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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

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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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Notes

  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.

References

  • Adger, W. N. (1997). Sustainability and social resilience in coastal resource use. CSERGE GEC working paper.

  • Bell, B. A., Morgan, G. B., Kromrey, J. D., & Ferron, J. M. (2010). The impact of small cluster size on multilevel models: A Monte Carlo examination of two-level models with binary and continuous predictors. JSM Proceedings, Survey Research Methods Section,11, 4057–4067.

    Google Scholar 

  • Bennet, L., Dahal, D. R., & Govindasamy, P. (2008). Caste, ethnic and regional identity in Nepal: further analysis of the 2006 Demographic and Health Surveys. Calverton, Maryland, USA: Macro International Inc.

    Google Scholar 

  • Berke, P., Newman, G., Lee, J., Combs, T., Kolosna, C., & Salvesen, D. (2015). Evaluation of networks of plans and vulnerability to hazards and climate change: A resilience scorecard. Journal of the American Planning Association,814, 287–302.

    Google Scholar 

  • Berke, P. R., & Campanella, T. J. (2006). Planning for postdisaster resiliency. The Annals of the American Academy of Political and Social Science, 604(1), 192–207.

    Google Scholar 

  • Berkes, F., Colding, J., & Folke, C. (2003). Navigating Social-Ecological Systems: Building Resilience for Complexity and Change. Cambridge University Press, 416 p.

  • Berkes, F., & Folke, C. (1998). Linking social and ecological systems for resilience and sustainability. Linking social and ecological systems: Management practices and social mechanisms for building resilience (p. 14). Cambridge: Cambridge University Press.

    Google Scholar 

  • Berkes, F., & Ross, H. (2013). Community resilience: Toward an integrated approach. Society & Natural Resources,261, 5–20.

    Google Scholar 

  • Brown, D., & Kulig, J. C. (1996). The concepts of resiliency: Theoretical lessons from community research. Health and Canadian Society,4(1), 29–52.

    Google Scholar 

  • Bruneau, M., Chang, S. E., Eguchi, R. T., Lee, G. C., O’Rourke, T. D., Reinhorn, A. M., et al. (2003). A framework to quantitatively assess and enhance the seismic resilience of communities. Earthquake spectra, 19(4), 733–752.

    Google Scholar 

  • Bubeck, P., Botzen, W. J., & Aerts, J. C. (2012). A review of risk perceptions and other factors that influence flood mitigation behavior. Risk Analysis: An International Journal, 32(9), 1481–1495.

    Google Scholar 

  • Burton, C. G. (2015). A validation of metrics for community resilience to natural hazards and disasters using the recovery from hurricane katrina as a case study. Annals of the Association of American Geographers, 105(1), 67–86.

    Google Scholar 

  • Carpenter, S., Walker, B., Anderies, J. M., & Abel, N. (2001). From metaphor to measurement: Resilience of what to what? Ecosystems,48, 765–781.

    Google Scholar 

  • Cavallaro, M., Asprone, D., Latora, V., Manfredi, G., & Nicosia, V. (2014). Assessment of urban ecosystem resilience through hybrid social–physical complex networks. Computer-Aided Civil and Infrastructure Engineering,298, 608–625.

    Google Scholar 

  • CFP. (2015). Inter-agency common feedback report. The inter-agency common feedback project, UNOCHA, Nepal UNCT & Nepal UNRCHCO, July–December, 2015. http://www.cfp.org.np/reports. Accessed April 22, 2018.

  • Cimellaro, G. P., Solari, D., & Bruneau, M. (2014). Physical infrastructure interdependency and regional resilience index after the 2011 Tohoku earthquake in Japan. Earthquake Engineering and Structural Dynamics,4312, 1763–1784.

    Google Scholar 

  • Cohen, O., Bolotin, A., Lahad, M., Goldberg, A., & Aharonson-Daniel, L. (2016). Increasing sensitivity of results by using quantile regression analysis for exploring community resilience. Ecological Indicators,66, 497–502.

    Google Scholar 

  • Cutter, S. L. (2016). Resilience to what? Resilience for whom? The Geographical Journal,1822, 110–113.

