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Construction of the Scale-Specific Resilience Index to Facilitate Multiscale Decision Making in Disaster Management: A Case Study of the 2015 Nepal Earthquake

Abstract

Many scholars have advocated for the use of empirical evidence to assess resilience across scales and over time. Accordingly, we conduct a case study using survey data on individual perceptions of disaster relief that were gathered each month from August to December 2015, shortly after the 2015 Nepal earthquake. We construct a scale-specific resilience index (SSRI) based on a set of variables that are validated separately at different spatial scales and over time against the survey data. The regression results show that the variables related to household structure, industrial diversity, community capital, and accessibility and emergency services are validated against the survey data at both the district and sub-district levels, the variables related to ethnic diversity and the capacity of emergency camps are validated only at the district level, and the earthquake experiences variable is validated only at the sub-district level. Consequently, to achieve optimal models, we use six validated variables to construct an SSRI at the district level and seven variables, including those related to the vulnerability of household property and the average elevation, to construct an SSRI at the sub-district level. The SSRI scores are validated via multilevel regression models against the surveyed relief scores after the 2015 Nepal earthquake. The results show that the SSRI scores based on the validated variables correlate favorably and as expected against the survey data at both district and sub-district levels, and outperform the composite resilience index, which considers all of the variables regardless of their individual validation results. The method used to construct the SSRI helps to identify the contributions of multidimensional resilience indicators across spatial scales and over time in real cases, and also provides index scores of scale-specific resilience that are easily understood and applicable to multi-scale decision-making processes.

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Fig. 1
Fig. 2

Notes

  1. 1.

    The between-group variation in a multilevel regression model in this paper was estimated by intra-class correlation coefficient (ICC), which is commonly used as a criterion to determine whether it is necessary to fit a multilevel regression.

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Acknowledgements

We acknowledge the community perception survey data from the Inter-Agency Common Feedback Project, developed by the UN Office for the Coordination of Humanitarian Affairs (UNOCHA), the UN Country Team in Nepal (Nepal UNCT), and the UN Resident Coordinator/Humanitarian Coordinator’s Office in Nepal (Nepal UNRCHCO).

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Appendix A

Appendix A

See Fig. 2 and Table 7.

Table 7 Sample statistics for variables at district and subdistrict scales

A1: Data Source and Survey Samples

Individual survey data are collected by community perception surveys supported by the Inter-Agency Common Feedback Project (CFP), developed by the UN Office for the Coordination of Humanitarian Affairs (UNOCHA), the UN Country Team in Nepal (Nepal UNCT), and the UN Resident Coordinator/Humanitarian Coordinator’s Office in Nepal (Nepal UNRCHCO). The purpose of this project is to obtain feedback from affected people to support informed decision making, strengthen response efforts, and ensure future recovery progress and resilience (CFP 2015). Six survey rounds are conducted from July to December 2015; each includes around 1400 respondents in the 14 most severely affected districts. Within each district, four or five sample subdistricts per district are selected considering initial reports of mortality rates and destruction and consultations with local government and different agencies. Below the subdistrict level, random sampling is used to select four or five wards per subdistrict. Within each selected ward, trained volunteers are randomly selected around five households with some flexibility to broadly capture a more diverse set of perceptions. In this way, each round of the survey gathers data from 1400 respondents, around 100 respondents per district (CFP 2015). All survey data are provided by Nepal UNRCHCO. Due to differences in the survey design in July, the analysis begins with data collected in August, and thus five rounds of survey data are used to monitor the relief process over 5 months. To reduce the influence of sample size, all samples in a subdistrict are deleted if the subdistrict is a singleton subdistrict (i.e., a subdistrict containing only one sample respondent) (Bell et al. 2010) or if the sample size of the subdistrict varies significantly over time. Consequently, around 1000 respondents remain in each round of the survey (Table 8).

Table 8 Number of respondents for each survey round by district

The relief score is based on a survey question “Is the post-earthquake relief effort making progress?” All samples with answers of either “Don’t know” or “Refused” are discarded. The remaining answers are given on a 5-point Likert scale (“1 = not at all”; “2 = not very much”; “3 = neutral”; “4 = mostly yes”; “5 = completely yes”). Without specific respect to any social, economic, or physical relief process, this measure can be used as a proxy for other, more “objective,” but often difficult to measure, relief progress. To empirically examine potential resilience indictors that are significantly associated with relief progress, we recode the relief score into binary variables—1 for mostly yes or completely yes and 0 for others—and apply the recoded relief score as the dependent variable in further regression models (Table 9).

Table 9 Sample statistics for individual variables: proportions expressed as percentages

A2: Results of Model Comparisons

For each survey round at different levels, a simple binary model with only individual variables (Model 1) serves as the baseline, whereas a random-intercept two-level model based on Model 1 (Model 2) and a model with additional contextual variables based on Model 2 (Model 3) serve as comparisons to acknowledge the two area-level effects and the explanatory power of variables as proxies for resilience. In comparisons of any pair of Model 1 and Model 2, Model 2 has significantly reduced values of WAIC and LOOIC, indicating greater predictive accuracy due to the strong areal unit effect on both district and subdistrict levels. As such, a hierarchical model proves a better choice in this case. When comparing each pair of Model 2 and Model 3, we observe that Model 3 had smaller WAIC and LOOIC, which indicates that the predictive accuracy is improved with the addition of contextual variables. Another finding is that Model 3 always has a comparatively smaller VPC, suggesting that the added contextual variables could account for part of the unobserved between-group variances on both the district and subdistrict levels. In addition, for both Model 2 and Model 3 within the same survey round, subdistricts play a larger role than districts in explaining variations. This is expected because on a lower spatial resolution, larger areal units are more internally heterogeneous, thus reducing the proportion of between-groups variances (Tarkiainen et al. 2010) (Table 10).

Table 10 Variance partition coefficients (VPC) and predictive accuracy of different predictive models

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Song, J., Huang, B., Li, R. et al. Construction of the Scale-Specific Resilience Index to Facilitate Multiscale Decision Making in Disaster Management: A Case Study of the 2015 Nepal Earthquake. Soc Indic Res 148, 189–223 (2020). https://doi.org/10.1007/s11205-019-02188-8

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Keywords

  • Resilience indicator
  • Individual resilience
  • Community resilience
  • Disaster management
  • Multilevel regression
  • Hierarchy theory
  • Spatial scales
  • Temporal variation
  • Earthquake
  • Nepal