Evaluating the components of social and economic resilience: After two large earthquake disasters Rudbar 1990 and Bam 2003
| Autor: | Amir Bastaminia, Mohammad Reza Rezaei, M H Saraei |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Population
lcsh:Risk in industry. Risk management 0211 other engineering and technologies 0507 social and economic geography 02 engineering and technology Management Monitoring Policy and Law economic resilience Natural disaster Socioeconomics education Resilience (network) Neighbourhood (mathematics) resilience Original Research 021110 strategic defence & security studies education.field_of_study Bam Emergency management business.industry 05 social sciences lcsh:HD61 Geography social resilience Sample size determination earthquake Household income business 050703 geography Safety Research Social capital Rudbar |
| Zdroj: | Jàmbá: Journal of Disaster Risk Studies, Volume: 9, Issue: 1, Pages: 1-12, Published: 2017 Jàmbá : Journal of Disaster Risk Studies, Vol 9, Iss 1, Pp e1-e12 (2017) Jàmbá : Journal of Disaster Risk Studies |
| Popis: | Extensive damages of natural disasters have made resilience a focus of disaster management plans in order to limit damages. The aim of this study was a comparative evaluation of social and economic resilience in Bam and Rudbar. This applied research attempted to quantify and compare different dimensions of social and economic resilience in Bam and Rudbar with a descriptive-analytical method. Cochran’s formula determined the sample size as 330 households from both cities (a total of 660 households). The indicators of social and economic resilience were identified from the literature, and then data were collected through a field study using questionnaires. Data were analysed using multiple linear regression and feedforward multilayer perceptron artificial neural network. Results denoted that several resilientrelated socio-economic features were significantly different for Bam and Rudbar cities, such as the number of earthquakes experienced, length of stay in current neighbourhood and mean individual and household income. Mean social and economic resilience scores were significantly higher for Rudbar (216.3 ± 33.4 and 30.6 ± 7.3) compared to Bam (193 ± 26.5 and 29.4 ± 7.07) ( p < 0.05). In addition, linear regression indicated that an increase in education level of the household head, length of stay in current neighbourhood and household income could result in an increase in social and economic resilience of the households under study. Neural network analysis revealed that social capital and employment recovery are the most and least effective factors, respectively, in both cities. In the population under study, social component, namely, social capital, was the most important determinant of resilience. |
| Databáze: | OpenAIRE |
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