    Google Scholar 

  • Cutter, S. L., Ash, K. D., & Emrich, C. T. (2014). The geographies of community disaster resilience. Global Environmental Change,29, 65–77.

    Google Scholar 

  • Cutter, S. L., Ash, K. D., & Emrich, C. T. (2016). Urban–rural differences in disaster resilience. Annals of the American Association of Geographers, 106(6), 1236–1252.

    Google Scholar 

  • Cutter, S. L., Boruff, B. J., & Shirley, W. L. (2003). Social vulnerability to environmental hazards. Social science quarterly, 84(2), 242–261.

    Google Scholar 

  • Cutter, S. L., Burton, C. G., & Emrich, C. T. (2010). Disaster resilience indicators for benchmarking baseline conditions. Journal of Homeland Security and Emergency Management, 7(1).

  • Dahal, D. R. (2003). Social composition of the population: Caste/ethnicity and religion in Nepal. Population Monograph of Nepal,1, 87–135.

    Google Scholar 

  • Dang, Y., Dong, G., Chen, Y., Jones, K., & Zhang, W. (2017). Residential environment and subjective well-being in Beijing: A fine-grained spatial scale analysis using a bivariate response binomial multilevel model. Environment and Planning B: Urban Analytics and City Science. https://doi.org/10.1177/2399808317723012.

    Article  Google Scholar 

  • Despotaki, V., Sousa, L., & Burton, C. G. (2018). Using resilience indicators in the prediction of earthquake recovery. Earthquake Spectra,34(1), 265–282.

    Google Scholar 

  • Dhar, T. K., & Khirfan, L. (2016). Community-based adaptation through ecological design: Lessons from Negril, Jamaica. Journal of Urban Design,212, 234–255.

    Google Scholar 

  • Diaz, E., Green, D., Goodman, M., Kirschbaum, D., Molthan, A., Stough, T., & Webb, F. (2015). NASA response to 2015 M7.8 Nepal earthquake. https://trs.jpl.nasa.gov/bitstream/handle/2014/45940/15-5729_A1b.pdf?sequence=1. Accessed April 22, 2018.

  • Fekete, A., Damm, M., & Birkmann, J. (2010). Scales as a challenge for vulnerability assessment. Natural Hazards,553, 729–747.

    Google Scholar 

  • Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically weighted regression: The analysis of spatially varying relationships. Hoboken, NJ: Wiley.

    Google Scholar 

  • Frazier, T. G., Thompson, C. M., Dezzani, R. J., & Butsick, D. (2013). Spatial and temporal quantification of resilience at the community scale. Applied Geography,42, 95–107.

    Google Scholar 

  • Gardner, J. S., & Dekens, J. (2007). Mountain hazards and the resilience of social–ecological systems: lessons learned in India and Canada. Natural Hazards, 41(2), 317–336.

    Google Scholar 

  • Gellner, D. N. (2007). Caste, ethnicity and inequality in Nepal. Economic and Political Weekly, 42(2), 1823–1828.

    Google Scholar 

  • Godschalk, D. R. (2003). Urban hazard mitigation: Creating resilient cities. Natural Hazards Review,43, 136–143.

    Google Scholar 

  • Goldstein, H., Browne, W., & Rasbash, J. (2002). Partitioning variation in multilevel models. Understanding Statistics: Statistical Issues in Psychology, Education, and the Social Sciences,14, 223–231.

    Google Scholar 

  • He, S. Y. (2017). A hierarchical estimation of school quality capitalisation in house prices in Orange County, California. Urban Studies,54(14), 3337–3359. https://doi.org/10.1177/0042098016669473.

    Article  Google Scholar 

  • Holling, C. S. (1973). Resilience and stability of ecological systems. Annual Review of Ecology and Systematics,41, 1–23.

    Google Scholar 

  • Howe, P. D., Mildenberger, M., Marlon, J. R., & Leiserowitz, A. (2015). Geographic variation in opinions on climate change at state and local scales in the USA. Nature Climate Change,56, 596–603.

