F.1) and results from participatory engagement with the population through the use of a questionnaire at the province administrative level as well as utilizing the feedback from the Reslience Performance Scorecard Method (Burton, Khazai, Anhorn, & Contreras, 2017), detailed in Deliverable F.3. The current report presents the computation of the composite social vulnerability index and assigning an average vulnerability score for the geographic level under assessment by reverting to the chosen indicators and the results of the questionnaire data. The computation of a comparative resilience of a group due to socio-economic parameters is conducted, with the identification of conditions that make people or places vulnerable to extreme natural events.

Figure 1: Integration of potential hazards, exposures and societal resilience for seismic risk assessment

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2. METHODOLOGY

2.1. Social Vulnerability Index

A set of socio-economic independent variables are chosen and sub-categorized into groups. Variables are collected from census data at the desired geographical level. Furthermore, the indicators are normalized and processed using factor analysis to determine the significance of each in its corresponding group, measured in the form of constants or factor loadings.The sum-product of each variable for every desired geographical level times the determined factor loading within a group is termed the factor score (Cutter, Mitchell & Scott, 2000). Every social dimension is quantified by these factor scores (sub-indexes), placed in an additive model to produce the composite social vulnerability index score (SoVI) (Cutter, Boruff & Shirley, 2003). SoVI is a relative metric, that generates scores dependent on the overall geographic scale of reference and boundary level chosen for the analysis.

2.2. SCORECARD Approach

The Resilience Performance Scorecard Methodology RPS is a multi-level, multi-scale evaluation tool (Burton, Khazai, Anhorn & Contreras 2017). RPS empowers stakeholders to assess earthquake risk and resilience parameters based on qualitative information. RPS engages the exposed group or communities leading to an increase of awareness and identification of key gaps at the community and institutional level within the boundary level. Target surveys are designed accordingly to address the reality of the society under evaluation and applied to both community members and local administration or institutional decision-makers in the risk management field. Key areas addressed: social capacity, awareness and advocacy, legal and institutional arrangements, planning and regulation, critical infrastructure and services and emergency preparedness and response.

2.3. Hybrid Approach: Composite Social Vulnerability Index

In the case of ITERATE, none of the two methodologies alone was considered ideal to accurately assess the social vulnerability of the Algerian Population. SoVI approach requires census information not entirely available whereas RPS approach does not provide a measure of risk that can be directly integrated with the physical risk. A hybrid methodology was thus adopted combining both methodologies with the readily available census data and collected information from the questionnaires. The dimensions of the RPS were scored and treated as a sub-category to the SoVI framework, thus obtaining a composite vulnerability index. The methodology for incorporating the population’s feedback to the SCORECARD approach with the available census data is summarized in Figure 2 below.

2 ECHO/SUB/2016/740181/PREV23 – ITERATE – Improved Tools for Disaster Risk Mitigation in Algeria Project co-funded by ECHO – Humanitarian Aid and Civil Protection

Figure 2: Schematic representation of Social Vulnerability Assessment Methodology adopted for ITERATE

3. ASSESSMENT

3.1. Social Vulnerability Index (SoVI) The census-based indicators for the province of used for the computation of the index form a set of 33 variables grouped into 7 main groups: Population, Education, Economy, Health, Infrastructure, Habitat, Governance and Institutional Capacity. Raw census data was collected, standardized and processed accordingly and transformed into a comparable scale by employing the MIN-MAX normalization (0 being the least socially vulnerable and 1 being the most vulnerable).

� − ���(� ) � = ���� − ���(� ) Unavailable datasets were still estimated using a “reference country” approach (e.g. knowing the indicator value for the province of Blida – the case-study region – given no other information regarding other provinces, the data could be normalized with reference to countries of geographical or socio-economical similarities). Other datasets were derived from “proxy” indicators whose correlation with the unknown parameter is high (rule of thumb: � > 0.5 or � < -0.5).

