1. Spatial Regression Model For Build-Up Growth Geo-informatics, Mahasarakham University

4. 4 -It produces a separate set of regression parameters for every observation across the study area. It therefore relaxes the assumption in traditional OLS models that the relations hips (regression coefficients) between dependent and independent variables being model led is constant across a study area as seen in below equation : - where g represents the vector of coordinates of the location, which indicate that there is a separate set of parameters for each of the g observations. When using GWR the parameter s can be estimated by solving: OLS GWR - where W(g) is the weight matrix which denotes connectivity between observations. The weight can be determined by several methods. Two common methods are the bi-square function and the Gaussian function. In the instance of the Gaussian function the weight for the observation i is shown in equation 5 : 3.Geographic Weighted Regression