    Google Scholar 

  • IPCC. (2014). Annex II: Glossary (Mach, K.J., S. Planton and C. von Stechow eds.). In: Climate change 2014: Synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change (Core Writing Team, R. K. Pachauri and L.A. Meyer eds.). IPCC, Geneva, Switzerland (pp. 117–130). https://www.ipcc.ch/pdf/assessment-report/ar5/syr/AR5_SYR_FINAL_Glossary.pdf. Accessed 22 April 22, 2018.

  • Joerin, J., & Shaw, R. (2011). Mapping climate and disaster resilience in cities, Chap 3. In: Climate and Disaster Resilience in Cities (pp. 47–61). Emerald Group Publishing Limited.

  • Jones, B., & Andrey, J. (2007). Vulnerability index construction: Methodological choices and their influence on identifying vulnerable neighbourhoods. International Journal of Emergency Management, 4(2), 269–295.

    Google Scholar 

  • Jones, K., Johnston, R., Manley, D., Owen, D., & Charlton, C. (2015). Ethnic residential segregation: A multilevel, multigroup, multiscale approach exemplified by London in 2011. Demography, 52(6), 1995–2019.

    Google Scholar 

  • Kontokosta, C. E., & Malik, A. (2018). The resilience to emergencies and disasters index: Applying big data to benchmark and validate neighborhood resilience capacity. Sustainable Cities and Society,36, 272–285.

    Google Scholar 

  • Lam, N. S., Qiang, Y., Arenas, H., Brito, P., & Liu, K. B. (2015a). Mapping and assessing coastal resilience in the Caribbean region. Cartography and Geographic Information Science,424, 315–322.

    Google Scholar 

  • Lam, N. S., Reams, M., Li, K., Li, C., & Mata, L. P. (2015b). Measuring community resilience to coastal hazards along the Northern Gulf of Mexico. Natural Hazards Review,171, 04015013.

    Google Scholar 

  • Laska, S., & Morrow, B. H. (2006). Social vulnerabilities and Hurricane Katrina: An unnatural disaster in New Orleans. Marine Technology Society Journal,404, 16–26.

    Google Scholar 

  • Lo, A. Y., & Cheung, L. T. (2015). Seismic risk perception in the aftermath of Wenchuan earthquakes in southwestern China. Natural Hazards,783, 1979–1996.

    Google Scholar 

  • Madsen, W., & O’Mullan, C. (2016). Perceptions of community resilience after natural disaster in a rural Australian town. Journal of Community Psychology,443, 277–292.

    Google Scholar 

  • Malizia, E. E., & Ke, S. (1993). The influence of economic diversity on unemployment and stability. Journal of Regional Science,332, 221–235.

    Google Scholar 

  • Manley, D., Johnston, R., Jones, K., & Owen, D. (2015). Macro-, meso-and microscale segregation: Modeling changing ethnic residential patterns in Auckland, New Zealand, 2001–2013. Annals of the Association of American Geographers,105(5), 951–967. https://doi.org/10.1080/00045608.2015.1066739.

    Article  Google Scholar 

  • Manyena, S. B. (2014). Disaster resilience: A question of ‘multiple faces’ and ‘multiple spaces’? International Journal of Disaster Risk Reduction,8, 1–9.

    Google Scholar 

  • McPhearson, T., Pickett, S. T., Grimm, N. B., Niemelä, J., Alberti, M., Elmqvist, T., et al. (2016). Advancing urban ecology toward a science of cities. BioScience,663, 198–212.

    Google Scholar 

  • Mihunov, V. V., Lam, N. S., Zou, L., Rohli, R. V., Bushra, N., Reams, M. A., et al. (2018). Community resilience to drought hazard in the south-central United States. Annals of the American Association of Geographers, 108(3), 739–755.

    Google Scholar 

  • Mishra, A., Ghate, R., Maharjan, A., Gurung, J., Pathak, G., & Upraity, A. N. (2017). Building ex ante resilience of disaster-exposed mountain communities: Drawing insights from the Nepal earthquake recovery. International Journal of Disaster Risk Reduction,22, 167–178.

