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Land prices, especially in an urban area, are dynamically changing.  To be able to do an evaluation, the right models must have the ability to understand land price characteristics that also dynamically changing. Every land price must attach to a location (spatial based). One of the locations (spatial based) models is Geographically Weighted Regression (GWR). This model can provide a local model based on the concept of attachment between observation and regression points. The main component is the determination of Optimum Bandwidth, which will determine the accuracy of the final GWR model. In the bandwidth process, it is necessary to do trial and error to get the Optimum Bandwidth value. Cross-Validation method commonly used to determine optimum bandwidth on observation point, but this study aims to minimize the process of trial and error in determining optimal bandwidth outside the observation point by using kriging interpolation. The Kriging method can substantially provide better bandwidth usage without having to do a trial process with too many errors.

Bandwidth Modelling on Geographically Weighted Regression with Bisquare Adaptive Method using Kriging Interpolation for Land Price Estimation Model