3.1.1. Population The population factor uses demographic attributes to predict the vulnerability of populations to earthquake risk and to recover from disastrous events when they occur. Population groups that are considered the most vulnerable include elders, very young, disabled individuals, population of foreign nationalities, refugees, etc. Additionally, it is generally assumed that women can have a more difficult time during recovery than men, often due to sector-specific employment, lower wages, and family care responsibilities (Cutter, Boruff, & Shirley, Social Vulnerability to Environmental Hazards, 2003).Variables for data not readily available were substituted with proxies from the national scale. The selected indicators chosen to be representative of the population related vulnerability are listed in Table 1.

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Table 1: Population related indicators for SoVI analysis and Vulnerability Score Population Categories Percentage < 18 Years 37.92% > 65 Years 4.92% Disabled 5.15% Refugee Camps 0.22% Population of Foreign Nationalities 0.66% Vulnerability Score 2.56

3.1.2. Education A strong link between education and a community’s ability to cope with disasters or conflict exists, as highlighted by UNESCO and UNICEF (2015). The education factor is determined by the assumption that higher levels of education are linked to greater earnings over lifetime and a greater access to information. Additionally, it is assumed that populations with lower education have a more difficult time understanding warning information and finding recovery aid after a disaster. Thus, continuous contact with the most educated levels of the society leads to better informed population and even to a higher level of preparedness. The selected indicators along with the vulnerability score attributed to education chosen to be most representative are listed in Table 2.

Table 2: Education related indicators for SoVI analysis and Vulnerability Score Education Categories Percentage Score Vulnerability Illiterate 17.20% 5 0.85 Without elementary education 16.00% 3.75 0.49 Without high school education 35.00% 2.5 1.52 High school education 24.20% 1.25 0.71 Graduated 7.60% 0 0.00 Total 3.57

3.1.3. Economy The economic well-being of a population has a great impact in its ability to absorb losses and enhance resilience to hazard impacts. Wealth enables communities to absorb and recover from losses more quickly due to insurance, social safety systems, and entitlement programs. Income parameters are complemented with the type of employment that is present in a community, since some lines of labour may be compromised after a disaster. For example, low-skilled service workers (housekeeping, childcare, and gardening) may be affected, as disposable income fades and the need for services declines. The selected indicators chosen to be representative of the economic related vulnerability are listed in Table 3.

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Table 3: Economy related indicators for SoVI analysis and Vulnerability Score Economy Categories Value Vulnerability Unemployment 2.44% 1.25 Women Labour Force 13.30% 3.95 Average Monthly Income 345 USD 1.95 Poverty 6.79% 0.7 % Labour Force in Service Industries 30.37% 1.44 % Labour Force in Secondary Sectors 16.07% 1.14 Total (Average) 1.83

3.1.4. Health Health care services and providers such as physicians, nursing homes and hospitals, are fundamental in post-event relief. The lack of adequate and sufficient medical services will compromise immediate response and longer-term recovery from disasters. The overall quality of the services is represented by the amount of healthcare facilities, capacities and personnel, as well as the life expectancy and the percentage of the population covered by basic social security healthcare services. The health-related indicators for the social vulnerability of Blida are summarized in Table 4.

Table 4: Health related indicators for SoVI analysis and Vulnerability Score Health Categories Value Vulnerability N. of Hospitals (per 1000 population) 0.067 4.81 N. of Beds (per 1000 population) 2.61 2.86 N. of Doctors (per 1000 population) 1.49 0.74 Crude Birth Rate (per 1000 population) 24.44 1.52 Crude Death Rate (per 1000 population) 3.87 1.41 Life Expectancy at Birth 67.3 years 4.50 % with Basic Health Insurance 100% 0.00 Total (Average) 2.26

3.1.5. Infrastructure Access to utilities and amenities such as piped water and electricity, as well as having multiple road access lines available are assumed to improve the post disaster response and therefore reduce vulnerability. The infrastructure indicator category is summarized below in Table 5 along with the respective vulnerability scores and its average.