    Google Scholar 

  • Mobley, L. R., Kuo, T. M., & Andrews, L. (2008). How sensitive are multilevel regression findings to defined area of context? A case study of mammography use in California. Medical Care Research and Review,653, 315–337.

    Google Scholar 

  • Morrow, B. H. (2008). Community Resilience: A Social Justice Perspective (Vol. 4). Oak Ridge, TN: CARRI Research Report.

  • Nelson, D. R., Lemos, M. C., Eakin, H., & Lo, Y. J. (2016). The limits of poverty reduction in support of climate change adaptation. Environmental Research Letters, 11(9), 094011.

    Google Scholar 

  • Norris, F. H., Stevens, S. P., Pfefferbaum, B., Wyche, K. F., & Pfefferbaum, R. L. (2008). Community resilience as a metaphor, theory, set of capacities, and strategy for disaster readiness. American Journal of Community Psychology,411–2, 127–150.

    Google Scholar 

  • Norton, W. (2013). Human geography (8th ed.). Don MIlls, Ontario: Oxford University Press.

    Google Scholar 

  • NPC. (2015). Nepal earthquake 2015: Post disaster needs assessment, executive summary. National Planning Commission, Government of Nepal, Kathmandu. http://www.worldbank.org/content/dam/Worldbank/document/SAR/nepal-pdna-executive-summary.pdf. Accessed 22 April 22, 2018.

  • Pandey, R., & Bardsley, D. K. (2015). Social-ecological vulnerability to climate change in the Nepali Himalaya. Applied Geography,64(1), 74–86.

    Google Scholar 

  • Pfefferbaum, B. J., Reissman, D. B., Pfefferbaum, R. L., Klomp, R. W., & Gurwitch, R. H. (2008). Building resilience to mass trauma events. Handbook of injury and violence prevention (pp. 347–358). Boston, MA: Springer.

    Google Scholar 

  • Pfefferbaum, R. L., Pfefferbaum, B., Van Horn, R. L., Klomp, R. W., Norris, F. H., & Reissman, D. B. (2013). The communities advancing resilience toolkit CART: An intervention to build community resilience to disasters. Journal of Public Health Management and Practice,193, 250–258.

    Google Scholar 

  • Piironen, J., & Vehtari, A. (2016). On the hyperprior choice for the global shrinkage parameter in the horseshoe prior. arXiv:1610.05559.

  • Rose, A. (2004). Defining and measuring economic resilience to disasters. Disaster Prevention and Management: An International Journal,134, 307–314.

    Google Scholar 

  • Rose, A., & Krausmann, E. (2013). An economic framework for the development of a resilience index for business recovery. International Journal of Disaster Risk Reduction,5, 73–83.

    Google Scholar 

  • Rufat, S. (2013). Spectroscopy of urban vulnerability. Annals of the Association of American Geographers, 103(3), 505–525.

    Google Scholar 

  • Sharifi, A. (2016). A critical review of selected tools for assessing community resilience. Ecological Indicators,69, 629–647.

    Google Scholar 

  • Sherrieb, K., Norris, F. H., & Galea, S. (2010). Measuring capacities for community resilience. Social Indicators Research,992, 227–247.

    Google Scholar 

  • Shieh, Y. Y., & Fouladi, R. T. (2003). The effect of multicollinearity on multilevel modeling parameter estimates and standard errors. Educational and Psychological Measurement,636, 951–985.

    Google Scholar 

  • Song, J., Huang, B., & Li, R. (2017). Measuring recovery to build up metrics of flood resilience based on pollutant discharge data: A case study in East China. Water,98, 619.

    Google Scholar 

  • Song, J., Huang, B., & Li, R. (2018). Assessing local resilience to typhoon disasters: A case study in Nansha, Guangzhou. PLoS ONE,133, e0190701.

    Google Scholar 

  • Sousa, L., Silva, V., Marques, M., & Crowley, H. (2016). On the treatment of uncertainties in the development of fragility functions for earthquake loss estimation of building portfolios. Earthquake Engineering and Structural Dynamics,4512, 1955–1976.

