Abstract
The development of using location analysis in all aspects of life encouraged us to use it in this paper for analyzing weather in some Kurdistan regions which are (Sulaimaniyah, Erbil and Duhok) based on the data recorded by the devices placed in 27 different areas which had been recording the climate for all months during 2015. Moran test is used to check spatial dependency in a data and Spatial autoregressive model (SAR), spatial error model (SEM) is created also Lagrange test used to select the significant and alternative spatial model between (SAR and SEM). Different criteria or measures like (R2adj, RMSE, MAPE, AICc) are used for finding the best fit model also for all of them used (rook, bishop and queen) matrixes to specify the spatial correlation. The important result in the practical part shows that SAR model for queen and rook matrixes is significant while SEM model is not significant for all three matrixes and SAR model for queen matrix is best appropriate model.
Highlights
Spatial data refers to all types of data objects or elements that are presented in a geographical space or horizon
This paper shows spatial regression model and model possessory error in an attempt to provide a general guide Shows the importance of spatial loading, with particular on the importance of using spatial regression models,which Each of which includes spatial reliability testing and that is whether or not find tests the Moran, and ignore this Reliability may lead to the loss of information important for empowerment reflected in end up on the strength of estimate Statistical index extracted, these models are the link between the usual regression models with change models
In table (14), the values of the two test Lagrange Multiplier (LM) θ and robust LM θ according to the weight matrix rook, bishop and queen are not significant because the values of the two tests are less than the value of Chi-Square with degree of freedom χ 2(1,0.05)=3.841 we can concluded that the Spatial autoregressive model (SAR) Model is better than the spatial error model (SEM) model so SAR is alternative model
Summary
This paper presents two candidate approximate-semi-sparse solutions of the SAR model based on the Taylor series expansion and Chebyshev polynomials When accuracy of these new approximation algorithms and an exact algorithm were compared, both provided accurate results. The log likelihood function for y of the spatial lag model is obtained by adding the term ln |I-λW| to the log likelihood function of the standard regression model On account of this correction the MLE estimates will differ from the OLS estimates. It is square matrix which is elements positive value and denoted by W not necessary to be symmetric while create this matrix based on neighboring, relation neighboring from location for another location in same row in the rows of matrix and value for the diagonal usually equal to zero and chose weight matrix is very important for determine the spatial effects so we must create a Appropriate weight matrix and there for some way to create this matrix
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