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Table 5: Infrastructure related indicators for SoVI analysis and Vulnerability Score Infrastructure Categories Value Vulnerability Road Network (in km/km2) 0.85 0.50 % Population without Water 4.84% 0.35 % Population without Electricity 0.18% 0.01 Vehicles (per person) 0.10 4.61 Total (Average) 1.19

3.1.6. Habitat The habitat in which communities live, in terms of its population and built environment density can play a major role in the outcome of a disaster. Urban areas with high densities have a greater requirement for resources and often the process of evacuation out of harm’s way may be more complicated. Additionally, the size and ownership of residential ownership is assumed to be an indicator for families who have financial stability and are rooted in the locality. The aforementioned indicators are illustrated in Table 6.

Table 6: Habitat related indicators for SoVI analysis and Vulnerability Score Habitat Categories Value Vulnerability Density (pp/km2) 636.00 0.87 Housing Density (pp/house) 6.70 2.59 Urban Density (No. buildings/km2) 3.77 2.05 Number of Households (per province population) 0.15 1.01 Total (Average) 1.63

3.1.7. Governance and Institutional Capacity This indicator category captures the capacity of communities to engage in organizational linkages, to reduce risk through mitigation and planning. Variables such as abstention rates for elections is used as a measure for citizen participation and community engagement in risk reduction. Crime rate is assumed to be an indicator of the ability of communities to protect social and organizational systems. The assumed indicators are summarized in Table 7.

Table 7: Governance and Institutional Capacity related indicators for SoVI analysis and Vulnerability Score Governance and Institutional Capacity Categories Value Vulnerability Crime Rate (over 120) 47.8 1.99 Abstention Rate 51.88% 2.59 Total (Average) 2.29

3.1.8. Summary of SoVI Analysis The results of the previous sections (3.1.1 to 3.1.7) are summarized and illustrated in Figure 3 below. The average unweighted social vulnerability score is calculated to be 2.19 on a scale of 0 to 5. From the indicators listed above, it is observed that the economic, habitat and infrastructure sectors are

6 ECHO/SUB/2016/740181/PREV23 – ITERATE – Improved Tools for Disaster Risk Mitigation in Algeria Project co-funded by ECHO – Humanitarian Aid and Civil Protection performing well when compared to the rest, with both health and governance sectors around the marginal value considering the health coverage system and level of involvement of the population in political events. The education and population sectors however demonstrated the highest vulnerability score due to reasons such as the illiteracy rate (17.20 %), achieved level of education (population without elementary and secondary education level respectively at 16 and 35%) and amount of considerably vulnerable population groups listed at 48.8% of the total population.

5.00

4.00

3.00

2.00 Vulnerability Score Vulnerability

1.00

0.00

Indicators Figure 3: Social Vulnerability Scores per Indicator

3.2. SCORECARD-Based Assessment

As mentioned in section 2.2, the scorecard approach or RPS captures the key functional and organizational areas of opportunity for urban resilience enhancement with local government officials (i.e. the targeted decision-making stakeholders). Participatory scheme engagement with stakeholders for the design of the indicators (questions) and targets (answer schemes) is required. A questionnaire was organized in six sections representing a resilience dimension in agreement with the strategic goals of the Sendai Framework of the United Nations Office for Disaster Risk Reduction. (Aitsi- Selmi, Egawa, Sasaki, Wannous, & Murray, 2015). Results are processed based on frequency, disaggregated based on community to understand the difference in perception and then grouped into one indicator “awareness” to be integrated within the classic SoVI approach and thus forming the hybrid approach mentioned in section 2.3. Each of the questions and eligible answers has been tailored to the Algerian context and has been developed and reviewed in cooperation with the Algerian team members of the project to assure that the overall questionnaire captures the reality of the communities that it attempts to characterize (see Deliverable DF.3). The indicator classes within the survey, their dimensions and the grading scheme are summarized in Table 8.

