To reach a more  accurate prediction of future of a city, modeling must be done for all land-uses of the town. Logistic regression only can model a bivariate urban growth, i.e., urban and non-urban. Also, this method cannot consider the neighborhood effects in the allocation process. Due to this issue, the aim of this paper is to provide a method for modeling multiple land-use changes and applying the neighborhood parameter in allocation process, and thereby increasing the accuracy of modeling. So, in this article, we predicted the land-use map of the year 2014, using the land-use maps of the years 2002 and 2008 by considering the effects of the neighborhood parameter and by comparing the predicted land-use map of the year 2014 with the reference map of 2014, the accuracy of  the model was obtained. Reference land-use maps were obtained using classification of Landsat images of 2002, 2008, and 2014 using the Support Vector Machine (SVM) method. In the proposed method, the first modeling was performed separately using the Logistic Regression method for each land-use. Then the results of the Logistic Regression as a Competency Map for allocation process were combined with the Markov Chain and a combined method of MOLA-Neighborhood to obtain the land-use map of 2014. The procedure was performed in 4 different scenarios. In three of them, the neighborhood effects was considered as 3×3, 5×5, and 7×7 kernel and in the last one, modeling was performed without considering neighborhood effects.  The accuracy of 4 scenarios was compered using the reference map of 2014. In the best scenario the accuracy of method was obtained using overall accuracy, kappa index and location about 84.26 % and 76.35 %, and 79.3 %. Finally, the accuracy of each land-use category was evaluated separately using the ROC, which indicates the capability of the proposed approach of this paper.  Finally, the land-use map of the year 2020 was predicted in two different scenarios.