    Google Scholar 

  • Spialek, M. L., Czlapinski, H. M., & Houston, J. B. (2016). Disaster communication ecology and community resilience perceptions following the 2013 central Illinois tornadoes. International Journal of Disaster Risk Reduction,17, 154–160.

    Google Scholar 

  • Stewart, E. A. (2003). School social bonds, school climate, and school misbehavior: A multilevel analysis. Justice Quarterly,203, 575–604.

    Google Scholar 

  • Tarkiainen, L., Martikainen, P., Laaksonen, M., & Leyland, A. H. (2010). Comparing the effects of neighbourhood characteristics on all-cause mortality using two hierarchical areal units in the capital region of Helsinki. Health & Place,162, 409–412.

    Google Scholar 

  • Tierney, K. (1997). Impacts of recent disasters on businesses: The 1993 Midwest floods and the 1994 Northridge Earthquake. In B. Jones (Ed.), Economic consequences of earthquakes: Preparing for the unexpected (pp. 189–222). Buffalo, NY: National Center for Earthquake Engineering Research.

    Google Scholar 

  • Timmerman, P. (1981). Vulnerability, resilience and the collapse of society: a review of models and possible climatic applications (No. 1). Institute for Environmental Studies, University of Toronto.

  • Torry, W. I. (1979). Intelligence, resilience and change in complex social-systems: Famine administration in India. Mass Emergencies,2, 71–85.

    Google Scholar 

  • UNISDR. (2009). Terminology on disaster risk reduction. Geneva: United Nations International Strategy for Disaster Risk Reduction.

    Google Scholar 

  • Vehtari, A., Gelman, A., & Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing,27(5), 1413–1432.

    Google Scholar 

  • Villagra, P., Rojas, C., Ohno, R., Xue, M., & Gómez, K. (2014). A GIS-base exploration of the relationships between open space systems and urban form for the adaptive capacity of cities after an earthquake: The cases of two Chilean cities. Applied Geography,48, 64–78.

    Google Scholar 

  • Watson, I. (2017). Resilience and disaster risk reduction: reclassifying diversity and national identity in post-earthquake Nepal. Third World Quarterly, 38(2), 483–504.

    Google Scholar 

  • WHO. (2016). Nepal earthquake 2015: An insight into risks, a vision for resilience. Geneva: World Health Organization, Regional Office for South-East Asia.

    Google Scholar 

  • Wood, N. J., Burton, C. G., & Cutter, S. L. (2010). Community variations in social vulnerability to Cascadia-related tsunamis in the U.S. Pacific Northwest. Natural Hazards,522, 369–389.

    Google Scholar 

  • Xiao, Y., & Drucker, J. (2013). Does economic diversity enhance regional disaster resilience? Journal of the American Planning Association,792, 148–160.

    Google Scholar 

  • Xiao, Y., & Feser, E. (2014). The unemployment impact of the 1993 US midwest flood: A quasi-experimental structural break point analysis. Environmental Hazards,132, 93–113.

    Google Scholar 

  • Xiao, Y., & Nilawar, U. (2013). Winners and losers: Analysing post-disaster spatial economic demand shift. Disasters,374, 646–668.

    Google Scholar 

  • Xin, H., Aronson, R. E., Lovelace, K. A., Strack, R. W., & Villalba, J. A. (2013). Resilience of Vietnamese refugees: Resources to cope with natural disasters in their resettled country. Disaster Medicine and Public Health Preparedness,74, 387–394.

    Google Scholar 

  • Zhou, H., Wan, J., & Jia, H. (2010). Resilience to natural hazards: A geographic perspective. Natural Hazards,531, 21–41.

    Google Scholar 

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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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  1. Department of Urban Planning and Design, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu Province, China

    Jinglu Song

  2. Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Jinglu Song & Bo Huang

  3. Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Bo Huang & Rongrong Li

  4. Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China

    Bo Huang

  5. School of Development and Social Engineering, Pokhara University, Pokhara 30, Kaski, Gandaki Province, Nepal

    Rishikesh Pandey

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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
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1.1 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
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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
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1.2 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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