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Table 8: SCORECARD Approach Indicators Dimensions General Questions Grading Scheme Awareness & Advocacy Level of awareness and knowledge • 1 = Almost None (AA) of earthquake disaster risk. (Little or no Social Capacity (SC) The capacities of the population to awareness) efficiently prepare, respond and • 2 = Low (Awareness recover from a damaging of needs) earthquake. • 3 = Moderate (Engagement and Legal and Institutional Efficiency of mechanisms commitment) Arrangements (LIA) advocating earthquake risk • 4 = High (Full reduction in nearby quarters. integration) Planning, regulation and Perceived level of commitment and • 5 = Not applicable mainstreaming risk mainstreaming of DRR through (problem with the mitigation (PRMRM) regulatory planning tools. question or the target) Emergency Preparedness, Level of effectiveness and Response and Recovery competency of disaster (EPRR) management including mechanisms for response and recovery. Critical Services and Level of resilience of critical Public Infrastructure services to disasters. Resiliency (CSPIR)

In order to have a better understanding of the awareness of the population towards risk and to be able to compute it within a more comprehensive analysis, it was decided to consider an additional indicator named “Awareness”. This indicator is based on the results of the questionnaires distributed to the population during the social vulnerability assessment carried out when implementing the SCORECARD approach (Cerchiello et al., 2017; 2018). Integration of the results of the SoVI and the SCORECARD approaches has been undertaken, as the level of awareness is considered pivotal in any social vulnerability study.

Based on the feedbacks of the survey, the assumption of splitting Blida into two representative geographical areas (Figure 5) was done considering the following reasons: survey participants (82), of which residents of the Blida province (66), of which are disaggregated into residents of the Blida municipality (28) and adjacent municipalities (38) and illustrated in Figure 4 below.

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Figure 4: Province of Blida Map showing the location of the questionnaire participants

Figure 5: Blida Province and the assumed geographical setting (Yellow: municipality of Blida, Purple: adjacent municipalities)

The results of the SCORECARD approach are illustrated respectively in Figures 6 and 7 for the entire province of Blida and down to the geographical setting assumed in Figure 5.

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q1.1 Awareness & Advocacy Critical Service & Infrastructure Resilience q6.4 q1.2 q6.3 5 q1.3 q6.2 q1.4 q6.1 q2.1 4 q5.11 q2.2 q5.10 3 q2.3 q5.9 q2.4 2 q5.8 q2.5 Social Capacity 1 Emergency Preparedness, q5.7 q2.6 Response & Recovery q5.6 0 q2.7

q5.5 q2.8

q5.4 q2.9

q5.3 q2.10 Province of Blida q5.2 q3.1 q5.1 q3.2 q4.4 q3.3 q4.3 q3.4 Planning, Regulation & Mainstreaming q4.2 q3.5 Risk Mitigation q4.1 q3.6 Legal & Institutional Arrangements q3.7

Figure 6: SCORECARD Approach results for the Blida Province

q1.1 Awareness & Advocacy Critical Service & Infrastructure Resilience q6.4 q1.2 q6.3 5 q1.3 q6.2 q1.4 q6.1 q2.1 4 q5.11 q2.2

q5.10 3 q2.3 q5.9 q2.4 2 q5.8 q2.5 Social Capacity 1 Emergency Preparedness, q5.7 q2.6 Response & Recovery q5.6 0 q2.7

q5.5 q2.8

q5.4 q2.9

q5.3 q2.10 Commune of Blida q5.2 q3.1 Surroundings of Blida q5.1 q3.2 q4.4 q3.3 Planning, Regulation & Mainstreaming q4.3 q3.4 q4.2 q3.5 Risk Mitigation q4.1 q3.6 Legal & Institutional Arrangements q3.7

Figure 7: SCORECARD Approach results for both geographic levels (municipality of Blida and adjacent municipalities)

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5 5

4 4

3 3

2 2 Vulnerability Score Vulnerability Social Vulnerability Score Vulnerability Social

1 1

0 0 AA SC LIA PRMRM EPRR CSIR AA SC LIA PRMRM EPRR CSIR Awareness Indicator Awareness Indicators Commune of Blida Surroundings of Blida Figure 8: Histograms of Mean Vulnerability Score per Awareness Indicator for (left) Blida Province and (right) Two geographic divisions.

