Análise de índices físicos na resiliência em terremotos nos bairros de Babol, Irã
In recent years, one of the most important goals of urban managers has been the planning for resilience to natural disasters, especially earthquakes. Babol, as the second most populous city of Mazandaran province in northern Iran, is exposed to earthquakes due to its proximity to two active faults,...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2020 |
| País: | Brasil |
| Institución: | Universidade Federal do Ceará (UFC) |
| Repositorio: | Repositório Institucional da Universidade Federal do Ceará (UFC) |
| Idioma: | portugués |
| OAI Identifier: | oai:repositorio.ufc.br:riufc/53640 |
| Acceso en línea: | http://www.repositorio.ufc.br/handle/riufc/53640 |
| Access Level: | acceso abierto |
| Palabra clave: | Resiliência física Terremoto Análise fatorial. Análise de agrupamento Babol |
| Sumario: | In recent years, one of the most important goals of urban managers has been the planning for resilience to natural disasters, especially earthquakes. Babol, as the second most populous city of Mazandaran province in northern Iran, is exposed to earthquakes due to its proximity to two active faults, the Caspian Sea and northern Alborz. The purpose of this study was to evaluate the effective physical indicators in the resilience rate of 22 neighborhoods of Babol. The research method is descriptive-analytical and applied-developmental. Accordingly, 48 effective indicators in the physical dimension were extracted and evaluated for their suitability through KMO test in consultation with experts and professors in urban planning, crisis management and earthquake. Then, the selected indices were reduced to four factors by factor analysis, which accounted for 61.9% of the variance. Among the four factors, the first factor alone accounted for 24.8% of the variance, which is the most influential factor in the study, and the fourth factor with 10.3% of the variance has the least effect. Cluster analysis was used for homogeneous classification of the neighborhoods and Babol's 22 neighborhoods were classified into 8 homogeneous groups. The results of clustering method show that about 40.9% of neighborhoods are evaluated as very poor, poor and relatively poor and in undesirable conditions in general. Also, about 18.2% of the neighborhoods are in the medium level and about 40.2% of the neighborhoods are evaluated as good, very good, excellent and very excellent levels in terms of physical resilience. |
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