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Volume 37, Issue 3 (2022)
GeoRes 2022, 37(3): 391-398 |
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Received: 2022/02/14 | Accepted: 2022/05/12 | Published: 2022/07/1
Received: 2022/02/14 | Accepted: 2022/05/12 | Published: 2022/07/1
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Mohagheghi P, Ghadami M, Azimi Amoli J, Janbaz Ghobadi G. Physical-Economic Resilience Assessment of District 12 of Tehran City Confronting Earthquake. GeoRes 2022; 37 (3) : 9
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1- Noor Branch, Islamic Azad University, Noor, Iran
2- Mazandaran University, Babolsar, Iran
2- Mazandaran University, Babolsar, Iran
Abstract (895 Views)
Aims: The best way to deal with natural hazards, especially earthquakes, is to improve residential resilience in different dimensions. Therefore, understanding the resilience of the current situation has a vital role in reducing the vulnerability of cities. The purpose of this research was to evaluate the physical and economic resilience of the 12th district of Tehran in the face of an earthquake.
Methodology: This descriptive-analytical study was conducted. The attribute library method was used for data collection and ArcGIS and Super Decision software and FANP and Anselin Local Morans models were used for data analysis.
Findings: The highest coefficient among the indicators affecting the resilience of the region was related to the movement and access network (4.261), building condition (3.925), uses (3.218), economic stability (3.211), density ( 3.002), and properties of the substrate were (1.544). The state of very low resilience covered 8,895,457m2 of the area and 522,081m2 (19.7%) were in the state of very high resilience. There was a significant difference in terms of resilience between the six regions of district 12. In terms of physical resilience, areas six and one had the highest level of resilience, and area three had the lowest level of resilience. In terms of economic resilience, Bazar, Baharestan, and Mokhtari neighborhoods had the best situation and Pamnar and Harandi neighborhoods had the worst situation.
Conclusion: The southern regions of the district are in a very low resilience situation compared to the northern regions, which requires quick planning in order to increase resilience.
Methodology: This descriptive-analytical study was conducted. The attribute library method was used for data collection and ArcGIS and Super Decision software and FANP and Anselin Local Morans models were used for data analysis.
Findings: The highest coefficient among the indicators affecting the resilience of the region was related to the movement and access network (4.261), building condition (3.925), uses (3.218), economic stability (3.211), density ( 3.002), and properties of the substrate were (1.544). The state of very low resilience covered 8,895,457m2 of the area and 522,081m2 (19.7%) were in the state of very high resilience. There was a significant difference in terms of resilience between the six regions of district 12. In terms of physical resilience, areas six and one had the highest level of resilience, and area three had the lowest level of resilience. In terms of economic resilience, Bazar, Baharestan, and Mokhtari neighborhoods had the best situation and Pamnar and Harandi neighborhoods had the worst situation.
Conclusion: The southern regions of the district are in a very low resilience situation compared to the northern regions, which requires quick planning in order to increase resilience.
Article number: 9
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