Analysis of the Interaction of Resilience Variables of New Urban Habitations against Earthquake Risk: A Case Study of Isfahan Metropolitan)

IntroductionAmong natural disasters, earthquakes are one of the most important natural disasters that pose a threat to the development of the society. Each year, it causes various physical, social, and economic damage around the world. The consequences of an earthquake, both in terms of recurrence and in terms of the damage it causes, affect society. Because, on the one hand, earthquakes contribute to the lack of security for residents at risk, and on the other hand, they reduce the risk of achieving sustainable development. Therefore, earthquakes, both psychologically and financially, due to the speed of occurrence and the volume of destruction, have devastating effects and are at the forefront of natural disasters. Until the 1980s, the dominant approach to crisis management worldwide was based on reducing vulnerability, but since the 1980s, efforts have been made to change the prevailing crisis management paradigm. Thus, the prevailing view has shifted from focusing solely on reducing vulnerability to increasing disability resilience. In this new paradigm, the shift from reactivity to deterrence and participation has changed. Meanwhile, analyzing the interaction of earthquake resilience variables to identify key factors is one of the issues that should be considered in any society. It is noteworthy that the type of attitude towards the issue of resilience and how it is analyzed, on the one hand, plays a key role in how resilience recognizes the current situation and its causes, and on the other hand, affects policies and measures to reduce risk and how to deal with it. Therefore, analyzing the interaction of resilience variables against earthquakes and reducing their effects according to the results will be of great importance.Keywords: Resilience, Interaction, New Urban Habitations, Earthquake Risk, Isfahan Urban Area.MethodologyDue to the studied components and the nature of the subject, the approach of this research is descriptive-analytical. The interaction analysis method was used to analyze the data. Interaction/structural interaction analysis is a method for analyzing the possible occurrence of an issue in a predicted set. The probabilities of this can be adjusted by judgments about the potential for interaction between the predicted subjects. In this study, using 86 variables in the form of 6 indicators, the interaction of the studied resilience variables in the new urban Habitations of the Isfahan metropolitan was analyzed using MIC Mac software. DiscussionPreliminary analysis of the matrix data indicates that there are a total of 3496 relationships for the matrix. Also, the degree of saturation of the matrix is %63.29, which indicates that the selected factors have a relatively large and scattered effect on each other, and in fact, the system has been in an unstable state. Out of 4791 evaluable relations in this matrix, 2778 relations have zero numbers, which means that the factors have not affected or have not affected each other. Also, the studied matrix was %100 desirable and optimized based on statistical indices with two data rotations. The results of direct matrix data analysis have shown that the variables of geographical confinement diversity, level of awareness about seismicity of the habitation, and population density with scores of 159, 158, and 146, respectively. As the most important influential variables of the severity of the damage, compensation capacity proximity to hazardous areas with 191, 162, and 157 points, respectively, have been identified as the most important variables. In a cross-matrix, the sum of the row numbers of each factor indicates the degree of influence, and the sum of its columns indicates the degree of influence of that factor on other factors. Also, the results of indirect matrix data analysis have shown that the variables of geographical environment diversity, level of knowledge about seismicity in the region, and population density with scores of 1312373, 1272025, and 1200271, respectively, were the most important indirect variables. Severity, damage capacity and compensation capacity, and community-based risk management with scores of 15372702, 1298828, and 1298341, respectively, have been identified as the most important indirectly affected variables. What can be understood from the scattering plane of the variables affecting the resilience of new urban Habitations in the Isfahan metropolitan, the concentration of most variables around the diagonal axis, which indicates the instability of the system under study? ConclusionIn this study, using Mick Mac software, the effective variables on resilience forecasting of new urban Habitations in the Isfahan urban area have been investigated. The results of direct matrix data analysis have shown that the variables of geographical confinement diversity, level of awareness about seismicity of habitat, and population density were the most important influential variables. Severity, compensation capacity, and proximity were the most important risk areas. The analysis of indirect matrix data has also shown that the variables of geographical environment diversity, level of knowledge about seismicity of the region, and population density were the most important indirect variables. The variables of the severity of the damage, and compensation capacity were the most important indirectly affected risk-based risk management variables. EReferences- Ahmad Pour, A., Abdali, Y., Sadeghi, A., & AllahGholi Pour, S. (2018). Analysis of resilience components in the central tissue of Hamedan using Moran spatial autocorrelation. Quarterly Journal of Physical Development Planning, 5(1), 93-106.