Abstract
Land, as a commodity, has high economic value that was rapidly changing. It requires a model of land price estimation that can counterbalance the demand. The model of land prices can be formed by calculating the proximity distance between land parcels to parameters that gave an effect on surrounding price changes, such as infrastructure and other public facilities. GWR (Geographically Weighted Regression) is a spatial analysis technique based on a regression method that used in this study. Using GWR, the boundaries of the surrounded land area will determine in the form of bandwidth values. The bandwidth is analogous to the radius of a circle, with the point of the land that will be estimated as the center. This bandwidth plays an important role to determine how far the distance or how many points will influence the price of a land area. The estimation of land price is conducted by assign a weight to the price of other parcels which are within the bandwidth range following the Kernel weighting function, Bi square, and Gauss. The land price bandwidth obtained is then interpolated using the IDW (Inverse Distance Weighting) method to form the bandwidth model of the entire study area. Weighting with Adaptive kernel Bi-Square gives better results compared to Adaptive Gauss kernel.
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