Spatial Analysis of Deterioration in Qom's Neighborhoods Using Geographically Weighted Regression

Analysis of spatial patterns of deterioration and spatial relationships between deterioration and its influencing factors in order to better understand of effective factors and improve performance of urban renewal management is very impressive and significant. So this research uses spatial statistical methods to analyze the spatial pattern of deterioration and its influencing factors. The results of the application of Moran's index on the spatial distribution of deterioration, this coefficient is positive and equal to 0.314. Representing the spatial distribution of the deterioration is cluster. Since Moran index can not identify Spatial diverse patterns, General G statistic analysis was covered the defect. General G statistic showed that neighborhoods with high deterioration together and have a high concentration of the cluster. About 6.29 percent of the area of deteriorate devoted to hot high-cluster and consists of five neighborhoods. Since the deterioration dependent on the local and spatial variables, Geographically weighted regression (GWR) was used to investigate the influencing factors on the deterioration. The results showed that the model with R^2 = 0.92 and R^2 Adjusted equal to 0.84 has acceptable accuracy in modeling the spatial relationships of effective factors on urban deterioration as well as Moran’s I of residuals GWR refers to insignificant autocorrelation. The results shows that impermeability, microlithic, the quality of infrastructure, dependency ratio, leasehold property and land prices variables have increasingly effect on deterioration. So these findings can scientific basis for policy in order to reduce deterioration and its effective.