Application of Spatial Error Model For Ordinary And Fuzzy Data With Comparison

In this paper, an application of spatial data (ordinary & fuzzy)\r\nwas studied by using a spatial regression model, which is the\r\nspatial error model (SEM), data used are 38 location for\r\nseveral Iraqi cities. In this study estimated parameter’s model\r\nby ordinary least squares OLS and maximum likelihood\r\nMLE method for (ordinary and fuzzy data) then applied\r\nSimulation method to ensure the performance of the methods\r\nfor estimating the model that used in our study, accordingly a\r\nprogram was designed in Matlab language , included the\r\ngeneration of data with different sample sizes (n =50, 100)\r\ndefault values for the parameters model, the spatial \r\ndependence parameter and a spatial weights matrix was used\r\naccording the Rook Contiguity. After repeating the\r\nexperiment 1000 times and comparison through statistical\r\ncriteria (RMSE) the simulation results showed that the spatial\r\nerror model for fuzzy data is better than the spatial error\r\nmodel for ordinary data.