عنوان مقاله [English]
نویسنده [English]چکیده [English]
Iran has gained considerable importance in recent decades In relation to spatial development due to the increase in population, especially the urban population. Urban population growth in the country has been inharmonious development and the elimination of agricultural resources and the natural landscape of the surrounding towns. However, it is a source of valuable space, but eroded in many cities That would solve a lot of problems in cities now. So it is always a problem in modern cities, the renovation and improvement of the eroded city. which has led to efforts to modernize and renovate it Various scholars and urban managers. Accordingly, Hence is used to achieve the aim of the study (improvement and modernization for proposed priority areas) Therefore, it has been used to achieve this goal of 7 main variable decay include dating, price, type of building, area, land slope, and access status number of residential units on floors worn texture in the present study. Is used to analyze research data from functions (Sum fuzzy, And fuzzy, product fuzzy) in the GIS software and has also been usedThe Fuzzy Analytic Hierarchy Process (FAHP), through weighting criteria In order to compare the results. Finally, were compared with the results obtained from fuzzy functions and fuzzy AHP to identify the best locations for the development and modernization of distressed area. The result showed According to the model used functions in this study, The total share of urban distressed area (An area of over 244023 m i.e. %721 of the total tissue) Been a strong priority for modernization. While the share of distressed area (An area of over 41502/5 m i.e. %11 of the total tissue) Poor conditions for modernization. As well as more mature on the FAHP model, i.e. an area of approximately 10/0 square meters of 32085% share of total benefits for Improvement the tissue would suggest. Of course, the proposed operation details are displayed in other than the Sum operator on functions and fuzzy AHP model more accurately.