Investigating the Spatial Pattern of Vulnerability of Tehran Neighborhoods against Floods

Flood is one of these dangers that cause heavy damage to cities every year. When a flood occurs, it is obvious that people who are weaker from a socio-economic point of view are more affected by these risks and will be able to recover in a much longer time. Using 54 key indicators, this research has determined the degree of vulnerability in the regions of Tehran. Then the results are spatially analyzed and the degree of vulnerability in each of the dimensions as well as the final index has been determined. In this research, the local Moran model and the High-low clustering tool have been used. Based on the results obtained from the high low clustering tool and according to the standard Z score (-3.83) as well as the P value (0.0001) in the final index of vulnerability, it can be said with 99% confidence that the pattern of spatial clustering of the values of this index is not caused by chance, so the zero hypothesis is rejected. Also, considering that the standardized value is negative and the significant level is displayed in the blue area; it can be concluded that in the final index spatial clustering has been done in the case of low values. Also, by calculating the local Moran for the final index of vulnerability, it was determined that 30% of the neighborhoods were clustered and 4% were non-clustered. No specific clustering was observed for 66% of the neighborhoods.
Flood is one of these dangers that cause heavy damage to cities every year. When a flood occurs, it is obvious that people who are weaker from a socio-economic point of view are more affected by these risks and will be able to recover in a much longer time. Using 54 key indicators, this research has determined the degree of vulnerability in the regions of Tehran. Then the results are spatially analyzed and the degree of vulnerability in each of the dimensions as well as the final index has been determined. In this research, the local Moran model and the High-low clustering tool have been used. Based on the results obtained from the high low clustering tool and according to the standard Z score (-3.83) as well as the P value (0.0001) in the final index of vulnerability, it can be said with 99% confidence that the pattern of spatial clustering of the values of this index is not caused by chance, so the zero hypothesis is rejected. Also, considering that the standardized value is negative and the significant level is displayed in the blue area; it can be concluded that in the final index spatial clustering has been done in the case of low values. Also, by calculating the local Moran for the final index of vulnerability, it was determined that 30% of the neighborhoods were clustered and 4% were non-clustered. No specific clustering was observed for 66% of the neighborhoods.
Flood is one of these dangers that cause heavy damage to cities every year. When a flood occurs, it is obvious that people who are weaker from a socio-economic point of view are more affected by these risks and will be able to recover in a much longer time. Using 54 key indicators, this research has determined the degree of vulnerability in the regions of Tehran. Then the results are spatially analyzed and the degree of vulnerability in each of the dimensions as well as the final index has been determined. In this research, the local Moran model and the High-low clustering tool have been used. Based on the results obtained from the high low clustering tool and according to the standard Z score (-3.83) as well as the P value (0.0001) in the final index of vulnerability, it can be said with 99% confidence that the pattern of spatial clustering of the values of this index is not caused by chance, so the zero hypothesis is rejected. Also, considering that the standardized value is negative and the significant level is displayed in the blue area; it can be concluded that in the final index spatial clustering has been done in the case of low values. Also, by calculating the local Moran for the final index of vulnerability, it was determined that 30% of the neighborhoods were clustered and 4% were non-clustered. No specific clustering was observed for 66% of the neighborhoods.