Bandwidth plays a crucial role in the Geographically Weighted Regression modelas it affects the model’s ability to describe spatial dependencies. If the bandwidth is too large, the model will be similar to a normal regression model. Conversely, if it is too small, the model will be too rough. Bandwidth can be selected in several ways, e.g. manually determined by experts or using Akaike Information Criteria, Cross-Validation, and Lagrange Multiplier methods. This study offers an alternative approach to choosing bandwidth based on the covariance function representing a linear combination between the Bessel and Gaussian-Type functions. We applied this function to analyze the land price in Manado with four infrastructure accessibility variables, such as accessibility to government offices, education facilities, shopping centers, and healthcare facilities. Therefore, the proposed method is different from the index methods (AIC and CV) which have been used by other researchers. The results showed that the non-parametric covariance function provides a smaller bandwidth than conventional methods, specifically Akaike Information Criteria and Cross-Validation. In addition, the value of R2(adjusted) given by the covariance function is greater than the one given by the proportional method. This means that the optimal bandwidth obtained using the covariance function is more suitable to explain the land price in the city of Manado.