What is clearly observed in both Figures 6 and 7 is the participants’ unawareness to social capacity themes reflected in questions 2.7, 2.8 and 2.9. The population showed low awareness to matters of access to gas and electricity, level of primary education and people who are in possession of at least that aforementioned level (both males and females) in their own neighbourhoods. Regarding Legal & Institutional arrangements, the sample population showed low levels of confidence towards their local administrative unit (i.e. civil protection agency) in the event of an earthquake for example. The feedback of the questionnaire data is to be displayed to the local administration to produce plans and strategies in subjects where the population shows low levels of awareness.

4. AVERAGE VULNERABILITY SCORE

Following the processing of both the classical social vulnerability index and the RPS response, the total score has been calculated from 0 to 5, assuming, different weights to each indicator, considering reasons, such as completeness of data, significance of indicator or relevance. Lower weight was given to those indicators for which data was not sufficiently representative of the real context, specifically Infrastructure and Institutional capacity (OECD 2008). Lower weight was also assigned to Awareness, given that it is not originally foreseen by the SoVI approach and considering the sample size (71 participants). Weights were then equally distributed amongst other indicators. The results of the hybrid approach and the final vulnerability scores are summarized in Table 9.

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Table 9: Vulnerability Score per Indicator and Average Vulnerability Score Vulnerability Scores (0-5) (not Indicator Weights weighted) Population 2.56 17 Education 3.57 17 Economy 1.83 17 Vulnerability Score (0- Health 2.26 17 5) (weighted) Habitat 1.63 17 Infrastructure 1.19 5 Governance and 2.29 5 Institutional Capacity Awareness 3.14 5 Average Vulnerability 2.31 2.35 (Blida)

The obtained vulnerability score, calculated for the entire province of Blida, was then divided into the 25 identified municipalities, in order to keep the consistency with the assessment carried out with the SCORECARD approach. Although the general vulnerability score can be applied to the entire urban area, the specific characteristics of the municipalities, mainly associated to the expansion patterns of the city in time, render them different from each other. Standardisation has been applied to the average score (Z-score), which will help understanding how each neighbourhood’s score differs from the average of the entire municipality. To achieve this goal, a proxy variable, for which different values could be associated to each area, should be considered (i.e. Z-score), according to Equation (1).

� − � � = (1) �

The urban density, collected from census data, has been used to evaluate the final results on the municipality level by also referring to the exposure dataset. Using the responses to the questionnaires, a vulnerability score has been attributed to the income and density variable for the main commune of Blida and for the other surrounding communes. The deviation (Z) of each neighbourhood’s score (X) from the global average (µ) and corresponding standard deviation (σ) has been calculated for both cases.

The final social vulnerability, Z-scored on density for all the municipalities of Blida, is illustrated in Figure 9, while the final average vulnerability Z-scored on income and density for the two-area geographically split province is displayed in Figure 10. From Figure 9, given the large vulnerability index for all the municipalities, it is observed that Ouled Yaich demonstrates the highest vulnerability level given the population density while the municipality of Blida showed the second highest level. On the other hand, Figure 10 shows high vulnerability for the Blida municipality and a lower score for the surrounding areas.

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5

4

3

2

1

0 Blida Chrea Chebli Larbaa Tamou Souhan - Affroun Soumaa O. Yaich - Mouzaia O. Slama Bouaarfa Benkhelil El Gherouaou O. El Alleug Beni Merad Ain Romana Beni H. Melouane Figure 9: (top) Final Social Vulnerability Map of Blida given (bottom) the Vulnerability Z-scored on Density for all municipalities

5 0.00 – 2.00

2.00 – 5.00 4

3

2

1

0 Municipality of Blida Adjacent Municipalities

Density Income

Figure 10: (Left) Final Social Vulnerability Map of Blida for the assigned geographic division given (Right) the Vulnerability Z-scored on Density and Income for the two regions.