- Aksha, S. K., & Emrich, C. T. (2020). Benchmarking community disaster resilience in Nepal. International Journal of Environmental Research and Public Health, 17(6), 1-22.- Delavar, M. R., Sadrykia, M., & Zare, M. (2017). A GIS-based fuzzy decision making model for seismic vulnerability assessment in areas with incomplete data. International Journal of Geo-Information, 6(4), 119.- Febriyanti, F., Martini, S., Hidajah, A. C., & Dwirahmadi, F. (2021). A study on community economic resilience in response to earthquakes in Jailolo sub–district, North Maluku. Journal Berkala Epidemiologi, 9(2), 105-114.- Forrester, I. T., Mayaka, P., Brown-Fraser, S., Dawkins, N., Rowel, R., & Sitther, V. (2017). Earthquake disaster resilience: A framework for sustainable gardening in haiti’s vulnerable population. Journal of Hunger & Environmental Nutrition, 12(1), 136-149.- Godet, M. (2008). Strategic foresight. France, Paris: Lipsor Working Paper.- Haidarifar, M. R., Sialigli, M., & Soleimanirad, E. (2019). The evaluation of urban resilience components (case study: Kermanshah metropolis). Journal of Geography and Environmental Studies, 7(28), 107-125.- Kawachi, I., Aida, J., Hikichi, H., & Kondo, K. (2020). Disaster resilience in aging populations: Lessons from the 2011 Great East Japan earthquake and tsunami. Journal of the Royal Society of New Zealand, 50(2), 263-278.- Khayambashi, E., Taghvaei, M., & Varesi, H. R. (2020). Isfahan metropolitan resilience predictive model in crisis and unexpected incidents. Journal of Geographical Researchers, 35(1), 19-30.- Liu, B., Chen, X., Zhou, Z., Tang, M., & Li, S. (2020). Research on disaster resilience of earthquake-stricken areas in Longmenshan fault zone based on GIS. Journal of Environmental Hazards, 19(1), 50-69.- Matsuura, H. (2021). Level of disaster resilience and migration patterns in Japanese and foreign residents. Springer International Publishing.- Mitchell, J. K. (2014). Crucibles of hazard: Mega–cities and disasters in transition. Tokyo: Tokyo University Press.- Naimi, K., & Pourmohammadi, M. R. (2016). Identifying key factors affecting the future of Sanandaj's lower urban settlements with emphasis on future research application. Quarterly Journal of Urban Studies, 5(20), 53-64.- O’Brien, K., Sygna, L., & Haugen, J. E. (2004). Vulnerable or resilience? A multi–scale assessment of climate impacts and vulnerability in Norway. Journal of Climate Change, 64(1-2), 193-225.- Panday, S., Rushton, S., Karki, J., Balen, J., & Barenes, A. (2021). The role of social capital in disaster resilience in remote communities after the 2015 Nepal earthquake. International Journal of Disaster Risk Reduction, 55, 1-11.- Parizadi, T., Shaikholeslami, A., & Karimi Razakani, A. (2019). Analysis of the state of urban resilience against natural hazards (case study: Baqer-shahr city). Journal of Research and Urban Planning, 37, 41-54.- Rabbani, T. (2012). Structural analysis method is a tool for identifying and analyzing variables affecting the outcome of urban issues. Proceedings of the First National Conference on Future Research. February 17, 2012. Tehran.- Rao, F., & Summers, R. J. (2016). Planning for retail resilience: Comparing Edmonton and Portland. Cities, 58, 97-106.- Salmani, M., Kazemi Sani Ataallah, N., Badri S. A., & Motavaf, Sh. (2016). Identifying and analyzing the impact resilience indicators in the rural areas of north and northeast Tehran. Journal of Spatial Analysis Environmental Hazards, 3(2), 1-25.- Song, J., Huang, B., Li, R., & Pandey, R. (2020). Construction of the scale-specific resilience index to facilitate multi-scale decision making in disaster management: A case study of the 2015 Nepal earthquake. Journal of Social Indicators Research, 148(1), 189-223.- Soofi, S. Y. (2016). Achieving urban resilience: Through urban design and planning principles. Master’s Thesis, Oxford Brookes University. Oxford. UK.- Spaans, M., & Waterhout, B. (2017). Building up resilience in cities worldwide Rotterdam as participant in the 100 resilient. Cities, 61, 109-116.- Statistics Center of Iran (2016). The results of the 2016 census of Isfahan province. (n.p).- Turner Ii, B. L. (2010). Vulnerability and resilience: Coalescing or paralleling for approaches sustainability science?. Journal of Global Environmental Change, 20(4)

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.