Application of Grey Clustering and Radial Basic Functions in economical zoning of Tehran Megalopolis concentrating on Credit and Financial Institutes installation

The importance of local decisions and their effects on different corporations and services is best known. Selection of suitable places makes it easier for the corporations to find and catch more customers، and hence، credit and financial institutes are always seeking the most proper locations for their activity centers. Achieving this goal depends mainly on identifying the effective criteria and effectiveness of each of the mentioned factors. This research is up to introduce a model in order to identify the optimum zones for installation of banks and credit and financial institutes'' branches using objective and subjective methods. In the first step، after identifying effective criteria and economical factors based on the pattern of customers and managers spatial behavior، spatial data were collected. In the subjective method، weight of each criterion was calculated using the Analytical Hierarchy Process (AHP) after fulfillment of a paired comparative questionnaire by the expert. For spatial modeling and zoning the Grey Clustering Analysis (GCA) was used. Also، in the objective method the RBFLN neural network was used for spatial modeling. The Boolean combination function «AND» was used to combine the results of the two former methods. The results of combining the optimum classes constitutes of primary proposed zones. This process was conducted on each of the 22 regions of Tehran. Finally the model was evaluated for both objective and subjective methods، which suggest the significant performance of the model.