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5. CONCLUSION

This study presented a detailed assessment of the seismic social vulnerability level of a case study province (Blida) in Algeria. The research was based and promoted on the European Civil Protection project ITERATE, which enhanced the local capacities with training for fostering good practice in risk prevention and preparation. Two distinct methodologies, usually applied separately and independently, have been employed and merged to characterize the social vulnerability of the Blida Province. The National Office of Statistics (ONS) literature and publications of census data for the Algerian provinces provided aid for the first methodology based on social vulnerability indicators (SoVI). The local population’s contribution to the delivery and filling on the ad-hoc questionnaires (SCORECARDS) constituting the basis of the second approach. The SoVI approach was subdivided in eight indicators, which were in turn characterized on the basis of different variables, whereas the SCORECARD approach was established over six dimensions of awareness. Quantitatively, the main outcome of this research is an overall score for social vulnerability, along with a model and mapping of the observed concentration of vulnerability obtained from the integration of the SCORECARD results into the SoVI results, put together with the hazard and physical vulnerability models for integrated seismic risk calculations for Algeria.

6. BIBLIOGRAPHY

Aitsi-Selmi, A., Egawa, S., Sasaki, H., Wannous, C., & Murray, V. (2015). The Sendai Framework for Disaster Risk Reduction: Renewing the Global Commitment to People's Resilience, Health, and Well-being. International Journal of Disaster Risk Science, 6(2), 164-176. Retrieved 4 17, 2018, from https://link.springer.com/article/10.1007/s13753-015-0050-9 Armaș, I., & Gavriș, A. (2013). Social vulnerability assessment using spatial multi-criteria analysis (SEVI model) and the Social Vulnerability Index (SoVI model) – a case study for Bucharest, Romania. Natural Hazards and Earth System Sciences, 13(6), 1481-1499. Retrieved 1 16, 2018, from http://nat-hazards-earth-syst-sci.net/13/1481/2013/nhess-13- 1481-2013.pdf Burton, C. G., & Silva, V. (2016). Assessing Integrated Earthquake Risk in OpenQuake with an Application to Mainland Portugal. Earthquake Spectra, 32(3), 1383-1403. Retrieved 1 16, 2018, from http://earthquakespectra.org/doi/abs/10.1193/120814eqs209m Burton, C., Khazai, B., Anhorn, J., & Contreras, D. M. (2017). Resilience Performance Scorecard (RPS) Methodology. GEM Foundation. Cerchiello V., Ceresa P., Monteiro R. (2017). Using the Scorecard Approach to Measure Seismic Social Resilience in Nablus, Palestine. In: Murayama Y., Velev D., Zlateva P., Gonzalez J. (eds) Information Technology in Disaster Risk Reduction. ITDRR 2016. IFIP Advances in Information and Communication Technology, vol 501. Springer, Cham Cutter, S. L., Boruff, B., & Shirley, W. L. (2003). Social Vulnerability to Environmental Hazards. Social Science Quarterly, 84(2), 242-261. Retrieved 1 8, 2018, from http://onlinelibrary.wiley.com/doi/10.1111/1540-6237.8402002/abstract Cutter, S. L., Mitchell, J. T., & Scott, M. S. (2000). Revealing the Vulnerability of People and Places: A Case Study of Georgetown County, South Carolina. Annals of The Association of American Geographers, 90(4), 713-737. Retrieved 1 16, 2018, from http://webra.cas.sc.edu/hvri/docs/gtown.pdf

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Khazai, B., Anhorn, J., & Burton, C. (2014). Participatory evaluation of earthquake risk and resilience in Lalitpur Sub-Metropolitan City. Lalitpur Sub-Metropolitan City: GEM Foundation.

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