Modeling the spatial relations of factors effective in installation of current financial and credit institutes of Tehran using geographically weighted regression

Financial and credit centers, especially banks, are assumed as the most important economic activity in the urban space. The significant role of these centers in offering services to the citizens on one hand, and the competition between these centers on the other hand confirms the necessity of investigations about the optimum place for their site selection (Rahnamaee et al., 1391: 48). In such studies, the amount and the way that the space would effect is of importance. And, neglecting the effect of the space would lead to consequent errors in estimating, forecasting, and projecting (Soltani et al., 1389: 100). Multiplicity of effective variables in site selection in urban spaces has made the spatial decision makings complex, and modeling spatial relations necessary. Modeling these relations requires spatial methods due to the spatial nature of these factors.
In most studies about identifying the optimum place for installation sites of various activities and services, methods are used that are based on experts’ ideas. Also, multi-variable decision making techniques in geographical information system are widely used. While in these researches the spatial relations between the variables are not identified and modeled with respect to the current distribution pattern and the current situation are not used for further prediction.